Welcome to Microsoft Research Asia Faculty Summit 2016. The summit is organized by Microsoft Research Asia in partnership with Yonsei University. We are delighted to provide a forum for academic leaders from the top research universities and institutes to share the latest progress of related research areas, discuss the future of artificial intelligence (AI) and how we foster next generation talent in these areas.
The rapid advances of artificial intelligence are leading a new industry revolution and changing every aspect of our lives. There are a few reinforcing trends and forces that are driving this disruption and exponential growth opportunities. The large push for digitalization and increased connectivity of devices are generating big data that fuel machine learning to build extensive models that are increasingly powerful enough to create semantic representations of the world we live in. The ever increasing power of computation, such as cloud computing and internet of things, are providing infrastructure support for building ever more intelligent software that is written and programmed by big data through machine learning. These reinforcing forces are contributing to the advancement of artificial intelligence, which is now playing the most central and critical role in the tech world. New innovations are redefining our digital life and digital work, reinventing productivity and business processes, and creating more personal computing with new forms of human computation interaction. It is vital that we understand these important trends and advancement of technologies, and how we can leverage this unprecedented opportunity to further advance the capacity of artificial intelligence to impact and improve everyday life.
Since its debut in 2000, more than 2,000 academics across the Asia-Pacific region have participated in Microsoft Research Asia Faculty Summit. This year’s program consists of plenary sessions, breakout sessions, and research showcases that will enable us to share the current research results and identify future challenges. We hope you will explore new ideas and collaborative opportunities during the Faculty Summit. Look forward to seeing you in Seoul!
President of Korea AI Society
Professor of Korea University
Fellow of IEEE
Assistant Managing Director
Microsoft Research Asia
Fellow of IEEE
Friday, November 4
08:20 – 08:30
Opening and Welcome
08:30 – 09:30
Future AI 2025
|Moderator: Tie-Yan Liu, Microsoft Research
09:30 – 09:50
09:50 – 11:10
MSR AI Engage – Learning and Intelligence
|Chair: Evelyne Viegas, Microsoft Research
How to Make Spoken Dialogue based Intelligent Agent Pervasive?
|Chair: Qiang Huo, Microsoft Research
Conversation As A Platform (CAAP)
Chair: Ming Zhou, Microsoft Research
|11:10 – 12:30||
Machine Learning: Theory Meets Application
|Chair: Tie-Yan Liu, Microsoft Research
Video Analysis and Understanding
|Chair: Wenjun Zeng, Microsoft Research
| Chair: Mu Li, Microsoft Research
12:30 – 14:00
Lunch + Research Showcase
14:00 – 15:20
Machine Learning System and Infrastructure
|Chair: Tao Qin, Microsoft Research
Social Multimedia and Visual Q&A
| Chair: Jingdong Wang, Microsoft Research
|Chair: Jun Yan, Microsoft Research
|15:20 – 16:40||
Deep Learning and Reinforcement Learning
|Chair: Taifeng Wang, Microsoft Research
Learning for Vision and Multimedia
|Chair: Jingdong Wang, Microsoft Research
Big Scholarly Data Research, Utilities, and Impact Assessments
|Chair: Kuansan Wang, Microsoft Research
16:40 – 17:00
|17:00 – 18:00||
Future Talent 2040
|Moderator: Tim Pan, Microsoft Research
|18:30 – 20:30||
Dinner Event at Four Seasons Hotel Seoul
Saturday, November 5
08:30 – 09:50
Machine Learning: Generative vs. Discriminative
|Chair: Tie-Yan Liu, Microsoft Research
3D Real World Capturing and Reconstruction
Chair: Richard Cai, Microsoft Research
Urban Big Data and Urban Computing
|Chair: Yu Zheng, Microsoft Research
09:50 – 11:10
Chair: Katsu Ikeuchi, Microsoft Research
Chair: Gang Hua, Microsoft Research
AI and Psychology
|Chair: Xing Xie, Microsoft Research
11:10 – 11:30
11:30 – 12:30
Artificial Intelligence Research at Microsoft Research Asia
|Wei-Ying Ma, Microsoft Research|
|12:30 – 14:00||Lunch & Networking|
This session examines the future direction of robotics research. As a background movement, AI is sparking great interest and exploration. In order to realize AI in human society, it is necessary to embody such AI in physical forms, namely to have physical forms. Under such circumstance, this session explores and clarifies the current direction of basic robotics research. Thorough examination of what types of research components are missing, and how does such capability development affect the directional paths of research will be highlighted.
Machine Learning System and Infrastructure
It is evident that when machine learning meets big data, it is vital to have a powerful system/infrastructure to support the distributed training task. In recent years, people have used different frameworks for this purpose, including iterative MapReduce, parameter server, and data flow. In this session, we are going to discuss how to enhance these frameworks from both system and algorithmic perspectives, and how to implement parallel machine learning algorithms under these frameworks. In addition, we will discuss the future trend of machine learning system and infrastructure and how to push its frontier through close collaboration between academia and industry.
Deep Learning and Reinforcement Learning
In recent years, the growing research on deep learning and reinforcement learning has led to many exciting breakthroughs. However, on the other hand, there are still many open problems regarding deep learning and reinforcement learning. In this session, we will reflect, problem-solve and discuss key missing elements, as well as synthesizing the opportunities for academia and industry to further advance this field.
Machine Learning: Generative vs. Discriminative
Generative learning and discriminative learning are two major approaches in machine learning. The fast development of deep learning demonstrates the power of discriminative learning. In recent years, some have started to integrate generative learning into the deep learning process in order to incorporate prior knowledge. In this session, we will discuss the pros and cons of each approach and how to seamlessly integrate them together.
Machine Learning: Theory Meets Application
In this session, we will discuss machine learning from two extremes, theory and application. The goal is to bring theory and application researchers together and inspire each other’s work, so that the theory research can become more targeted and the application research can have better theoretical guarantees.
How to Make Spoken Dialogue based Intelligent Agent Pervasive?
Since the launch of a spoken dialogue based intelligent assistant, dubbed Siri, on the iPhone 4S in October 2011, major tech giants such as Apple, Microsoft, Google, Amazon, and Facebook have all invested significantly to develop such type of virtual-assistant Apps and/or services over the past few years. Despite all the hype, none of them have become pervasive yet. In this session, several leading experts from academia in Asia are invited to discuss how to make spoken dialogue based intelligent agent pervasive by addressing the following technical challenges: (1) Better speech capturing and processing for robust distant speech recognition; (2) Robust and scalable spoken language understanding; (3) Robust and flexible dialogue management.
MSR AI Engage – Learning and Intelligence
Given the investment and evidence of progress in Artificial Intelligence some suggest that it is merely a matter of time until AI can match, complement or surpass human intelligence. This session looks at recent research advances in machine learning and cognitive science and discusses the needs and design principles to support fundamental research in AI. Together we will look at how to push AI technology towards more natural human-AI communication and interaction that will facilitate social learning and collaborations between humans and AI agents.
3D Real World Capturing and Reconstruction
Capturing and reconstruction 3D real world (scene, objects, and dynamic human characters) plays important role in VR/AR applications. However, real time high quality 3D capturing and reconstruction is still a very challenging task. In this session, we are going to do some reflection on this important research field, and discuss what’s missing and what are the opportunities for academia and industry to further advance this field.
Social Multimedia and Visual Q&A
In recent years, the growing research on social multimedia and visual Q&A has led to many exciting breakthroughs. However, on the other hand, there are still many open problems regarding social multimedia and visual Q&A. In this session, we are going to do some reflection on this important research field, and discuss what’s missing and what are the opportunities for academia and industry to further advance this field.
Learning for Vision and Multimedia
In recent years, the growing research on deep learning has led to many exciting breakthroughs in vision and multimedia communities. However, on the other hand, there are still many open problems regarding deep learning for vision and multimedia. In this session, we are going to do some reflection on this important research field, and discuss what’s missing and what are the opportunities for academia and industry to further advance this field.
Video Analysis and Understanding
Video is the biggest big data that contains an enormous amount of information. Recently computer vision and machine learning technologies have been significantly leveraged to turn raw video data into insights to facilitate various applications and services. In this session, we intend to do some reflection on this important research field, and discuss what’s missing and what are the opportunities for academia and industry to further advance this field.
Computer vision has continued to be of the hottest and most active research areas in artificial intelligence, both in academia and industry. Although we have made tremendous progress in the past ten years which has opened vast opportunities, we are still facing multitudes of challenges in building computer vision systems that are as robust as the human vision system. For example, it still remains to be an open challenge to build a computer vision system that can learn and evolve in a similar way as a human vision system. This session intends to gather thoughts on the future trends of computer vision, while savoring the success of this fast evolving field in the past several years.
AI and Psychology
Gaining an in-depth understanding of users is critical for building artificial intelligence systems. With the rapid development of positioning, sensing and social networking technologies, large quantities of human behavioral data are now readily available. They reflect various aspects of human mobility and activities in the physical world. The availability of this data presents an unprecedented opportunity to user understanding. In addition, recent studies in psychology suggest that numerous psychological features, such as personality traits, are highly correlated to user behaviors. It will be interesting to study how we can design computational frameworks for inferring psychological features of users, based on their data at different levels and across heterogeneous domains, and how these frameworks can benefit the development of artificial intelligence systems. In this session, we plan to invite researchers from computer science, psychology and cognitive science areas. We will brainstorm innovative ideas, technologies, systems and applications along this interdisciplinary research direction.
Urban Big Data and Urban Computing
Urban computing connects ubiquitous sensing technologies, advanced data management & machine learning models, and novel visualization methods to create win-win-win solutions that improve urban environment, human life quality, and city operation systems.
Machine computable knowledge is playing a key role and will play a more important role in the field of artificial intelligence. Only after the turn of this century, massive amounts of structured and semi-structured data that directly or indirectly encode human knowledge became widely available, turning the knowledge extraction, representation and computing problems into a computational grand challenge with feasible solutions in sight. Such world knowledge in turn enhances various applications such as semantic search, automatic question-answering, recommendation systems, chat engines in Web and enterprise scenarios, etc.
Conversation As A Platform (CAAP)
Conversation as a platform (CAAP), or chatbots, builds a seamless linked set of technologies ranging from chit-chat for social connection, to botification of search and question-answering for informational needs and up to dialogue systems for task completion. Chatbots will have profound impact as previous shifts we’ve had such as graphical user interface, the web browser and the touchscreen. Many companies have begun investing heavily in this area with the promise of booking a meeting room or buying a cup of coffee as easy as sending a short text message on a social network. It combines the human language (speech and natural language processing) with the power of cloud computing and big data and applies it pervasively to computing. Microsoft has built impactful Xiaoice, Rinna and Tay for markets in China, Japan and US. We would like to introduce our recent progress in this area and invite researchers and professors from universities to join us to present and discuss important topics in CAAP including but not limited to chit-chat, QnA and dialogue systems. We hope to find a few interesting topics for future collaborations.
In recent years, research in machine translation continues to make significant progress after statistical methods were widely adopted since 1990’s, especially in the area of large-scale learning from big data and employing deep learning methods to improve translation. In this session, we are going to go through recent breakthroughs in the machine translation area, and discuss directions, methods and challenges in the next step of machine translation research and real-world system construction.
Big Scholarly Data Research, Utilities, and Impact Assessments
Recent years have witnessed dramatic progress in machines exhibiting intelligent behaviors, ranging from chat bots performing complicated logistic scheduling or serving as TV news anchors, to machine beating humans in playing Go or recognizing objects in photo images. As the core algorithms behind these advancements were largely proposed and attempted since the last two decades, one plausible explanation to the sudden renaissance of machine intelligence points to the large amount of data available for training, and the cloud computing infrastructure being more mature and affordable for handling large scale data processing. In this panel discussion, we will be discussing whether and how these trends can be leveraged in the areas of scholarly communications and education. We will address the following: What the data available so far can tell us? Is it time for the community to jointly evolve beyond age-old impact metrics (e.g., JIF, h-index) that are known to have serious drawbacks? How can we improve research with big data? In education, how will classrooms over the next decades be delivered? All these are a slice of possible questions the panel will discuss and debate.
Artificial Intelligence Research at Microsoft Research Asia
AI research at Microsoft Research Asia will be introduced, including machine learning, computer vision, natural language processing, knowledge mining and urban computing based on big data. Specifically, I will share our research on developing new learning algorithms and distributed machine learning platform for training very big models on big data based on heterogeneous hardware (e.g. CPU, GPU and FPGA cluster). In addition to deep learning, we are also working on knowledge mining and symbolic learning that integrates facts, common sense, and logic rules in a unified knowledge representation for machine comprehension of text. I will introduce these research works and show how they have been used to build artificially intelligent and socially engaging conversational agents such as XiaoIce and Microsoft’s Cognitive Services.
Yong-Hak Kim, President, Yonsei University
Dr. Yong-Hak Kim is a leading expert in social network theories, he places emphasis on the importance of facilitating “extelligence,” the improvement of intelligence through the coupling of otherwise estranged bright ideas. After becoming the 18th President of Yonsei University in February 2016, one of his first initiatives was to establish the “Creative Playground” in the University Library, a habitat where students can share opinions for interdisciplinary research and cultivate experimental ideas to develop innovative startups. As we enter a generation with a 100-year life expectancy, the world must explore uncharted territory due to the revolutionary developments in science and technology and information communication. This demands a new university paradigm. Accordingly, Dr. Kim has commenced forward-thinking innovation of the university’s research, administration, and education system to become a pioneering leader of our rapidly changing society.
Dr. Kim has served on the editorial boards of the American Journal of Society, Rationality and Society, and Korean Journal of Sociology. He also has held positions in various government committees as a policy advisor, including the Consulting Committee of the president of Korea and the Neural Science Review Committee of the Ministry of Science and Technology.
After receiving his bachelor’s degree in sociology from Yonsei University, Professor Kim received his master’s and doctorate degrees from the University of Chicago. Since beginning his professorship at Yonsei University in 1987, Dr. Kim previously served in various senior administrative positions such as Vice President of the Admissions Office, Dean of the University College, Dean of the College of Social Sciences, and Dean of the Graduate School of Public Administration.
Peter Lee, Corporate Vice President, Microsoft Research
Dr. Peter Lee is a computer scientist, technology innovator, and Corporate Vice President at Microsoft Research. He leads Microsoft’s New Experiences and Technologies organization (NExT), with the mission to create research-powered technologies and products, and to advance human knowledge through fundamental scientific research. While NExT openly publishes its research work, its technology projects are often conducted more secretly. Still, recently publicized projects are illustrative of Dr. Lee’s approach to bringing advanced research ideas into the real world, for example: advances in artificial intelligence, such as deep neural networks for computer vision and the simultaneous language translation feature in Skype; new silicon and post-silicon computer architectures for Microsoft’s Azure cloud, and experimental under-sea datacenters; next-generationaugmented-reality experiences for HoloLens and virtual reality devices; large-scale digital storage in DNA; and AI-powered socio-technological experiments such as XiaoIce and Tay.
Prior to joining Microsoft, Dr. Lee held executive positions in both government and academia. At the Defense Advanced Research Projects Agency (DARPA), he founded a new division focused on research and development programs in computing and related areas in the social and physical sciences. One example of his work at DARPA was the DARPA Network Challenge, an open competition that mobilized millions of people worldwide in a hunt for red weather balloons — a unique experiment in social media and open innovation that altered the thinking throughout the Department of Defense on the power of social networks.
Before DARPA, Lee served as Head of Carnegie Mellon University’s top-ranked computer science department and also briefly as the university’s Vice Provost for Research. As a Professor of Computer Science, he carried out research in computer security, software reliability, program analysis, and language design. He published over 90 research papers in peer-reviewed journals and conference proceedings, several of which have been recognized with “test of time” awards, including the ACM SIGOPS 2006 Hall of Fame Award, for their seminal contributions to the field. At CMU, he was a devoted, award-winning teacher, and advised doctoral students to 15 completed Ph.D.’s who today are working across academia and industry.
Peter Lee is a Fellow of the Association for Computing Machinery. He is a dedicated advocate for the academic research community, serving in a variety of national and international venues. In 2016, he was appointed by President Obama to the President’s Commission on Enhancing National Cybersecurity. He is a member of the National Academies’ Computer Science and Telecommunications Board, where he recently chaired key studies on the impact of federal research investments on economic growth. Dr. Lee is a member of the Advisory Committee for the National Science Foundation’s Computer and Information Science and Engineering Directorate, and the former Chair of Board of the Computing Research Association. In 2010, Dr. Lee co-chaired a review of federal investments in networking and information technology for the President’s Council of Advisors on Science and Technology. Dr. Lee has also appeared before both the US House Science and Technology Committee and the US Senate Commerce Committee, testifying on the importance of federal investments in basic research – the Federal NITRD program and the America COMPETES Act – to the nation’s economy, global competitiveness, innovation, and national security. In the tech industry, Dr. Lee is a highly sought public speaker, widely quoted on industry trends and disruptive innovation organizations such as the New York Times, MIT Technology Review, Wired, Fast Company, The Economist, ArsTechnica, CNN, Seattle Times, and dozens of other universities and media outlets.
Tie-Yan Liu, Microsoft Research
Tie-Yan Liu is a principal researcher of Microsoft Research Asia, leading the machine learning group. His research interests include artificial intelligence, machine learning, information retrieval, data mining, and computational economics. As a researcher in an industrial lab, Tie-Yan is making his unique contributions to the world. On one hand, many of his technologies have been transferred to Microsoft’s products and online services, such as Bing, Microsoft Advertising, and Azure. He has received many recognitions and awards in Microsoft for his significant product impacts. On the other hand, he has been actively contributing to the academic community. He is an adjunct professor at CMU and several universities in China, and an honorary professor at Nottingham University. He is frequently invited to chair or give keynote speeches at major machine learning and information retrieval conferences. He is a senior member of the IEEE and the ACM, as well as a senior member, distinguished speaker, and academic committee member of the CCF.
Marti A. Hearst, University of California, Berkeley
Dr. Marti Hearst is a professor in the School of Information and the EECS Department at UC Berkeley. Her primary research interests are user interfaces for search engines, information visualization, natural language processing, and improving MOOCs. She wrote the first book on Search User Interfaces. Prof. Hearst was named a Fellow of the ACM in 2013 and has received an NSF CAREER award, an IBM Faculty Award, two Google Research Awards, an Okawa Foundation Fellowship, four Excellence in Teaching Awards, and has been principal investigator for more than $3.5M in research grants.
Prof. Hearst is currently Vice President-elect of the Association for Computational Linguistics (ACL). She has served on the Advisory Council of NSF’s CISE Directorate and is currently on the Web Board for CACM, member of the Usage Panel for the American Heritage Dictionary, and on the Edge.org panel of experts. She is on the editorial board of ACM Transactions on Computer-Human Interaction (TOCHI) and was formerly on the boards of ACM Transactions on the Web (TWEB), Computational Linguistics, ACM Transactions on Information Systems (TOIS), and IEEE Intelligent Systems.
Prof. Hearst received BA, MS, and PhD degrees in Computer Science from the University of California at Berkeley, and she was a Member of the Research Staff at Xerox PARC from 1994 to 1997.
Hsiao-Wuen Hon, Microsoft Research
Dr. Hsiao-Wuen Hon is corporate vice president of Microsoft, chairman of Microsoft’s Asia-Pacific R&D Group, and managing director of Microsoft Research Asia. He drives Microsoft’s strategy for research and development activities in the Asia-Pacific region, as well as collaborations with academia.
Dr. Hon has been with Microsoft since 1995. He joined Microsoft Research Asia in 2004 as deputy managing director, stepping into the role of managing director in 2007. He founded and managed Microsoft Search Technology Center from 2005 to 2007 and led development of Microsoft’s search products (Bing) in Asia-Pacific. In 2014, Dr. Hon was appointed as chairman of Microsoft Asia-Pacific R&D Group.
Prior to joining Microsoft Research Asia, Dr. Hon was the founding member and architect of the Natural Interactive Services Division at Microsoft Corporation. Besides overseeing architectural and technical aspects of the award-winning Microsoft Speech Server product, Natural User Interface Platform and Microsoft Assistance Platform, he was also responsible for managing and delivering statistical learning technologies and advanced search. Dr. Hon joined Microsoft Research as a senior researcher in 1995 and has been a key contributor to Microsoft’s SAPI and speech engine technologies. He previously worked at Apple, where he led research and development for Apple’s Chinese Dictation Kit.
An IEEE Fellow and a distinguished scientist of Microsoft, Dr. Hon is an internationally recognized expert in speech technology. Dr. Hon has published more than 100 technical papers in international journals and at conferences. He co-authored a book, Spoken Language Processing, which is a graduate-level textbook and reference book in the area of speech technology used in universities around the world. Dr. Hon holds three dozen patents in several technical areas.
Dr. Hon received a Ph.D. in Computer Science from Carnegie Mellon University and a B.S. in Electrical Engineering from National Taiwan University.
Seong-Whan Lee, Korea University
Seong-Whan Lee obtained his B.S. in Computer Science and Statistics from Seoul National University in 1984, and M.S. and Ph.D. in Computer Science from Korea Advanced Institute of Science and Technology (KAIST) in 1986 and 1989, respectively. Dr. Lee is the Hyundai Motor chair professor and the head of Department of Brain and Cognitive Engineering of Korea University since 2009.
Dr. Lee is a fellow of IEEE, IAPR and Korean Academy of Science and Technology, and has served several professional societies and governmental committees as a chairman or governing board member. He was the founding Editor-in-Chief of the International Journal of Document Analysis and Recognition and has been an Associate Editor of several international journals: ACM Trans. on Applied Perception, IEEE Trans. on Affective Computing, Pattern Recognition, Image and Vision Computing, International Journal of Pattern Recognition and Artificial Intelligence, and International Journal of Image and Graphics.
He served as the General Chair of the 1st IEEE International Workshop on Biologically Motivated Computer Vision(2000), the 6th IEEE International Conference on Automatic Face and Gesture Recognition(2004), the 2nd IEEE International Conference on Biometrics(2007), and the 11th IEEE International Conference on Systems, Man, and Cybernetics(2012). He also served as the Program Chair of the 8th IEEE International Conference Document Analysis and Recognition(2005) and the 18th IAPR International Conference on Pattern Recognition(2006).
Currently, he is serving as the president of the Korea Artificial Intelligence Society. His research interests include artificial intelligence, pattern recognition, and brain and cognitive engineering. He has authored more than 300 publications in international journals and conference proceedings with 8,921 Google Scholar citations and 10 books.
Katsu Ikeuchi, Microsoft Research
Katsushi Ikeuchi received the BE degree in Mechanical Engineering from Kyoto University in 1973 and the PhD degree in Information Engineering from the University of Tokyo in 1978. After working at the MIT-AI Lab, CMU-Robotics Institute, U Tokyo-Institute of Industrial Science, he joined Microsoft Research Asia as a Principal Researcher in 2015. During this tenure, he supervised more than 50 PhD students. His research interest spans computer vision, robotics, and computer graphics. In these research fields, he has received several best paper awards, including the David Marr award. His community service include a dozen general or program chairs of major international conferences, including CVPR’96, ICCV’15. He is the editor-in-chief of the International Journal of Computer Vision. He received the Distinguished Researcher Award from IEEE-PAMI, Medal of Honor with Purple Ribbon (Shiju-ho-syo) from Japanese Emperor and the Okawa award from Okawa foundation.
Hiroshi Ishiguro, Osaka University
Hiroshi Ishiguro (M’) received a D.Eng. in systems engineering from the Osaka University, Japan in 1991. He is currently Professor of Department of Systems Innovation in the Graduate School of Engineering Science at Osaka University (2009-), Distinguished Professor of Osaka University (2013-) and visiting Director (2014-) (group leader: 2002-2013) of Hiroshi Ishiguro Laboratories at the Advanced Telecommunications Research Institute and an ATR fellow. His research interests include distributed sensor systems, interactive robotics, and android science. He has published more than 300 papers in major journals and conferences, such as Robotics Research and IEEE PAMI. On the other hand, he has developed many humanoids and androids, called Robovie, Repliee, Geminoid, Telenoid, and Elfoid. These robots have been reported many times by major media, such as Discovery channel, NHK, and BBC. He has also received the best humanoid award four times in RoboCup. In 2011, he won the Osaka Cultural Award presented by the Osaka Prefectural Government and the Osaka City Government for his great contribution to the advancement of culture in Osaka. In 2015, he received the Prize for Science and Technology (Research Category) by the Minister of Education, Culture, Sports, Science and Technology (MEXT).
Masayuki Inaba, The University of Tokyo
Masayuki Inaba is a professor of Department of Creative Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He received Dr. of Engineering of Information Engineering from The University of Tokyo in 1986. He was appointed as a lecturer in 1986, an associate professor in 1989, and a professor in 2000 at The University of Tokyo. His research interests include key technologies of robotic system, humanoid and software architecture for advanced robots. His research projects have included hand-eye coordination in rope handling, vision-based robotic server system, remote-brained robot approach, whole-body behaviors in humanoids, robot sensor suit with electrically conductive fabric, musculoskeltal humanoid development, humanoid specialization for home assistance, and developmental integration systems with open source robot platforms. He received several awards including outstanding Paper Awards in 1987, 1998, 1999 and 2015 from the Robotics Society of Japan, JIRA Awards in 1994, ROBOMECH Awards in 1994 and 1996 from the division of Robotics and Mechatronics of Japan Society of Mechanical Engineers, and Best Paper Awards of International Conference on Humanoids in 2000 and 2006, ICRA Conference Best Paper Award in 2014 with JSK Robotics Lab members.
Jin Bae Park, Yonsei University
Prof. Jin Bae Park is a professor of the School of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea. His major research interests include intelligent mobile robot, drone control, adaptive dynamic programming, fuzzy logic control, neural networks, artificial intelligence, and genetic algorithms. He received his bachelor’s degree from Yonsei University in 1977 and received his master’s and doctoral degrees from Kansas State University in 1985 and 1990, respectively.
After joining Yonsei University in 1992, Professor Park has been appointed for major leadership positions in the University, including Dean of Information and Communication Services (2004-2005), Dean of Admissions (2005-2006), Dean of Research Affairs/President of University-Industry Foundation (2006-2008). Senior Vice President for Administration and Development (2014-2016) and Senior Vice President of International Campus (2015-2016).
He was the editor-in-chief for International Journal of Control, Automation, and Systems from January 2006 to December 2010, and the President for the institution of Control, Robotics and Systems (ICROS) in 2013. He has published more than 1,200 papers and has been received more than 30 awards because of his academic excellence including research, teaching, and services.
Qiang Huo, Microsoft Research
Dr. Qiang HUO is a Principal Research Manager of Speech Group of Microsoft Research Asia (MSRA). Prior to joining MSRA in August 2007, Qiang had been a faculty member at the Department of Computer Science, The University of Hong Kong (HKU) since 1998. From 1995 to 1997, he worked at Advanced Telecommunications Research Institute (ATR) in Kyoto, Japan. In the past 30 years, he has been doing research and making contributions in the areas of speech recognition, ink recognition, OCR, gesture recognition, biometric-based user authentication, hardware design for speech and image processing. Many multimodal interaction technologies Qiang and his team invented and developed have been deployed in Microsoft’s products and services such as Windows, Windows Phone, Office, OneNote, OneDrive, Bing Translator, Bing Dictionary, Microsoft Speech Platform, Microsoft Cognitive Services.
Jingdong Chen, Northwestern Polytechnical University
Professor Jingdong Chen received the Ph.D. degree in pattern recognition and intelligence control from the National Laboratory of Pattern Recognition, Chinese Academy of Sciences in 1998. He is currently a professor at the Northwestern Polytechnical University (NPU) in Xi’an, China. Before joining NPU in Jan. 2011, he served as the Chief Scientist of WeVoice Inc. in New Jersey for one year. Prior to this position, he was with Bell Labs in New Jersey for nine years. Before joining Bell Labs, he held positions at the Griffith University in Brisbane, Australia and the Advanced Telecommunications Research Institute International (ATR) in Kyoto, Japan. His research interests include acoustic signal processing, microphone array processing, speech enhancement, and adaptive noise/echo control. He co-authored 11 monograph books, published nearly 200 papers in academic journals and conferences.
Dr. Chen served as an Associate Editor of the IEEE Transactions on Audio, Speech, and Language Processing from 2008 to 2014 and as a technical committee (TC) member of the IEEE Signal Processing Society (SPS) TC on Audio and Electroacoustics from 2007 to 2009. He is currently a member of the IEEE SPS TC on Audio and Acoustic Signal Processing. He was the General Chair of IWANC 2016, the Technical Program Chair of IEEE TENCON 2013, a Technical Program Co-Chair of IEEE WASPAA 2009, IEEE ChinaSIP 2014, IEEE ICSPCC 2014, and IEEE ICSPCC 2015, and helped organize many other conferences.
Dr. Chen received the 2008 Best Paper Award from the IEEE Signal Processing Society, the best paper award from the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) in 2011, the Bell Labs Role Model Teamwork Award twice, respectively, in 2009 and 2007, the NASA Tech Brief Award twice, respectively, in 2010 and 2009, the Young Author Best Paper Award from the National Conference on Man-Machine Speech Communications in 1998. He is also the Co-author of a paper for which Chao Pan received the IEEE Region 10 Distinguished Student Paper Award (First Prize) in 2016. Dr. Chen was a recipient of the Japan Trust International Research Grant from the Japan Key Technology Center in 1998 and the “Distinguished Young Scientists Fund” from the National Natural Science Foundation of China (NSFC) in 2014.
Tatsuya Kawahara, Kyoto University
Professor Tatsuya Kawahara received B.E. in 1987, M.E. in 1989, and Ph.D. in 1995, all in information science, from Kyoto University, Kyoto, Japan. From 1995 to 1996, he was a Visiting Researcher at Bell Laboratories, Murray Hill, NJ, USA. Currently, he is a Professor in the School of Informatics, Kyoto University. He has also been an Invited Researcher at ATR and NICT.
He has published more than 300 technical papers on speech recognition, spoken language processing, and spoken dialogue systems. He has been conducting several speech-related projects in Japan including speech recognition software Julius and the automatic transcription system for the Japanese Parliament (Diet).
From 2003 to 2006, he was a member of IEEE SPS Speech Technical Committee. He was a general chair of IEEE Automatic Speech Recognition and Understanding workshop (ASRU 2007). He also served as a Tutorial Chair of INTERSPEECH 2010 and a Local Arrangement Chair of ICASSP 2012. He is an editorial board member of Elsevier Journal of Computer Speech and Language, APSIPA Transactions on Signal and Information Processing, and IEEE/ACM Transactions on Audio, Speech, and Language Processing. He is VP-Publications (BoG member) of APSIPA and a senior member of IEEE.
Kai Yu, Shanghai Jiao Tong University
Professor Kai Yu is the director of the SpeechLab at Computer Science and Engineering Department of Shanghai Jiao Tong University (SJTU) and the co-founder and Chief Scientist of AISpeech Ltd. Prior to joining SJTU as a research professor, he was a senior research associate at Cambridge University and a co-founder of VocalIQ which was later acquired by Apple. He got his Bachelor and Master from Tsinghua University in 1999, 2002 and his Ph.D. from Cambridge University in 2006. He is a senior member of IEEE and the associate director of the technical committee of the Alliance of Intelligent Speech Technology Industry of China. His research interests include dialogue systems, speech recognition, synthesis, speaker verification, natural language processing and machine learning. He has published over 80 peer reviewed papers and is one of the recipients of the ISCA Computer Speech and Language Best Paper Award (2008-2012). He was selected into the “1000 Overseas Talent Plan (Young Talent)” by Chinese central government, the “Excellent Young Scientists Project” by NSFC China and the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning. He was awarded WuWenJun Science and Technology Progress Award by the Chinese Association for Artificial Intelligence for his contribution in commercializing speech technology for language learning.
Ming Zhou, Microsoft Research
Dr. Ming Zhou is a principal researcher and manager of Natural Language Computing Group in Microsoft Research Asia. He is the chair of Chinese Information Technology Committee of Chinese Computer Federation and executive member of Chinese Information Processing Society.
He designed the CEMT-I machine translation system in 1989, the first experiment of Chinese-English machine translation in Mainland China. He designed the famous Chinese-Japanese machine translation software product J-Beijing in Japan which was deployed in J-Server, the popular translation service in Japan that was granted Makoto Nagao Award by Japan Machine Translation Association in 2008. He is the leader of the famous AI gaming of Chinese Couplets/Poetry Generation and Riddles(http://duilian.msra.cn), and the English Assistance Search Engine, Engkoo, which won the Wall Street Journal’s 2010 Asian Innovation Readers’ Choice Award and was shipped in Bing in 2011 as Bing Dictionary(http://cn.bing.com/dict/), and Engkoo cloud IME which was shipped as Bing IME in 2012. Recently, his group has closely worked with MS product teams and shipped famous chat-bot products in China(Xiaoice), Japan(Rinna) and US(Tay).
Dr. Zhou received his B.S. degree in computer engineering from Chongqing University in 1985, and his M.S. degree and Ph.D. in computer science from Harbin Institute of Technology in 1988 and 1991. He did post-doctoral work at Tsinghua University from 1991 to 1993, then he became an associate professor. During 1996-1999, during his sabbatical leave, he worked for Kodensha Ltd. Co. in Japan as the leader of the Chinese-Japanese machine translation project. He joined the natural language group at Microsoft Research China (now Microsoft Research Asia) in Sept. 1999.
Tim Baldwin, University of Melbourne
Tim Baldwin is a Professor in the Department of Computing and Information Systems, The University of Melbourne, and an Australian Research Council Future Fellow. He has previously held visiting positions at Cambridge University, University of Washington, University of Tokyo, Saarland University, NTT Communication Science Laboratories, and National Institute of Informatics. His research interests include text mining of social media, computational lexical semantics, information extraction and web mining, with a particular interest in the interface between computational and theoretical linguistics. Current projects include web user forum mining, monitoring and text mining of Twitter, and text analytics for the creative industries.
Tim completed a BSc(CS/Maths) and BA(Linguistics/Japanese) at The University of Melbourne in 1995, and an MEng(CS) and PhD(CS) at the Tokyo Institute of Technology in 1998 and 2001, respectively. Prior to joining The University of Melbourne in 2004, he was a Senior Research Engineer at the Center for the Study of Language and Information, Stanford University (2001-2004).
Jun Zhao, Institute of Automation, Chinese Academy of Sciences
Dr Zhao Jun is a professor at the National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences. He received his PhD degree from Tsinghua University in 1998. Before joining NLPR in 2002, he worked in the Hong Kong University of Science and Technology as a postdoctoral research fellow. His current research focuses on natural language processing, information extraction and question answering. Prof Zhao has published over 50 peer-reviewed papers in the prestigious conferences and journals, including ACL, SIGIR, TKDE, JLMR, IJCAI, EMNLP, etc. His paper “Relation Classification via Convolutional Deep Neural Network” obtained best paper award of COLING-2014. His paper “Collective entity linking in web text: a graph-based method” ranks 2 in the highest referenced papers of SIGIR in recent five years with the Google academic search. He also served as workshop cochair for ACL-2016.
Evelyne Viegas, Microsoft Research
Evelyne Viegas is the Director of Artificial Intelligence Outreach at Microsoft Research, based in Redmond, U.S.A. In her current role, Evelyne is building initiatives which focus on information seen as an enabler of innovation, working in partnership with universities and government agencies worldwide. In particular she is creating programs around computational intelligence research to drive open innovation and agile experimentation via cloud-based services; and projects to advance the state-of-the-art in artificial intelligence and data-driven research including knowledge representation, machine learning and reasoning under uncertainty at scale.
Nicole Beckage, the University of Kansas
Nicole M Beckage is an assistant professor at the University of Kansas department of Electrical Engineering and Computer Science. Her work centers on the use of machine learning and complex systems approaches to understand learning and cognition. Applications of her work include predictive models of language acquisition, models of decision making, and assessment of information in qualitative research interviews. She received the NSF Graduate Research Fellowship in 2010 and has interned with Microsoft Research and Pearson Education.
Chang D. Yoo, KAIST
Chang D. Yoo is a Professor in the School of Electrical Engineering at KAIST. He holds a Ph.D. in Electrical Engineering and Computer Science from MIT, an M.S. in Electrical Engineering from Cornell University, and a B.S. in Engineering and Applied Science from Caltech. His research interest are in machine learning for signal processing and speech and image processing. His current interests are in developing deep architectures for speech, face and cell classification, blind source separation, source localization and computer vision.
He was a visiting scientist at RLE, MIT in 2005 and 2015. He was on the IEEE Technical Committee on Machine Learning for Signal Processing from 2009 to 2011. He served as an Associate Editor of IEEE SIGNAL PROCESSING LETTERS, IEEE TRANSACTIONS ON AUDIO, SPEECH AND LANGUAGE PROCESSING, and IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY. He was the Dean of Special Projects and Institutional Relations at KAIST and Associate Vice President, of Special Projects and Institutional Relations at KAIST.
Richard Cai, Microsoft Research
Dr. Rui Cai is a lead researcher at Microsoft Research Asia. He received the B.E. and Ph.D. degrees in computer science from Tsinghua University, Beijing, China, in 2001 and 2006, respectively. His research interests include computer vision, multimedia content analysis, web search and data mining. He has published more than 40 quality papers in referred international conferences and journals, including ICCV, CVPR, KDD, WWW, SIGIR, KDD, ACM Multimedia, etc. He also has more than 20 granted US / international patents.”
Min H. Kim, KAIST
Min H. Kim is an associate professor of computer science at KAIST, Korea, leading the Visual Computing Laboratory (VCLAB). Prior to KAIST, he worked as a postdoctoral researcher at Yale University. He received his PhD in computer science from University College London (UCL) in 2010, with a focus on color reproduction in computer graphics. In addition to serving on many conference program committees, he has been an associate editor of ACM Transactions on Graphics (TOG), ACM Transactions on Applied Perception (TAP), and Elsevier Computers and Graphics (CAG). His research interests include computational imaging, such as computational photography, 3D imaging, and hyperspectral imaging, in addition to color and visual perception.
Yebin Liu, Tsinghua University
Yebin Liu is an associate professor in Automation Department, Tsinghua University. He received the B.E. degree from the Beijing University of Posts and Telecommunications, China, in 2002, and the PhD degree from the Automation Department, Tsinghua University, Beijing, China, in 2009. His research areas include computer vision, computer graphics and computational photography and mainly focus in capture and reconstruction of real world visual information. He has been awarded the NSFC Excellent Young Scientist Grant in 2015 and the First Prize of National Science and Technology Invention Award in 2012.
Yasuyuki Matsushita, Osaka University
Prof. Yasuyuki Matsushita received his B.S., M.S. and Ph.D. degrees in EECS from the University of Tokyo in 1998, 2000, and 2003, respectively. From April 2003 to March 2015, he was with Visual Computing group at Microsoft Research Asia. In April 2015, he joined Osaka University as a professor. His research area includes computer vision, machine learning and optimization. He is on the editorial board of IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), International Journal of Computer Vision (IJCV) and has served/is serving as a Program co-chair of PSIVT 2010, 3DIMPVT 2011, ACCV 2012, ICCV 2017, and a General co-chair for ACCV 2014. He is a senior member of IEEE.
Mu Li, Microsoft Research
Mu Li is a senior researcher in Natural Language Computing Group of Microsoft Research Asia. He received Ph.D. degree from Northeastern University, China in March 2001, and then joint Microsoft Research. His interests ranges from machine translation, language modeling, syntactic parsing, Asian language processing, deep learning and other NLP and machine learning tasks. He had published over 50 papers in top AI and NLP conferences and journals including ACL, EMNLP, AAAI, CL etc., and his current focus is to build large-scale practical neural machine translation system.
Boxing Chen, National Research Souncil Canada (NRC)
Boxing Chen is a Research Officer at the National Research Council Canada (NRC). He works on natural language processing, mainly focus on machine translation. Prior to NRC, he was a Senior Research Fellow at the Institute for Infocomm Research in Singapore, a Postdoc at FBK-IRST in Italy and a Postdoc at the University of Grenoble in France. He received his PhD degree from Chinese Academy of Science in 2003. He has co-authered more than 40 papers in NLP conferences and journals. His teams ranked the first place in the NIST 2012 OpenMT Chinese-to-English translation, the first place in the IWSLT 2007 and 2005 Chinese-to-English spoken language translation evaluation.
Jiajun Zhang, Chinese Academy of Sciences
Jiajun Zhang is an associate professor at National Laboratory of Pattern Recognition, Institute of Automation Chinese Academy of Sciences, where he received the Ph.D degree in June 2011. His research interests include machine translation, deep learning, multi-lingual natural language processing. He has published more than 20 papers in top conference including AAAI, IJCAI, ACL, EMNLP, COLING and in international journals including IEEE/ACM TASLP, IEEE Intelligent Systems, ACM TALLIP and TACL. He also received several best papers from PACLIC-2009, NLPCC-2012 and CWMT-2014.
Satoshi Nakamura, Nara Institute of Science and Technology
Dr. Satoshi Nakamura is Professor of Graduate School of Information Science, Nara Institute of Science and Technology, Japan, Honorarprofessor of Karlsruhe Institute of Technology, Germany. He received his B.S. from Kyoto Institute of Technology in 1981 and Ph.D. from Kyoto University in 1992. He was Associate Professor of Graduate School of Information Science at Nara Institute of Science and Technology in 1994-2000. He was Director of ATR Spoken Language Communication Research Laboratories in 2000-2008 and Vice president of ATR in 2007-2008. He was Director General of Keihanna Research Laboratories and the Executive Director of Knowledge Creating Communication Research Center, National Institute of Information and Communications Technology, Japan in 2009-2010. He is currently Director of Augmented Human Communication laboratory and a full professor of Graduate School of Information Science at Nara Institute of Science and Technology. He is interested in modeling and systems of speech-to-speech translation, spoken dialog systems and speech recognition. He is one of the leaders of speech-to-speech translation research and has been serving for various speech-to-speech translation research projects in the world including C-STAR, IWSLT and A-STAR. He served as a project leader of the network-based commercial speech-to-speech translation service for 3-G mobile phones in 2007 and VoiceTra project for iPhone in 2010. He received the Commendation for Science and Technology by the Minister of Education, Science and Technology, and the Commendation for Science and Technology by the Minister of Internal Affair and Communications. He also received LREC Antonio Zampolli Award 2012. He has been an Elected Board Member of International Speech Communication Association, ISCA since June 2011, IEEE SPS Speech and Language Technical Committee Member since 2013, and IEEE Fellow since January 2016.
Jingdong Wang, Microsoft Research
Jingdong Wang is a Lead Researcher at the Internet Media Group, Microsoft Research, Beijing, China. He received the B. Eng. and M. Eng. degrees in Automation from the Department of Automation, Tsinghua University, Beijing, China, in 2001 and 2004, respectively, and the PhD degree in Computer Science from the Department of Computer Science and Engineering, the Hong Kong University of Science and Technology, Hong Kong, in 2007. His areas of interest include computer vision, machine learning, and multimedia. He is currently working on deep learning, human understanding, person re-identification, multimedia search, and large-scale indexing. He has served or will serve as an area chair in CVPR 2017, ECCV 2016, ACMMM 2015 and ICME 2015, a track chair in ICME 2012, a special session chair in ICMR 2014. He has also been invited to serve as an editorial board member for IEEE Transactions on Multimedia, the international journal of Multimedia Tools and Applications, an associate editor of the international journal of Neurocomputing. He has shipped 10+ technologies to Microsoft products, including XiaoIce, Microsoft cognitive service, and Bing search.
Peng Cui, Tsinghua University
Dr. Peng Cui is an Assistant Professor in Tsinghua University. His research interests include network representation learning, social dynamics modeling and human behavioral modeling. He has published more than 60 papers in prestigious conferences and journals in data mining and multimedia. His recent research won the ICDM 2015 Best Student Paper Award, SIGKDD 2014 Best Paper Finalist, IEEE ICME 2014 Best Paper Award, ACM MM12 Grand Challenge Multimodal Award, and MMM13 Best Paper Award. He is the Area Chair of ICDM 2016, ACM MM 2014-2015, IEEE ICME 2014-2015, ICASSP 2013, Associate Editor of ACM TOMM, Elsevier Journal on Neurocomputing. He was the recipient of ACM China Rising Star Award in 2015.
Lexing Xie, Australian National University
Lexing Xie is Associate Professor in the Research School of Computer Science at the Australian National University, she leads the ANU Computational Media lab. Her research areas are in machine learning, multimedia, social media. Of particular recent interest are stochastic time series models, neural network for sequences, and active learning, applied to diverse problems such as multimedia knowledge graphs, modeling popularity in social media, joint optimization and structured prediction problems, and social recommendation. Lexing’s research has received six best student paper and best paper awards between 2002 and 2015. She is IEEE Circuits and Systems Society Distinguished Lecturer 2016-2017. She currently serves an associate editor of ACM Trans. MM, ACM TiiS and PeerJ Computer Science. Her service roles include the program and organizing committees of major multimedia, machine learning, web and social media conferences. She was research staff member at IBM T.J. Watson Research Center in New York from 2005 to 2010, and adjunct assistant professor at Columbia University 2007-2009. She received B.S. from Tsinghua University, Beijing, China, and Ph.D. from Columbia University, all in Electrical Engineering.
Toshihiko Yamasaki, The University of Tokyo
Toshihiko YAMASAKI is an Associate Professor, at the Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo. He received the B.S. degree, the M.S. degree, and the Ph.D. degree from The University of Tokyo in 1999, 2001, and 2004, respectively. He is currently an Associate Professor at Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo. He was a JSPS Fellow for Research Abroad and a visiting scientist at Cornell University from Feb. 2011 to Feb. 2013. His current research interests include multimedia big data analysis, pattern recognition, machine learning, and so on. His publication includes three book chapters, more than 55 journal papers, more than 160 international conference papers, more than 470 domestic conference papers. He has received around 50 awards.
Kuansan Wang, Microsoft Research
Dr. Kuansan Wang came to Microsoft Research in March 1998, first as a Researcher in the speech technology group working on the areas of spoken language understanding and dialog modeling. He contributed to the project MiPad and created the Speech Application Language Tags (SALT) that is now part of the international standards ISO/IEC 18051/ECMA-269/ETSI TS 102 173, ISO/IEC 18056/ECMA-323/ETSI TS 101 990, and ECMA-348/ISO IEC 18450. An object model version, described in this TR he wrote, has entered its final phase of being standardized. He also contributed to the world wide web consortium (W3C) Speech Recognition Grammar Specification (SRGS), W3C Speech Synthesis Markup Language (SSML), and various other publications from W3C Multimodal Interaction Working Group. Many of his research papers can still be found at the speech group’s publication list and video demo area.
In January 2004, Dr. Kuansan Wang moved to the speech product group and became a software architect. There he helped create and ship the product Microsoft Speech Server, which is still powering the corporate call center for Microsoft. If you calling into Microsoft’s main number, you will be greeted by his automated operator, MS Connect. In this capacity, he also managed the revision of the speech system used in the Microsoft Voice Command, an add-on to Windows Mobile smart phone that allows users to operate their smart phones with voice in an eyes-busy, hands-busy environment. Many of the technologies are still in use in Cortana, a virtual personal assistant from Microsoft.
Dr. Kuansan Wang was a founding member of an incubation group inside Microsoft that shipped Microsoft Response Point, a speech-enabled small business phone system that uses voice over Internet Protocol (VoIP) technologies. Because the incubation group was structured to run like a start-up inside Microsoft, he had the opportunity to be the acting development manager and later the testing manager to build the engineering team from ground up. In addition to the speech capabilities, he was also responsible for ensuring the product is easy to setup and easy to use, including the invention of the magic “Response Point button” that earns Microsoft revenue on every phone sold without even having Microsoft software on it!
Since September 2007, Dr. Kuansan Wang has been back in Microsoft Research (MSR), joining the newly founded Internet Service Research Center with a mission to revolutionize online services and make Web more intelligent. He has been teaching the machine to read the massive web contents to extract the knowledge, to understand users’ interests and anticipate their needs, and to serve and alert the web knowledge to users in a helpful way, including engaging in a natural conversation or multimodal dialog. The first application, on changing the way web search works in Bing, was first announced at MSR Faculty Summit in July 2010. It is exhilarating to see that, since that public disclosure, major web search companies, such as Google (in 2012) and Baidu (in 2014), have also introduced similar services into their products. To ensure the research community can verify, replicate and advance our results, components and data sets underlying my research work have been made available through Microsoft Cognitive Services, ranging from the web scale Markov N-gram to Knowledge Exploration Service. In March 2016, he has taken on an additional role as a Managing Director of MSR Outreach, an organization with the mission to serve the research community. In addition to applying the intelligent technologies to make Bing and Cortana smarter in gathering and serving academic knowledge, we are also starting an experimental website, academic.microsoft.com (powered by Academic API), and mobile apps dedicated to exploring new service scenarios for active researchers.
Before joining Microsoft, he worked at Bell Labs from 1994 to 1996, and the NYNEX (now part of Verizon) Science and Technology Center. He received my M.S. and Ph.D. from the University of Maryland in 1989 and 1994, and my B.S. from National Taiwan University in 1986, all in Electrical Engineering.
Xueqi Cheng, Institute of Computing Technology, Chinese Academy of Sciences
Dr. Xueqi Cheng is a professor in the Institute of Computing Technology, Chinese Academy of Sciences (CAS), and the director of the CAS Key Laboratory of Network Data Science and Technology. His main research areas include Web search and data mining, data science, and social media analytics et al. He is the general secretary of CCF Task Force on Big Data, the vice-chair of CIPS Task Force on Chinese Information Retrieval. He is the associate editor of IEEE Transactions on Big Data, Editorial Board Member of Journal of Computer Science and Technology and Chinese Journal of Computer. He was the general co-chair of ACM WSDM’15, Steering Committee co-chair of IEEE Conference on Big Data, PC chair of ChinaCom’12, and PC members of more than 20 conferences, including ACM SIGIR, WWW, ACM CIKM, ACL, IEEE ICDM, IJCAI, and ACM WSDM. He has more than 100 publications, and was awarded the Best Paper Award in ACM CIKM’11, and the Best Student Paper Award in ACM SIGIR’12. He is the founder of the open academic platform system (soscholar.com). He is also the principal investigator of more than 10 major research projects, funded by NSFC and MOST. He was awarded the NSFC Distinguished Youth Scientist (2014), the National Prize for Progress in Science and Technology (2012), the China Youth Science and Technology Award (2011).
Seung-won Hwang, Yonsei University
Prof. Seung-won Hwang is a Professor of Computer Science at Yonsei University. Prior to joining Yonsei, she had been an Associate Professor at POSTECH for 10 years, after her PhD from UIUC. Her recent research interest has been data(-driven) intelligence, led to 100+ publication at top-tier database/mining, AI, and NLP venues, including ACL, AAAI, SIGMOD, VLDB, and ICDE. She has received best paper runner-up and outstanding collaboration award from WSDM and Microsoft Research respectively.
Irwin King, The Chinese University of Hong Kong
Irwin King’s research interests include machine learning, social computing, web intelligence, data mining, and multimedia information processing for Big Data. In these research areas, he has over 200 technical publications in journals and conferences. In addition, he has contributed over 30 book chapters and edited volumes.
Prof. King is the Book Series Editor for Social Media and Social Computing with Taylor and Francis (CRC Press). He is also an Associate Editor of the Neural Network Journal and ACM Transactions on Knowledge Discovery from Data (ACM TKDD). Currently, he is a member of the Board of Governors of INNS and a Vice-President and Governing Board Member of APNNA. He also serves INNS as the Vice-President for Membership in the Board of Governors. Moreover, he is the General Chair of WSDM2011, General Co-Chair of RecSys2013, ACML2015, and in various capacities in a number of top conferences such as WWW, NIPS, ICML, IJCAI, AAAI, etc.
Prof. King is Associate Dean (Education), Faculty of Engineering and Professor at the Department of Computer Science and Engineering, The Chinese University of Hong Kong. He is also Director of the Shenzhen Key Laboratory of Rich Media and Big Data. He received his B.Sc. degree in Engineering and Applied Science from California Institute of Technology, Pasadena and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California, Los Angeles. He was on leave to AT&T Labs Research on special projects and also taught courses at UC Berkeley on Social Computing and Data Mining. Recently, Prof. King has been an evangelist in the use of education technologies in eLearning for the betterment of teaching and learning.
Sung-Hyon Myaeng, KAIST
Dr. Sung-Hyon Myaeng is currently a professor in School of Computer Science at Korea Advanced Institute of Science and Technology (KAIST), where he created the Web Science & Technology Division. He is also the Director of KAIST-Microsoft Research Collaboration Center (KMCC). Previously he was on the faculty at Syracuse University, USA, where he was granted tenure in 1994. He earned his MS and Ph. D. from Southern Methodist University, Texas, USA in 1985 and 1987, respectively.
His research has been in the intersection between lexical & semantic aspects in natural language processing and unconventional search techniques in information retrieval, currently focusing on various text mining problems such as human experience mining, trend analysis, & open information extraction with mobile and context-aware applications. He recently published the book “Experiential Knowledge Mining”.
He has served on program committees of many reputable international conferences in the areas of information retrieval, natural language processing, and Word Wide Web, including his role as a co-program chair for ACM SIGIR, 2002 and 2008. In 2008, he won an award from Microsoft Research, based on global competition for the RFP “Beyond Search – Semantic Computing and Internet Economics”. He now serves as Associate Vice President of International Office at KAIST.
Min Song, Yonsei University
Prof. Min Song is the Underwood Distinguished Professor at Yonsei University and a Professor of the Department of Library and Information Science and the director of Text and Social Media Mining Lab at Yonsei University. Prior to Yonsei, he was an Associate Professor in the Department of Information Systems at New Jersey Institute of Technology. Min received the best paper award from EDB in 2013, the outstanding service award from CIKM in 2009. His work received an honorable mention award in the 2006 Greater Philadelphia Bioinformatics Symposium and the Drexel Best Dissertation Award in 2005. He has published 150 journal and conference papers. Min has research interests in Biomedical Text Mining, Social Media Data Mining, and Information Retrieval. He received his PhD in Information Systems from Drexel University, a MA from Indiana University and a BA from Yonsei University in Korea.
Chengxiang Zhai, University of Illinois at Urbana-Champaign
ChengXiang Zhai is a Professor of Computer Science and Willett Faculty Scholar at the University of Illinois at Urbana-Champaign (UIUC), where he also holds a joint appointment at Carl R. Woese Institute for Genomic Biology, Statistics, and School of Information Sciences. He received a Ph.D. in Computer Science from Nanjing University in 1990, and a Ph.D. in Language and Information Technologies from Carnegie Mellon University in 2002. His research interests include information retrieval, text mining, natural language processing, machine learning, biomedical and health informatics, and intelligent education systems. He has published over 200 papers in these areas with high citations. He served as an Associate Editor of ACM Transactions on Information Systems, and Information Processing and Management, and Program Co-Chair of NAACL HLT 2007, ACM SIGIR 2009, and WWW 2015. He is an ACM Distinguished Scientist, and received a number of awards, including ACM SIGIR Test of Time Award (three times), the 2004 Presidential Early Career Award for Scientists and Engineers (PECASE), an Alfred P. Sloan Research Fellowship, IBM Faculty Award, HP Innovation Research Award, Microsoft Beyond Search Research Award, UIUC Rose Award for Teaching Excellence, and UIUC Campus Award for Excellence in Graduate Student Mentoring. He has two MOOCs on Coursera on Text Retrieval and Text Mining, respectively.
Taifeng Wang, Microsoft Research
Taifeng Wang is a lead researcher in Machine Learning group, Microsoft Research Asia. His research interests include machine learning, distributed system, search ads click prediction, graph mining. many of his technologies have been transferred to Microsoft’s products and online services, such as Bing, Microsoft Advertising, and Azure. Currently, he is working on distributed machine learning, and leading Microsoft’s open source project DMTK (Microsoft Distributed Machine Learning Toolkit). He has published tens of papers at top conference and journals and served as the PC member of many premium conferences such as KDD, WWW, SIGIR, IJCAI, and WSDM. He has been tutorial speakers in WWW 2011, SIGIR 2012 and ACML2016, and he has organized a workshop on Deep learning in WSDM 2015.
Dit-Yan Yeung, Hong Kong University of Science and Technology
Dit-Yan Yeung received his BEng degree in electrical engineering and MPhil degree in computer science from the University of Hong Kong, and PhD degree in computer science from the University of Southern California. He started his academic career as an assistant professor at the Illinois Institute of Technology. Currently he is a professor of computer science and engineering at the Hong Kong University of Science and Technology. His research interests are in computational and statistical approaches to machine learning and artificial intelligence as well as novel application of machine learning techniques to computer vision and e-learning.
Masashi Sugiyama, The University of Tokyo
Masashi Sugiyama received the PhD degree in Computer Science from Tokyo Institute of Technology, Japan in 2001. Since 2014 he has been Professor at the University of Tokyo. From 2016, he concurrently serves as Director of RIKEN Center for Advanced Integrated Intelligence Research.
Tao Qin, Microsoft Research
Dr. Tao Qin is a Lead Researcher in Microsoft Research Asia. His research interests include machine learning (with the focus on deep learning and reinforcement learning), artificial intelligence (with applications to chatbots and emotional intelligence), game theory (with applications to cloud computing, online and mobile advertising, ecommerce), information retrieval and computational advertising. He got his PhD degree and Bachelor degree both from Tsinghua University. He is a member of ACM and IEEE, and an Adjunct Professor (PhD advisor) in the University of Science and Technology of China.
James Tin-Yau Kwok, Hong Kong University of Science and Technology
Dr. Kwok is a Professor at the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He served / is serving as an Associate Editor for the IEEE Transactions on Neural Networks and Learning Systems, and the Neurocomputing journal. He has been program chair, area chair and keynote speaker for many international conferences. He is also a Governing Board Member of the Asia Pacific Neural Network Assembly (APNNA).
Chih-Jen Lin, National Taiwan University
Chih-Jen Lin is currently a distinguished professor at the Department of Computer Science, National Taiwan University. He obtained his B.S. degree from National Taiwan University in 1993 and Ph.D. degree from University of Michigan in 1998. His major research areas include machine learning, data mining, and numerical optimization. He is best known for his work on support vector machines (SVM) for data classification. His software LIBSVM is one of the most widely used and cited SVM packages. For his research work he has received many awards, including the ACM KDD 2010 and ACM RecSys 2013 best paper awards. He is an IEEE fellow, a AAAI fellow, and an ACM fellow for his contribution to machine learning algorithms and software design. More information about him can be found at this LINK.
Kyong Mu Lee, Seoul National University
Kyoung Mu Lee received the B.S. and M.S. Degrees in Control and Instrumentation Eng. from Seoul National University (SNU), Seoul, Korea in 1984 and 1986, respectively, and Ph. D. degree in Electrical Engineering from the University of Southern California in 1993. He is currently with the Dept. of ECE at Seoul National University as a professor. Prof. Lee has received several awards, in particular, the Most Influential Paper over the Decade Award by the IAPR Machine Vision Application in 2009, the ACCV Honorable Mention Award in 2007, the Okawa Foundation Research Grant Award in 2006, the Distinguished Professor Award and Outstanding Research Award from the college of Engineering of SNU in 2009 and 2010, respectively. He is currently serving as an Associate Editor in Chief of the IEEE TPAMI, an Area Editor of the CVIU, and has served as an Associate Editor of the IEEE TPAMI, the Machine Vision Application Journal and the IPSJ Transactions on Computer Vision and Applications, and the IEEE Signal Processing Letter. He also has served (or will serve) as a General Chair of ICCV2019, ACM MM2018, Program Chair of ACCV2012, a Track Chair of ICPR2012, a Workshop Chair of ICCV2013, and Area Char of CVPR, ICCV, ECCV many times. He was a Distinguished Lecturer of the Asia-Pacific Signal and Information Processing Association (APSIPA) for 2012-2013. More information can be found on his homepage http://cv.snu.ac.kr/kmlee.
Yu-Gang Jiang, Fudan University
Yu-Gang Jiang is a Professor in School of Computer Science and Vice Director of Shanghai Engineering Research Center for Video Technology and System at Fudan University, China. His Lab for Big Video Data Analytics conducts research on all aspects of extracting high-level information from big video data, such as video event recognition, object/scene recognition and large-scale visual search. He is the lead architect of a few best-performing video analytic systems in worldwide competitions such as the annual U.S. NIST TRECVID evaluation. His visual concept detector library (VIREO-374) and video datasets (e.g., CCV and FCVID) are widely used resources in the research community. His work has led to many awards, including “emerging leader in multimedia” award from IBM T.J. Watson Research in 2009, early career faculty award from Intel and China Computer Federation in 2013, the 2014 ACM China Rising Star Award, and the 2015 ACM SIGMM Rising Star Award. He holds a PhD in Computer Science from City University of Hong Kong and spent three years working at Columbia University before joining Fudan in 2011.
Tatsuya Harada, The University of Tokyo
Tatsuya Harada is a Professor in the Department of Information Science and Technology at the University of Tokyo. His research interests center on a large-scale visual recognition, caption generation from visual information, automatic contents generation and intelligent robot using machine learning. He received his Ph.D. from the University of Tokyo in 2001. He was a visiting scientist at Carnegie Mellon University in 2001 before joining the University of Tokyo in 2001. He is the recipient of Grand Challenge Special Prize on the Best Application of a Theoretical Framework at ACM Multimedia in 2011. He won the fine-grained classification task and the second place in the classification task at ILSVRC in 2012. He won the Visual Question Answering (VQA) challenge (Abstract Scenes) at CVPR in 2016.
Jun Yan, Microsoft Research
Dr. Jun Yan received the Ph.D. degree in digital signal processing and pattern recognition from the department of information science, school of mathematical science, Peking University, P.R. China. During his Ph.D., he has been a research intern of MSRA from 2003 to 2005 and awarded as Microsoft fellow in 2004. Before join Microsoft, he has been a research associate at CBI, HMS, Harvard, Cambridge, MA, in 2005. He joined Microsoft Research Asia (MSRA) from 2006. Currently he is working in the Data Mining and Enterprise Intelligence group of MSRA as a senior research manager.
His research interests are on knowledge mining for AI, text data preprocessing, information retrieval and behavior targeted online advertising etc. So far, he has successfully incubated tens of technologies, which have been used in Microsoft products. In academia, he has more than 60 quality papers published in referred conferences and journals, including SIGKDD, SIGIR, WWW, ICDM, TKDE, etc. He has been the PC members of international conferences SIGKDD, SIGIR etc. and is also reviewers of journals articles TKDE, TPAMI etc.
Juanzi Li, Tsinghua University
Juanzi Li is a full professor from the department of computer science and technology at Tsinghua university. Her research interest is semantic Web and knowledge base building. She is the chair of Knowledge and Language Computing Committee at the Chinese Information Processing Society of China. She is the principal investigator of many important projects supported Natural Science Foundation of China, the framework of EU cooperation projects (FP7), and etc. She has published over 90 papers in top international conferences and journals such as WWW, ACL, SIGIR, IJCAI, TKDE and TKDD. She won Wang Xuan News Science and Technology Award in 2009 and 2011(the first and second prize respectively), 2013 Scientific Innovation Award in Artificial Intelligence Community in China (the first prize).
Huajun Chen, Zhejiang University
Huajun Chen is a full professor of college of computer science, Zhejiang University, China. He is serving as the deputy-director of Key Lab of Big Data Computing of Zhejiang Province, and associate editor of Elsevier Journal of Big Data Research. His research interests are on the Semantic Web, Knowledge Graph, Ontologies and their applications such as biomedicine, smart cities, etc. He once won the best paper award in ISWC2006 (International Semantic Web Conference), and has published papers in referred conferences and journals including AAAI/IAAI, WWW, ICDE, TKDE , IEEE Magazine on Computational Intelligence, Briefings in bioinformatics, etc.
Tim Pan, Microsoft Research
Dr. Tim Pan is outreach senior director of Microsoft Research Asia, responsible for the lab’s academic collaboration in the Asia-Pacific region.
Tim Pan leads a regional team with members based in China, Japan, and Korea engaging universities, research institutes, and certain relevant government agencies. He establishes strategies and directions, identifies business opportunities, and designs various programs and projects that strengthen partnership between Microsoft Research and academia.
Tim Pan earned his Ph.D. in Electrical Engineering from Washington University in St. Louis. He has 20 years of experience in the computer industry and has co-founded two technology companies. Tim has a great passion for talent fostering. He served as a board member of St. John’s University (Taiwan) for 10 years, offered college-level courses, and wrote a textbook about information security. Between 2005 and 2007, Tim worked for Microsoft Research Asia as a university relations manager for Taiwan and Hong Kong. He rejoined Microsoft Research Asia in 2012.
Juliana Freire, New York University
Juliana Freire is a Professor of Computer Science and Data Science at New York University. She is the Executive Director of the NYU Moore Sloan Data Science Environment. She holds an appointment at the Courant Institute for Mathematical Science, is a faculty member at the NYU Center for Urban Science and Progress and at the NYU Center of Data Science, where she is also the Director of Graduate Studies. Her recent research has focused on big-data analysis and visualization, large-scale information integration, provenance management, and computational reproducibility. Prof. Freire is an active member of the database and Web research communities, with over 150 technical papers, several open-source systems, and 11 U.S. patents. She is an ACM Fellow and a recipient of an NSF CAREER, two IBM Faculty awards, and a Google Faculty Research award. She has chaired or co-chaired several workshops and conferences, and participated as a program committee member in over 70 events. Her research grants are from the National Science Foundation, DARPA, Department of Energy, National Institutes of Health, Sloan Foundation, Gordon and Betty Moore Foundation, W. M. Keck Foundation, Google, Amazon, the University of Utah, New York University, Microsoft Research, Yahoo! and IBM.
Fred Schneider, Cornell University
Fred B. Schneider is Samuel B. Eckert Professor of Computer Science at Cornell University and chair of the department. He joined Cornell’s faculty in Fall 1978, having completed a Ph.D. at Stony Brook University and a B.S. in Engineering at Cornell in 1975. Schneider’s research has focused on various aspects of trustworthy systems — systems that will perform as expected, despite failures and attacks. His early work concerned formal methods to aid in the design and implementation of concurrent and distributed systems that satisfy their specifications. He is author of two texts on that subject: On Concurrent Programming and (co-authored with D. Gries) A Logical Approach to Discrete Mathematics. He is also known for his research in theory and algorithms for building fault-tolerant distributed systems.
His paper on the “state machine approach” for managing replication received (in 2007) an SOSP “Hall of Fame” award for seminal research. More recently, his interests have turned to system security. His work characterizing what policies can be enforced with various classes of defenses is widely cited, and it is seen as advancing the nascent science base for security. He is also engaged in research concerning legal and economic measures for improving system trustworthiness.
Schneider was elected Fellow of the American Association for the Advancement of Science (1992), the Association of Computing Machinery (1995), and the Institute of Electrical and Electronics Engineers (2008). He was named Professor-at-Large at the University of Tromso (Norway) in 1996 and was awarded a Doctor of Science honoris causa by the University of Newcastle-upon-Tyne in 2003 for his work in computer dependability and security. He received the 2012 IEEE Emanuel R. Piore Award for “contributions to trustworthy computing through novel approaches to security, fault-tolerance and formal methods for concurrent and distributed systems”. The U.S. National Academy of Engineering elected Schneider to membership in 2011, and the Norges Tekniske Vitenskapsakademi (Norwegian Academy of Technological Sciences) named him a foreign member in 2010.
Schneider is a frequent consultant to industry, believing this to be an efficient method of technology transfer and a good way to learn about the real problems. He provides technical expertise in fault-tolerance and computer security to a variety of other firms, including Intel, Lincoln Laboratories, and Riskive. In addition, Schneider has testified about cybersecurity research at hearings of the US House of Representatives Armed Services Committee (subcommittee on Terrorism, Unconventional Threats, and Capabilities), as well as the Committee on Science and Technology (subcommittee on Technology and Innovation and subcommittee on Research and Science Education).
Xiaofan Wang, Shanghai Jiao Tong University
Dr. Xiaofan Wang received the B. Sc degree in mathematics from Suzhou University in 1986, the M. Sc degree in computational mathematics from Nanjing Normal University in 1991, and the Ph.D. degree from Southeast University in 1996. From Oct., 1996 to Dec., 2001, I had been worked at Nanjing University of Science & Technology, City University of Hong Kong and University of Bristol. I have been a Professor at Shanghai Jiao Tong University (SJTU) since 2002 and a Distinguished Professor of SJTU since 2008.
I received the 2002 National Science Foundation for Distinguished Young Scholars of P. R. China, the 2005 Guillemin-Cauer Best Transactions Paper Award from the IEEE Circuits and Systems Society, the 2008 First-Class Prize of Shanghai Natural Science Award, the 2008 Distinguished Professor of the Chaing Jiang Scholars Program, Ministry of Education, and the 2010 Peony Prize for Natural Science Researchers in Shanghai.
Sue Moon, KAIST
Sue Moon received her B.S. and M.S. from Seoul National University, Seoul, Korea, in 1988 and 1990, respectively, all in computer engineering. She received a Ph.D. degree in computer science from the University of Massachusetts at Amherst in 2000. From 1999 to 2003, she worked in the IPMON project at Sprint ATL in Burlingame, California. In August of 2003, she joined KAIST and now teaches in Daejeon, Korea. Her research interests are: online social networks and networking systems.
Alice Oh, KAIST
Alice Oh is an associate professor in the School of Computing at Korea Advanced Institute of Science and Technology. She heads the Users and Information Lab with the vision of developing machine learning models to better analyze and understand users and their needs for information.
Seungjin Choi, POSTECH
Seungjin Choi received B.S. and M.S. degrees in electrical engineering from Seoul National University, Korea, in 1987 and 1989, respectively, and a Ph.D. degree in electrical engineering from the University of Notre Dame, Indiana, in 1996. He was with the Laboratory for Artificial Brain Systems, RIKEN, Japan, in 1997 and was an Assistant Professor in the School of Electrical and Electronics Engineering, Chungbuk National University from 1997 to 2000. Since 2001, he has been a Professor of Computer Science at POSTECH, Korea. He also leads the Machine Learning Center supported by Korea Ministry of Science, ICT, and Future Planning. His primary research interests include probabilistic models and Bayesian inference, recently deep generative models.
Wenjun Zeng, Microsoft Research
Wenjun (Kevin) Zeng is a Principal Research Manager overseeing the Internet Media Group and the Media Computing Group at Microsoft Research Asia. He was with the Univ. of Missouri (MU) from 2003 to 2016, most recently as a Full Professor. He had worked for PacketVideo Corp., Sharp Labs of America, Bell Labs, and Panasonic Technology prior to joining MU. Wenjun has contributed significantly to the development of international standards (ISO MPEG, JPEG2000, and OMA). He received his B.E., M.S., and Ph.D. degrees from Tsinghua Univ., the Univ. of Notre Dame, and Princeton Univ., respectively. His current research interest includes mobile-cloud media computing, computer vision, social network/media analysis, multimedia communications, and content/network security.
He is a Fellow of the IEEE. He is an Associate Editor-in-Chief of IEEE Multimedia Magazine, and was an AE of IEEE Trans. on Circuits & Systems for Video Technology (TCSVT), IEEE Trans. on Info. Forensics & Security, and IEEE Trans. on Multimedia (TMM). He is/was on the Steering Committee of IEEE Trans. on Mobile Computing (current) and IEEE TMM (2009-2012). He served as the Steering Committee Chair of IEEE ICME in 2010 and 2011, and has served as the TPC Chair of several IEEE conferences (e.g., ChinaSIP’15, WIFS’13, ICME’09, CCNC’07). He will be a general co-Chair of ICME2018. He is currently guest editing an IEEE Communications Magazine Special Issue on Impact of Next-Generation Mobile Technologies on IoT-Cloud Convergence and a TCSVT Special Issue on Visual Computing in the Cloud – Mobile Computing, and was a Special Issue Guest Editor for the Proceedings of the IEEE, IEEE TMM, and ACM TOMCCAP.
Gunhee Kim, Seoul National University
Gunhee Kim is an assistant professor in the Department of Computer Science and Engineering of Seoul National University from 2015. He was a postdoctoral researcher at Disney Research for one and a half years. He received his PhD in 2013 under supervision of Eric P. Xing from Computer Science Department of Carnegie Mellon University. Prior to starting PhD study in 2009, he earned a master’s degree under supervision of Martial Hebert in Robotics Institute, CMU. His research interests are solving computer vision and web mining problems that emerge from big image data shared online, by developing scalable and effective machine learning and optimization techniques. He is a recipient of 2014 ACM SIGKDD doctoral dissertation award, and 2015 Naver New faculty award.
Shin'ichi Satoh, National Institute of Informatics (NII)
Shin’ichi Satoh received his BE degree in Electronics Engineering in 1987, his ME and PhD degrees in Information Engineering in 1989 and 1992 at the University of Tokyo. He joined National Center for Science Information Systems (NACSIS), Tokyo, in 1992. He is a full professor at National Institute of Informatics (NII), Tokyo, since 2004. He was a visiting scientist at the Robotics Institute, Carnegie Mellon University, from 1995 to 1997. His research interests include image processing, video content analysis and multimedia database. Currently he is leading the video processing project at NII.
Junsong Yuan, Nanyang Technological University
Junsong Yuan is currently an associate professor and program director of video analytics at School EEE, Nanyang Technological University (NTU), Singapore. He received Ph.D. from Northwestern University. His research interests include computer vision, video analytics, gesture and action analysis, large-scale visual search and mining, etc. He is Program Chair of IEEE Conf. on Visual Communications and Image Processing (VCIP’15), Organizing Co-Chair of Asian Conf. on Computer Vision (ACCV’14), and Area Chair of CVPR’17, ICPR’16, ACCV’14, WACV’14, ICME’14’15. He serves as guest editor of International Journal of Computer Vision (IJCV), and is currently associate editor of IEEE Trans. on Image Processing (T-IP), IEEE Trans. on Circuits and Systems for Video Technology (T-CSVT) and The Visual Computer journal (TVC). He received Nanyang Assistant Professorship from Nanyang Technological University, Outstanding EECS Ph.D. Thesis award from Northwestern University, Best Paper Award from IEEE Trans. on Multimedia, and Doctoral Spotlight Award from IEEE Conf. on Computer Vision and Pattern Recognition (CVPR’09).
Xing Xie, Microsoft Research
Dr. Xing Xie is currently a senior research manager in Microsoft Research Asia, and a guest Ph.D. advisor for the University of Science and Technology of China. He received his B.S. and Ph.D. degrees in Computer Science from the University of Science and Technology of China in 1996 and 2001, respectively. He joined Microsoft Research Asia in July 2001, working on data mining, social computing and ubiquitous computing. During the past years, he has published over 160 referred journal and conference papers, such as ACM Transactions on Intelligent Systems and Technology, ACM Transactions on the Web, ACM/Springer Multimedia Systems Journal, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Mobile Computing, IEEE Transactions on Multimedia, etc. He has more than 50 patents filed or granted. He has been invited to give keynote speeches at SocInfo 2015, Socialinformatics 2015, GbR 2015, W2GIS 2011, HotDB 2012, SRSM 2012, etc.
He currently serves on the editorial boards of ACM Transactions on Intelligent Systems and Technology (TIST), Springer GeoInformatica, Elsevier Pervasive and Mobile Computing, Journal of Location Based Services, and Communications of the China Computer Federation (CCCF). In recent years, he was involved in the program or organizing committees of over 70 conferences and workshops. Especially, he initiated the LBSN workshop series and served as program co-chair of ACM Ubicomp 2011, the 8th Chinese Pervasive Computing Conference (PCC 2012) and the 12th International Conference on Ubiquitous Intelligence and Computing (UIC 2015). In Oct. 2009, he founded the SIGSPATIAL China chapter which was the first regional chapter of ACM SIGSPATIAL. He is a member of the Steering Committee of the Pervasive and Ubiquitous Computing Conference Series. He is a senior member of ACM and the IEEE, and a distinguished member of China Computer Federation (CCF).
De-Nian Yang, Academia Sinica
De-Nian Yang received the BS and Ph.D. degrees from the Department of Electrical Engineering, National Taiwan University. His research interests include social networks and multimedia networking. He received the best paper awards (or nominates) from PAKDD, IEEE GLOBECOM, ACM CHI, and IEEE ICME. He also received Career Development Award and Junior Research Investigators Award in Academia Sinica, Outstanding Youth Electrical Engineer Award in Chinese Institute of Electrical Engineering, K. T. Li Distinguished Young Scholar Award in ACM Taipei/Taiwan Chapter, Excellent Junior Research Investigators in Ministry of Science and Technology and National Science Council, and Research Exploration Award in Pan Wen Yuan Foundation. He is now a senior member of IEEE and a member of ACM.
Tingshao Zhu, Chinese Academy of Sciences
Tingshao Zhu, PhD, earned his second Ph.D at the University of Alberta Canada in 2006. From 2008, he started working as a Professor at the Graduate University of Chinese Academy of Sciences(CAS), and a professor in the Institute of Psychology, CAS from 2012 in Beijing. Dr. Zhu has extensive experience on Data Mining and Machine Learning. His research on Mandarin Text-To-Speech, conducted at the Institute of Computing Technology, CAS, was the first attempt to acquire prosodic patterns using data mining, and several research groups in China are currently extending this research. His proposal of predicting personality/mental health states has defined a new direction for psychology research. The main foci of his current work are (1) user behavior modeling; (2) computational cyberpsychology and (3) data mining.
Hao Chen, Nankai University
Dr. Hao Chen is currently an associate professor at Department of Social Psychology of Nan Kai University in China. During the past several years, Prof. Chen has endeavored to integrate informatics technology into the research of psychological science, such as collecting massive data information about human cognition, emotion and behavior online, analyzing the psychological association and underlying mechanism at aggregate level, testing and revising the classical or cutting-edged psychological theories and hypotheses afresh with large-scale dataset. Prof. Chen has published over 40 academic papers concerning psychological or interdisciplinary topics, among which including online collective behavior and emotion, computational behavioral sciences, socioecological psychology, intimate relationships, and evolutionary psychology. Prof. Chen has received the Jung Tae-Gon Young Scholar Awards of the Asian Association of Social Psychology in 2009, nominated the National 100 Excellent Doctoral Dissertation Award in 2010, and won the Best Paper Award of the 2nd IEEE International Conference on Behavioral, Economic and Socio-Cultural Computing in 2015.
Wei Chen, Microsoft Research
Wei Chen (陈薇) is a researcher in Machine Learning Group, Microsoft Research Asia. Her current research interests include: distributed machine learning, deep learning theory, game-theoretic machine learning, mechanism design, and learning to rank. Before she joined Microsoft in July 2011, she obtained her Ph. D. in probability and mathematic statistics from Academy of Mathematics and System Science, Chinese Academy of Sciences.
Hwanjo Yu, POSTECH
Hwanjo Yu received his PhD in Computer Science at the University of Illinois at Urbana-Champaign at June 2004 under the supervision of Prof. Jiawei Han. From July 2004 to January 2008, he had been an assistant professor at the University of Iowa. He is now an associate professor at POSTECH (Pohang University of Science and Technology). He developed influential algorithms and systems in the areas of big data and machine learning, including (1) algorithms for classifying without negative data (PEBL,SVMC), (2) privacy-preserving SVM algorithms (PP-SVM), (3) SVM-JAVA: an educational java open source for SVM, (4) RefMed: the relevance feedback search engine for PubMed, and (5) TurboGraph: a fast parallel graph engine handling billion-scale graphs in a single PC. His methods and algorithms were published in prestigious journals and conferences including ACM KDD, AAAI, IJCAI, ACM SIGMOD, IEEE ICDE, IEEE ICDM, ACM CIKM, etc.
Wensheng Zhang, Chinese Academy of Sciences
Zhang Wensheng, research professor, Assistant Chief Engineer and doctoral advisor at the Institute of Automation, Chinese Academy of Sciences (CASIA). He also serves as Chair Professor and PhD supervisor at University of Chinese Academy of Sciences (UCAS), professor and PhD supervisor at University of Science and Technology of China (USTC), Nanjing University of Science and Technology (NJUST).
His recent fields of research include: artificial intelligence, Machine Learning, big data and data mining, embedded video image processing. He is the judge for China National Science & Technology Awards, member of the group of experts in Cloud Computing and Big Data sector of the National Science and Technology Major Project, member of National Defense Science Research team, Secretary of Human-computer Interface Committee at Chinese Association of Automation, and the member of Chinese Big Data Expert Committee. He has presided and completed over nine Key Projects and General Programs supported by National Natural Science Foundation, seven National High-tech R&D Program of China (863 Program), and two National Program on Key Basic Research Project (973 Program). He has published over 130 papers in journals and conferences, and is the inventor of over 20 patents.
Gang Hua, Microsoft Research
Gang Hua is a Senior Research Manager of the Visual Computing Group at Microsoft Research Asia. He was an Associate Professor of Computer Science in Stevens Institute of Technology between 2011 and 2015. He held an Academic Advisor position at IBM T. J. Watson Research Center between 2011 and 2014. He was a visiting researcher at Microsoft Research Asia in Summer 2013, and a Consulting Researcher at Microsoft Research in Summer 2012. He had also worked as full-time Researchers at IBM T. J. Watson Research Center, Nokia Research Center Hollywood, and Microsoft Live Labs Research. He received the Ph.D. degree in Electrical and Computer Engineering from Northwestern University in 2006.
His research in computer vision studies the interconnections and synergies among the visual data, the semantic and situated context, and the users in the expanded physical world, which can be categorized into three themes: human centered visual computing, big visual data analytics, and vision based cyber-physical systems. He is the author of more than 100 peer reviewed publications in prestigious international journals and conferences. His research was funded by NSF, NIH, ARO, ONR, Adobe Research, Google Research, Microsoft Research, and NEC Labs. He is the recipient of the 2015 IAPR Young Biometrics Investigator Award, and is elected as an IAPR Fellow in the 2016 class. To date, he holds 18 U.S. patents and has more than 10 U.S. patents pending. He is a Senior Member of the IEEE and a life member of the ACM.
Xiaogang Wang, The Chinese University of Hong Kong
Xiaogang Wang received his Bachelor degree in Electronic Engineering and Information Science from the Special Class of Gifted Young at the University of Science and Technology of China in 2001, M. Phil. degree in Information Engineering from the Chinese University of Hong Kong in 2004, and PhD degree in Computer Science from Massachusetts Institute of Technology in 2009. He is an associate professor in the Department of Electronic Engineering at the Chinese University of Hong Kong since August 2009. He received PAMI Young Research Award Honorable Mention in 2016, the Outstanding Young Researcher in Automatic Human Behaviour Analysis Award in 2011, Hong Kong RGC Early Career Award in 2012, and Young Researcher Award of the Chinese University of Hong Kong. He is the associate editor of the Image and Visual Computing Journal, Computer Vision and Image Understanding, IEEE Transactions on Circuit Systems and Video Technology. He was the area chair of ICCV 2011, ICCV 2015, ECCV 2014, ECCV 2016, ACCV 2014, and ACCV 2015. His research interests include computer vision, deep learning, crowd video surveillance, object detection, and face recognition.
Xilin Chen, Chinese Academy of Sciences
Dr. Xilin Chen is a professor with Institute of Computing Technology, Chinese Academy of Sciences (CAS), Beijing. He is a Fellow of IEEE and a Fellow of China Computer Federation (CCF). Dr. Xilin Chen is / was an associate editor of IEEE Transactions on Multimedia / Image Processing. He is now a leading editor of Journal of Computer Science of Technology, and the associate editor in chief of the Chinese Journal of Computer. He served as general chair of IEEE FG 2013, program chair of ACM ICMI 2010, tutorial chair of IEEE FG 2011, publicity chair of ACM ICMI 2015, workshop chair of ACM ICMI 2009, demo chair of ACM ICMI 2006, and local chair of IEEE ICIP 2017, ACM MM 2009 and ICME 2007. Dr. Xilin Chen’s research interests include Computer Vision, Pattern Recognition, Image Processing, and Multimodal Interface. He is a recipient of one China’s State Natural Science Award (2015) and four China’s State Scientific and Technological Progress Awards (2012, 2005, 2003, and 2000).
Sudipta Sinha, Microsoft Research
Sudipta Sinha is a researcher in the Adaptive Systems and Interaction group at Microsoft Research Redmond. He received his PhD in 2009 under the guidance of Dr. Marc Pollefeys from the University of North Carolina at Chapel Hill. His research interests lie broadly in computer vision and robotics. He has been working on correspondence estimation in images and video, structure from motion, stereo matching, dense 3d reconstruction, image-based rendering, visual place recognition and visual odometry. He was part of the team from UNC Chapel Hill that received the best demo award at CVPR 2007 for their real-time urban 3d reconstruction system and contributed towards the development of Microsoft Photosynth and Microsoft Hyperlapse Pro.
Yu Zheng, Microsoft Research
Dr. Yu Zheng is a research manager from Microsoft Research, passionate about using big data to tackle urban challenges. He currently serves as the Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology. He is also the founding Secretary of SIGKDD China Chapter and has served as chair on over 10 prestigious international conferences, e.g. as the program co-chair of ICDE 2014 (Industrial Track). Zheng received five best paper awards from ICDE’13 and ACM SIGSPATIAL10, etc. His book, titled Computing with Spatial Trajectories, has been used as a text book in universities world-widely and awarded the Top 10 Most Popular Computer Science Book authored by Chinese at Springer. In 2013, he was named one of the Top Innovators under 35 by MIT Technology Review (TR35) and featured by Time Magazine for his research on urban computing. Zheng is also a visiting Chair Professor at Shanghai Jiao Tong University and an Adjunct Professor at Hong Kong University of Science and Technology.
Hideyuki Tokuda, Keio University
Hideyuki Tokuda obtained his B.S. (1975), M.S. (1977) from Keio University and Ph.D. (Computer Science) (1983) from University of Waterloo, Canada, respectively. He is currently Director of Ubiquitous Computing and Communication Laboratory and a Professor of the Faculty of Environment and Information Studies, Keio University, Japan. In 1983, he joined School of Computer Science, Carnegie Mellon University and Senior Research Computer Scientist in 1991. Since 1990, he joined Keio University. He was Associate Professor (1990-1996), Executive Vice President (1997-2001), Dean of the Graduate School of Media and Governance (2001-2007), Dean of the Faculty of Environment and Information Studies (2007-2009), and Dean of the Graduate School of Media and Governance (2009-2015) in Keio.
After he completed Ph.D., he joined School of Computer Science, Carnegie Mellon University and worked on distributed real-time operating systems such as Real-Time Mach, the ARTS Kernel. In 1990, he came back to Keio University. His research and teaching interests include ubiquitous computing systems, operating systems, decentralized autonomous systems, sensor networks, IoT/CPS and smart cities. He has created many ubiquitous computing platforms such as Smart Space Lab., Smart Furniture, uPhoto, uTexture and uPlatea. Because of his research contribution, he was awarded Motorola Foundation Award (89), IBM Faculty Award (02), Ministry of Economy, Trade and Industry Award (04) and Ministry of Internal Affairs and Communication Award (05), KEIO-Gijyuku Award (06), IPSJ Achievement Award (2011), Information Security Cultural Award (15).
He is a member of Science Council of Japan, a former vice president of IPSJ (Information Processing Society of Japan), IPSJ Fellow, JSSST (Japan Society for Software Science and Technology) Fellow, JST Special appointment Fellow and a member of ACM, IEEE, IPSJ, IEICE and JSSST.
Minyi Guo, Shanghai Jiao Tong University
Minyi Guo is currently Zhiyuan Chair professor and chair of the Department of Computer Science and Engineering, Shanghai Jiao Tong University (SJTU), China. Before joined SJTU, Dr. Guo had been a professor of the school of computer science and engineering, University of Aizu, Japan. Dr. Guo received the national science fund for distinguished young scholars from NSFC in 2007, and was supported by “1000 recruitment program of China” in 2010. His present research interests include parallel/distributed computing, compiler optimizations, embedded systems, pervasive computing, and cloud computing. He has more than 300 publications in major journals and international conferences in these areas. He is now on the editorial board of IEEE Transactions on Parallel and Distributed Systems and Journal of Parallel and Distributed Computing. Dr. Guo is a senior member of IEEE.
Vincent S. Tseng, National Chiao Tung University
Vincent S. Tseng is currently a Distinguished Professor at Department of Computer Science in National Chiao Tung University and director of Center for Big Data Technologies and Innovations. He served as the chair for IEEE CIS Tainan Chapter during 2013-2015 and the president of Taiwanese Association for Artificial Intelligence during 2011-2012. He also acted as the director for Institute of Medical Informatics of National Cheng Kung University during 2008 and 2011. During February 2004 and July 2007, he had also served as the director for Informatics Center in National Cheng Kung University Hospital. Dr. Tseng received his Ph.D. degree with major in computer science from National Chiao Tung University, Taiwan, in 1997. After that, he joined Computer Science Division of University of California at Berkeley as a postdoctoral research fellow during 1998-1999. He has a wide variety of research interests covering data mining, big data, biomedical informatics, mobile and social networks. He has published more than 300 research papers in referred journals and conferences as well as 15 patents (held and filed). He has been on the editorial board of a number of top journals including IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Knowledge Discovery from Data, IEEE Journal of Biomedical and Health Informatics, etc. He has also served as chairs/program committee members for a number of premier international conferences related to data mining and intelligent computing, including KDD, ICDM, SDM, PAKDD, ICDE, CIKM, IJCAI, etc. In recent years, Dr. Tseng has also served on overseeing the architect on Big Data technologies and applications for the governmental and industrial units in Taiwan. Dr. Tseng has received a number of awards, including 2014 K. T. Li Breakthrough Award (only one recipient annually in Taiwan) and 2015 Outstanding Research Award by Ministry of Science and Technology Taiwan.
Wei-Ying Ma, Microsoft Research
Dr. Wei-Ying Ma is an Assistant Managing Director at Microsoft Research Asia where he oversees multiple research groups including Web Search and Mining, Natural Language Computing, Data Management and Analytics, and Internet Economics and Computational Advertising. He and his team of researchers have developed many key technologies that have been transferred to Microsoft’s Online Services Division including Bing Search Engine and Microsoft Advertising. He has published more than 250 papers at international conferences and journals. He is a Fellow of the IEEE and a Distinguished Scientist of the ACM. He currently serves on the editorial boards of ACM Transactions on Information System (TOIS) and ACM/Springer Multimedia Systems Journal. He is a member of International World Wide Web (WWW) Conferences Steering Committee. In recent years, he served as program co-chair of WWW 2008, program co-chair of Pacific Rim Conference on Multimedia (PCM) 2007, general co-chair of Asia Information Retrieval Symposium (AIRS) 2008, and the general co-chair of ACM SIGIR 2011.
Before joining Microsoft in 2001, Wei-Ying was with Hewlett-Packard Labs in Palo Alto, California where he worked in the fields of multimedia content analysis and adaptation. From 1994 to 1997, he was engaged in the Alexandria Digital Library project at the University of California, Santa Barbara. He received a bachelor of science in electrical engineering from the National Tsing Hua University in Taiwan in 1990. He earned a Master of Science degree and doctorate in electrical and computer engineering from the University of California at Santa Barbara in 1994 and 1997, respectively.
Exhibit 1: Comprehensible Video Search by Example
Contact: Gang Hua, Microsoft Research
We will demo our latest video search technology in the query by example setting. We not only return a ranked list of all videos in the corpus, but also return the key shots that are automatically identified by our algorithm to provide comprehensible evidence on why a video is ranked higher. Providing such capability would naturally lead to a more convenient way for engaging users in the loop for interactive video search.
Exhibit 2: Video to Language - Describing Videos with Natural Language
Contact: Tao Mei, Microsoft Research
This demo shows our recent work on video to language, including the translation of a video sequence to a textual description in the natural language form, as well as the automatic generation of human-level comments for a video.
Exhibit 3: Microsoft Conversation Hub
Contact: Ming Zhou, Microsoft Research
Microsoft Conversation Hub is a complete solution to build and deploy high-quality end-to-end conversation systems and services with minimal efforts. Specifically, it provides an SDK for you to build your conversation engine with only one simple click. Based on state-of-the-art intelligent chat-bot techniques from Microsoft Research Asia, the Microsoft Conversation Hub has already leveraged big data to build a General Conversation Engine (GCE) within the underlying system. Besides, the Microsoft Conversation Hub provides ready-to-use APIs that allow developers to use customized data to enhance the conversational bot capabilities, according to personalized requirements; while also being highly capable of analyzing and distilling the knowledge from customized data, which then provides appropriate responses during the conversation. In addition, to integrate into different platforms, Microsoft Conversation Hub provides tools to easily build REST APIs. With just a few steps of configurations, REST APIs can be used from anywhere on any platform within seconds. We also show a digital parrot, Polly, built on top of the Microsoft Conversation Hub.
Exhibit 4: Smart Attention
Contact: Mu Li, Microsoft Research
Smart Attention is a compact, flexible and efficient deep learning framework for natural language tasks, especially optimized for recurrent neural network and attention modeling both in terms of hardware utilization and memory footprints. Powered by .NET Core technology, Smart Attention can run on various platforms including Windows, Linux and Mac OS, and transparently uses CPU and GPU devices. Smart Attention also comes with a complete neural machine translation library enhanced with latest improvements from Microsoft Research, which can achieve best translation accuracy and training efficiency.
Exhibit 5: Mixed Reality Rendering for HoloLens
Contact: Xin Tong, Microsoft Research
We deliver a mixed reality rendering system with full surface reflectance effects on HoloLens. Our system delivers the realistic experience with the surface reflectance acquired from real object, rendered under real environment lighting surrounding the user. Using simple gestures and voice control, the user can easily navigate and observe the virtual object in different ways, as the object is presented in the real world.
Exhibit 6: Self-teaching Machine - AI that Teach Itself through a Dual-learning Game
Contact: Tao Qin, Microsoft Research
State-of-the-art machine translation (MT) systems are usually trained on aligned parallel corpora, which are limited in scale and costly to obtain in practice. Given that there exists almost unlimited monolingual data in the Web, in this work we study how to boost the performance of MT systems by leveraging monolingual data in two-language translation. Specifically, we formulate the translation system as a two-player communication game and learn the translation models through reinforcement learning. Player 1 only understands language A and sends a message in language A to Player 2 through a noisy channel, which is a translation model from language A to B. Player 2 only knows language B and sends her received message in language B back to Player 1 through another noisy channel, which is a translation model from language B to A. By checking whether the received message is consistent with her original one, Player 1 can assess the quality of the two channels (translation models) and improve the two channels accordingly. Similarly, Player 2 can send a message in language B to Player 1, go through a symmetric process, and improve the two translation models. This communication game can be played for multiple rounds until the obtained translation models get converged. Distinguishing features of this reinforcement approach include: (1) two dual translation models are trained within one framework; (2) translation models are improved purely from unlabeled data through reinforcement learning, without the need of aligned parallel corpora as supervision; (3) it opens a window for learning to translate from scratch without bilingual data.
Exhibit 7: CNTK+DMTK - Distributed Deep Learning Framework from Microsoft
Contact: Taifeng Wang, Microsoft Research
CNTK – Computational Network Toolkit is a unified deep-learning toolkit by Microsoft Research. This demo will show how to setup CNTK, how to use, configure and test it, and how to define your own networks. DMTK is another open source toolkit from Microsoft Research which focuses on distributed machine learning. In this demo we will also show what kinds of machine learning tools DMTK can provide and how external users can leverage such powerful tools. As both toolkits are from MSR, they focus on different domains. By merging and integrating them with each other, even powerful applications can be done based on them. It is our hope that the community will take advantage of CNTK+DMTK to share ideas more quickly through the exchange of open source working code.
Exhibit 8: Q&A Miner
Contact: Lei Ji, Microsoft Research
Q&A is an important knowledge data to enable many scenarios like auto question answering in bot. This Q&A miner provides a platform to: 1. Extract Q&A data automatically w/ human knowledge in loop. 2. Mine semantic tags like domain, entity, relation as well as intent and condition from Q&A data. Q&A extraction contains two parts: FAQ extraction from both web pages and enterprise documents such as Word, and Q&A extraction to extract from crowd sourcing data such as online forum. After we extract many Q&A pairs, Q&A Miner learns the semantic tags by using: NER, intent taxonomy mining and recognition, conditional knowledge mining as well as question linking techniques.
Exhibit 9: Ideal Couple - Predicting User Personality from Heterogeneous Information
Contact: Xing Xie, Microsoft Research
An incisive understanding of user personality is not only essential to many scientific disciplines, it instills a profound business impact on practical applications such as digital marketing, personalized recommendation, mental diagnosis, and human resources management. Previous studies have demonstrated that language usage in social media is effective in personality prediction. However, except for single language features, a less studied direction is how to leverage the heterogeneous information on social media to have a better understanding of user personality. In this demo, we show how to predict users’ personality traits by integrating heterogeneous information including; self-language usage, avatar, emoticon, and responsive patterns. In addition, we will find out the right star for users via careful consideration of the predicted personality.
Exhibit 10: Microsoft Academic - Research More, Search Less
Contact: Kuansan Wang and Rui Li, Microsoft Research
Microsoft Academic services includes a set of APIs and data that make it easier to build robust apps, and tap into rich, academic data. In addition, a new data structure and graph engine has been developed to facilitate the real-time intent recognition and knowledge serving. This new service puts a knowledge driven, semantic inference based search and recommendation framework front and center. One illustrating feature is semantic query suggestions that identify authors, topics, journals, conferences, etc., as you type and offer ways to refine your search based on the data in the underlying academic knowledge graph. Plus, you can use the set of productivity tools and services that make it easy to stay-up-to-date on the latest research papers, people, journals, conferences and news.
Exhibit 11: Project Malmo - A Platform for Fundamental AI Research
Contact: Evelyne Viegas, Katja Hofmann, David Bignell, Fernando Diaz and Alekh Agarwal, Microsoft Research
Project Malmo is an open source AI experimentation platform designed to support fundamental research in artificial intelligence. With the Project Malmo platform, Microsoft aims to provide an experimentation environment in which promising approaches can be systematically and easily compared, and that fosters collaboration between researchers while working on fundamental AI research challenges such as; integration of multi-model, high-dimensional sensory data and life-long learning. Project Malmo achieves flexibility by building on top of Minecraft, a popular computer game with millions of players. The game is particularly appealing due to its open ended nature, collaboration with other players, and creativity in game-play. In this demo, we show the capabilities of the Project Malmo platform, and the kind of research they can enable. These range from 3D navigation tasks to interactive scenarios where agents compete or collaborate to achieve a goal.
Exhibit 12: Hierarchical 3D Landmark Detection Based on Heterogeneously-Coupled Feature Extraction
Contact: Sangyoun Lee, Yonsei University
Many application technologies related to intelligent devices and wearable sensors have lately drawn a lot of attention from both the research community and the industry. Many of these technologies make use of hand and facial feature information, and the technical performances of these technologies are dependent on this information. Also, as the number of products equipped with these technologies rapidly increases, the performance of the core method becomes a crucial issue. However, it is a challenge to precisely detect the principal landmarks of fingers or face, as they flexibly deform with a large degree of freedom. Existing approaches mostly focus on depth feature extraction and the algorithm itself. Our approach, however, concentrates on depth-color-mutuality-based adaptive feature extraction and the hierarchical structure of the detection strategy. In other words, this research intends to develop a hierarchically organized structure of landmarks and a heterogeneously-coupled feature extraction method that builds a complementary correlation between depth and color features. A coarse-to-fine strategy will be adopted in order to construct a hierarchical landmark structure, and then features extracted from both depth and color information will have a co-operative effect on each other, functionally adapting to each condition of landmark.
Exhibit 13: Exploring User Experiences of Active Workstations
Contact: Uichin Lee, KAIST
We study user experiences of active workstations that incorporate physical activities such as walking and cycling to promote active office working environments. As a case study of active workstations, we developed a smart under-desk elliptical trainer that visualizes workout performance and supports context monitoring. We then conducted both controlled and in-the-wild experiments to systematically analyze user experiences. Our research results had significant implications for designing active workstations and interactive workplaces.
Exhibit 14: Development of Autonomous Drone Control Technique for Teleoperation
Contact: Jinbae Park, Yonsei University
We propose a novel autonomous drone with 2 robotic manipulators controlled by both automatically, and manually. Our system delivers an intuitive interface of controlling the drone by combining user posture recognition and a head-mounted display with drone’s movement. At the same time, vision-based object recognition and automatic grasping algorithm are implemented to help the teleoperation using the drone. Therefore, this autonomous drone can be deployed to numerous industrial applications such as object delivery, and manipulating remote facilities.
Exhibit 15: Human Activity Recognition Using Smart Shoes and Smart Bands
Contact: Gu-Min Jeone, Kookmin University
In this presentation, we will introduce two useful applications on human activity analysis using multiple wearable devices including smartphone, smart shoes and smart bands. These applications solve critical issues related to human living, i.e., calculating the number of walking steps, estimating walking distance, estimating energy expenditure and recognizing human activities, using a fusion of sensory data from the wearable devices. Analyzing acceleration, angular speed and pressure data at users’ shoes and wrists, we can recognize users’ activities and estimate important information related to users’ walking. Moreover, we also provide a useful tool in human data management which can store physical information, such as age, height, weight, etc., effectively record activity data and visualize these dynamic data.
Exhibit 16: Development of Real-Time Brain Signal Processing Algorithms based on Deep Learning for BCI-Racing
Contact: Seong-Whan Lee, Korea University
We propose a brain signal decoding method based on the deep learning technique to translate the users’ intention into the proper machine control commands. Our system recognizes the users’ voluntary imagination of body parts movements (i.e., MI; motor imagery) and transmits the appropriate commands to control the virtual avatar of ‘BrainRunners’ software (i.e., BCI racing; an obstacle race game using BCI). Various pilots including physically challenged people were possible to play the ‘BrainRunners’ by performing three classes of MI tasks in real time.
Exhibit 17: Processing and Optimizing Main Memory Spatial-Keyword Queries
Contact: Seung-won Hwang, Yonsei University
We show how richer human intelligence can be captured from a fusion of multimodal social data (AAAI 2016, ICDM 2016). We also discuss enabling technologies under the hood– a main memory spatial query optimization (VLDB 2016) and a cost-aware query parallelization technique (WSDM 2015).
Exhibit 18: Secure Automatic Unlock with a Trusted Device in Mobile System
Contact: Jong Kim, POSTECH
We propose a novel automatic lock system in order to handle drawbacks of existing automatic unlock methods (e.g., Google’s SmartLock). The system leverages various information from both a smartphone and a smartwatch, and it automatically locks the smartphone whenever unauthorized users use the smartphone when it’s behavior does not match with that of the smartwatch.
Exhibit 19: Weakly Supervised Video Highlight Detection with Triplet Deep Ranking
Contact: Hyeran Byun, Yonsei University
Highlight detection from videos has been widely studied due to the fast growth of video contents. However, most existing approaches to highlight detection, either handcraft feature-based or deep learning-based, heavily rely on human-curated training data, which is very expensive to obtain and thus hinders the scalability to both large datasets and unlabeled video categories. We observe that the largely available web images can be applied as a weak supervision for highlight detection. Motivated by this observation, we propose a novel triplet deep ranking approach to video highlight detection using web images as a weak supervision. Our approach can iteratively train two interdependent deep models (i.e., a triplet highlight model and a pairwise noise model) to deal with the noisy web images in a single framework. We train the two models with the relative preferences to generalize the capability regardless of the categories of training data.
Exhibit 20: Tree-guided MCMC Inference for Normalized Random Measure Mixture Models
Contact: Seungijn Choi, POSTECH
Normalized random measures (NRMs) provide a broad class of discrete random measures that are often used as priors for Bayesian nonparametric models. Dirichlet process is a well-known example of NRMs. Most of posterior inference methods for NRM mixture models rely on MCMC methods since they are easy to implement and their convergence is well studied. However, MCMC often suffers from slow convergence when the acceptance rate is low. Tree-based inference is an alternative deterministic posterior inference method, where Bayesian hierarchical clustering (BHC) or incremental Bayesian hierarchical clustering (IBHC) have been developed for DP or NRM mixture (NRMM) models, respectively. Although IBHC is a promising method for posterior inference for NRMM models due to its efficiency and applicability to online inference, its convergence is not guaranteed since it uses heuristics that simply selects the best solution after multiple trials are made. In this paper, we present a hybrid inference algorithm for NRMM models, which combines the merits of both MCMC and IBHC. Trees built by IBHC outlines partitions of data, which guides Metropolis-Hastings procedure to employ appropriate proposals. Inheriting the nature of MCMC, our tree-guided MCMC (tgMCMC) is guaranteed to converge, and enjoys the fast convergence thanks to the effective proposals guided by trees. Experiments on both synthetic and real-world datasets demonstrate the benefit of our method.
Exhibit 21: IReS: Integrated resource scheduling for intelligent clouds
Contact: Chuck Yoo, Korea University
Smart automobiles become IT device as they are evolving from the vehicle of transportation to platform of a variety of services for drivers. In particular, smart automobiles backed with cloud infrastructure can offer much rich services including self-driving and autonomous monitoring of running condition of automobile. To achieve high responsiveness and real-time data processing for smart automobile, clouds need to be intelligent – utilize cloud resources intelligently. For intelligent clouds, we have designed and implemented an intelligent resource scheduler (IReS) that integrates CPU and network resources. IReS has been tested in smart automobile environment equipped with digital cluster and shows its effectiveness.
Exhibit 22: A Novel Load Balancing Scheme for Multi-cloud using Data Relocation based on Multiple factors
Contact: Jooseok Song, Yonsei University
We have developed a new approach to improve network performance in social network services by relocating user’s data among multiple clouds using several factors: the amount of traffic, distance between user and cloud, and social relationship level between users on the social network services.
Exhibit 23: Correspondence Discovery between Image Regions and Phrases in Noisy Free-form Text
Contact: Gunhee Kim, Seoul National University
We address a variant of language grounding problems, to discover the reference alignment between the regions of images and the phrases in the associated natural language text. Unlike much of previous work, we especially deal with noisy text (e.g. user posts in social media) which is usually free-formed and contains irrelevant clutters. To this end, we introduce a novel vision-and-language dataset called Pinterest Entities. We design an attention-based deep learning network which aims to simultaneously extract key phrases from noisy text and localize the corresponding regions from image.
Exhibit 24: Identification of Cancer-driver Genes in Focal Genomic Alterations from Whole Genome Sequencing Data
Contact: Hyunju Lee, Gwangju Institute of Science and Technology
We developed a wavelet-based method to identify copy number alterations (CNAs) of genes, which may drive cancer initiation and development. To use high-resolution next generation sequencing (NGS) data, we employed a wavelet transformation, which removes noises in the NGS data and detects recurrent focal CNAs. When we applied the proposed method to glioblastoma multiforme, ovarian serous cystadenocarcinoma and lung adenocarcinoma, our approach achieved better performances than the existing algorithms using microarrays.
Exhibit 25: Student Characterization Based on Semantic Trajectory Analysis
Contact: Joon Heo, Yonsei University
Spatial big data (SBD) has been utilized in many fields and we propose SBD analytics to apply to education with semantic trajectory data based on ideal support of Songdo International Campus at Yonsei University. Higher education is under a pressure of disruptive innovation, so that colleges and universities strive to provide not only better education but also customized service to every single student, for a matter of survival in upcoming drastic wave. The entire research plan is to present a smart campus with SBD analytics for education, safety, health, and campus management, and this research is composed of four specific items: (1) to produce 3D mapping for test site; (2) to build semantic trajectory; (3) to collect pedagogical and other parameters of students through OSE center; (4) to find relationship among trajectory patterns and pedagogical characteristics. Successful completion of the research would set a milestone to use semantic trajectory to predict student performance and characteristics, even further to go to proactive student care system and student activity guiding system. It can eventually present better customized education services to participating students.
Exhibit 26: ConceptVector: Building User-Driven Concepts via Word Embedding
Contact: Jaegul Choo, Korea University
Organizing, classifying, and summarizing large document collections are important problems in today’s data-driven society. Central to many text analysis methods is the notion of a textual concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Textual concepts have potential for characterizing document collections, and can also be constructed once and then shared and reused over and over. Here we present a visual analytics system called ConceptVector that guides the user in building, refining, and sharing such concepts and then use them to classify documents. We validate ConceptVector via both a quantitative analysis and a user study to show that the happiness ranking of words generated with our methods are comparable to human-generated ones. Usage scenarios involving real-world datasets demonstrate the fine-grained level of analysis supported by ConceptVector.
Exhibit 27: Structured Output Prediction using Convolutional Neural Network for Human Pose Estimation
Contact: Kyoungmu Lee, Seoul National University
For last few years, CNN has been widely applied to various computer vision problems, proving its ability to extract powerful and informative features from raw images. However, there are only few attempts to exploit this powerful features from CNN in structured output prediction problems. In this project, we propose a framework for structured output prediction by combining convolutional neural networks (CNN) and Structured Support Vector Machine (SSVM). We applied the proposed framework to estimate human pose from a single image and showed the improvement performance.
Exhibit 28: Real Time Logo Detection using Shape, Color and Text Information
Contact: Chulhee Lee, Yonsei University
In this project, a new logo detection algorithm is developed based on the angle-distance map. This algorithm first identifies candidate logo regions based on color information. Then the algorithm computes the angle-distance map, which is invariant against scale and rotation. The proposed algorithm can detect logos with various rotations and sizes in natural images with low complexity. We implemented the algorithm as smartphone/tablet applications.
Exhibit 29: Imputing Uninteresting Items based on Pre-use Preferences for Effective Collaborative Filtering
Contact: Sang-Wook Kim, Hanyang University
We study how to improve the accuracy and running time of top-N recommendation with collaborative filtering (CF). Unlike existing works that use mostly rated items (which is only a small fraction in a rating matrix), we propose the notion of pre-use preferences of users toward a vast amount of unrated items. Using this novel notion, we effectively identify uninteresting items that were not rated yet but are likely to receive very low ratings from users, and impute them as zero. This simple-yet-novel zero-injection method applied to a set of carefully-chosen uninteresting items not only addresses the sparsity problem by enriching a rating matrix but also completely prevents uninteresting items from being recommended as top-N items, thereby improving accuracy greatly. As our proposed idea is method-agnostic, it can be easily applied to a wide variety of popular CF methods. Through comprehensive experiments using the Movielens dataset and MyMediaLite implementation, we successfully demonstrate that our solution consistently and universally improves the accuracies of popular CF methods (e.g., item-based CF, SVD-based CF, and SVD++) by two to five orders of magnitude on average. Furthermore, our approach reduces the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy.
Exhibit 30: Unified Depth Prediction and Intrinsic Image Decomposition from a Single Image via Joint Convolutional Neural Fields
Contact: Kwanghoon Sohn, Yonsei University
We present a method for jointly predicting a depth map and intrinsic images from single-image input. The two tasks are formulated in a synergistic manner through a joint conditional random field (CRF) that is solved using a novel convolutional neural network (CNN) architecture, called the joint convolutional neural field (JCNF) model. Tailored to our joint estimation problem, JCNF diff ers from previous CNNs in its sharing of convolutional activations and layers between networks for each task, its inference in the gradient domain where there exists greater correlation between depth and intrinsic images, and the incorporation of a gradient scale network that learns the con fidence of estimated gradients in order to eff ectively balance them in the solution. This approach is shown to surpass state-of-the-art methods both on single-image depth estimation and on intrinsic image decomposition.
Exhibit 31: TapSnoop - I Can Hear Your Touch-screen Taps: Leveraging Tap Sound to Infer Tapstrokes on Mobile Touch-screen Devices
Contact: Insik Shin, KAIST
In this work, we propose a novel tapstroke inference method, called TapSnoop. It accurately and robustly infers user typed sensitive information (e.g., passwords and PINs) by exploiting tapsound as a side channel of tapstrokes. First, for the accurate tapstroke inferencing, we develop tap detection and localization algorithms that leverage the acoustic characteristics of tapsound. Moreover, with the combined use of various sensors, we further improve the accuracy even in the presence of user’s mobility and ambient noise. We evaluate the performance of TapSnoop with an extensive evaluation collecting data from 10 real-world users in various scenarios. Our evaluation results show that TapSnoop achieves a high degree of accuracy (92.9% for a number keypad and 78.7% for a qwerty keypad). Furthermore, even with a moderate level of noise, it provides a similar degree of inference accuracy to the result obtained in a virtually noise-free environment.
Exhibit 32: Automatic Modeling and Verification of Software Vulnerabilities
Contact: Heejo Lee, Korea University
In this project, we focus on analyzing software vulnerability in an automated way. The proposed method is composed of two phases: vulnerability discovery phase which select potentially vulnerable code, and verification phase which verifies whether potentially vulnerable code is vulnerable or not. We apply a backward tracing method to reduce the number of paths to be explored. We test our method with Juliet Test Suite and show that our method can verify the vulnerability. Currently, we build a platform named IoTcube that analyzes vulnerabilities in software and networks, and the result of this study will be included in the IoTcube platform.
Exhibit 33: Paradigm Shift on Authentication - Uncertainty, Personalization
Contact: Steve(Sungdeok) Cha, Korea University
We propose a fresh departure from existing paradigms where image-based CAPTCHA mechanism is used to automatically generate appropriate image tags. This is possible if chosen (e.g., successful) responses are most likely to have been generated by humans who answered the CAPTCHA test to the best of their ability in order to create accounts with or log to subscribe portal services such as Microsoft Live.
Exhibit 34: Real-time traffic management system with online simulator and optimal traffic control algorithm based on smart-phone and vehicle detection system in the cloud platform
Contact: Hwasoo Yeo, KAIST
We deliver a real-time highway traffic prediction system with Microsoft AZURE. The system predicts 6 hours ahead of time with highway sensor data from Dedicated Short-Range Communication (DSRC), Vehicle Detection System (VDS), and Toll Collection System (TCS), over the range of South Korea highway network. Based upon Multi-level K-Nearest Neighbor (MK-NN) method, future speed, travel time and collision risk values at five-minute interval are provided. Also, various scenarios with respect to traffic accident and control strategies are included in the system using Modified Cell Transmission Model (MCTM). Furthermore, online simulation functions are incorporated into the system in order to help to find highway management strategies for the optimal system performance. By effectively distributing the computation power by AZURE platform, we can provide the real-time service by significantly reducing the computation time.
- MSR AI Engage: Learning and Intelligence
- Machine Learning: Theory Meets Application
- Machine Learning: System and Infrastructure
- Social Multimedia and Visual Q&A
- Deep Learning and Reinforcement Learning
- Learning for Vision and Multimedia
- Future Talent 2040