Tuesday, October 22, 2013
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Tutorial 1: Azure Platform for Cloud Computing | video
Chair: Gebi Liang, Microsoft Asia-Pacific R&D Group
Speakers: Michael Wang, Larry Zhang, and Wei Sun, Microsoft Asia-Pacific R&D Group
Tutorial 2: Kinect for Windows in Science Applications | video
Chair: Stewart Tansley, Microsoft Research Redmond
Speakers: Wei Liu, Microsoft Server and Tools; Cherry Wang, Microsoft Kinect for Windows China; Stewart Tansley, Microsoft Research Redmond
Tutorial 3: Data-Constrained Environmental Modeling: FetchClimate, Filzbach, and Distribution Modeller | video
Speaker: Drew Purves, Microsoft Research Cambridge
Wednesday, October 23, 2013
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What’s eScience? | video
Speaker: Kenji Takeda, Microsoft Research Cambridge
The nature of scientific discovery is continuously accelerating as the culture of research is profoundly affected by technology. From the development of instruments such as the microscope, to developments in mathematics and the tremendous advances in scientific computing, scientists are able to explore, understand, and describe the world around us in more ways than ever before. We are now in the midst of a Fourth Paradigm of data-intensive science in which instruments, sensors, and simulations are generating huge amounts of data that must be processed, analyzed, shared, and discussed to generate knowledge and opportunities to solve the major challenges of our global society. This new collaborative approach to research is the core of eScience. This talk will explore how eScience is transforming every discipline, and how cloud computing provides new opportunities for all researchers.
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Data Driven Applications | video
Speaker: Prakash Sundaresan, CTO, Microsoft Asia-Pacific R&D Group
Data has always been at the heart of applications. Over the last 30 years, application architectures have gone through some key paradigm shifts: from monolithic mainframe applications, to client-server architecture, to three-tier web architectures, and now converging on device+cloud as the dominant contemporary application architecture. The relationship of data to the application has also evolved through these paradigm shifts. A convergence of technology trends—proliferation of devices, cloud computing and big data techniques—is now putting data at the center of a new generation of data driven applications that are transforming fields as diverse as technology, commerce, government, and science. In this talk, we’ll examine these trends and show some examples of leading-edge data driven applications from a variety of fields from around the world. We’ll also look at some of the innovative tools and technologies that are enabling these new applications.
Emerging Trends in Online Education and Research | video
Speaker: Roy Zimmermann, director of Education and Scholarly Communication, Microsoft Research Connections
While online education and research services continue to emerge, it is still debatable whether they will bring revolutionary change or more evolutionary enhancements to existing platforms and services. Some services, like MOOCs, that just a short while ago were expected to be disruptive and to flip the entire education domain on its head, are now nesting themselves in more traditional education systems. The result is they are working more as background service providers than radical paradigm shifting mechanisms. Likewise, the emerging field of online research services is growing more crowded and niche driven with some entire services dedicated to bibliometrics, citation analysis, and alternative metrics for evaluating impact. This session will highlight a few education and research services as trend setters for the rest of their domains.
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eScience in the Medical Domain | video
Chair: Junichi Tsujii, Microsoft Research Asia
Speakers: Simon Mercer, Microsoft Research; Lai Maode, China Pharmaceutical University; Hoifung Poon, Microsoft Research; James Hogan, Queensland University of Technology
eScience research has the potential to change future scientific endeavors in the medical domain through the promotion of community-wide efforts to share data, software, and services, the advantages of big data and big text for the extraction of new knowledge, and the dissemination of knowledge in new ways, such as visualization and the collaborative construction of biological pathways. This session will explore a range of research projects seeking to provide elements of the infrastructure needed to realize this vision.
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Conducting Scientific Research in the Cloud | video
Chair: Dennis Gannon, Microsoft Research
Speakers: Honglin He, Chinese Academy of Sciences; Chunmiao Zheng and Yingying Yao, Peking University; Liang Lin, Sun Yat-Sen University
Cloud computing is based on harnessing the power of massive online data centers to support dynamically deployed user applications and data collections. Because clouds can scale on-demand from single core virtual machines to large numbers of servers, they are ideal for many eScience applications that require analysis of large data collections. Clouds are the engines of the fourth paradigm of science. This session describes three examples of new research projects in China that are using the Windows Azure cloud platform for eScience applications.
Interactive Visual Analytics for Scientific Discovery | video
Chair: Shixia Liu, Microsoft Research Asia
Speakers: Klaus Mueller, Stony Brook University; Huamin Qu, Hong Kong University of Science and Technology; Daniel Keim, University of Konstanz
Interactive visual analytics, the science of analytical reasoning facilitated by interactive visual interfaces, could help analysts explore and understand large-scale, complex data to support the analysts’ sense-making processes. It has been successfully applied to solving a wide range of real-world problems. Nevertheless, designing an effective visual analytics system remains challenging. This panel brings together recognized leaders in visual analytics to share their success stories and rich expertise in designing effective visual analytics systems for a variety of eScience applications. The panelists will describe the practical problems they explore, the visualization techniques they develop, and the interesting findings they discover. Finally, the panelists will provide guidance and suggestions for how scientists can effectively apply visual analytics techniques to facilitating scientific discovery and decision making.
Thursday, October 24, 2013
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IEEE eScience Keynote: From Genes to Stars | video
Speaker: Alex Szalay, Johns Hopkins University
Student Session: Learning Cloud Computing, Environmental Science, and You | video
Speaker: Wenming Ye, Microsoft Research
Studies revealing the impact of human activities on nature demonstrate the increasing importance of environmental science to help control pollution and reverse its effects. While social media and Web 2.0 companies have been enjoying the growth and benefits of cloud computing and Big Data, the general research and scientific computing communities are just starting to discover this powerful new computing paradigm. In this session, we will explore ways to use cloud computing and sensor networks to help solve interesting problems in environmental science today. Learning to use cloud computing and Big Data tools enables future scientists to better understand and solve the most challenging problems in protecting and preserving our environmental resources.
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From Smart Sensors to City OS (I) | video
Chair: Feng Zhao, Microsoft Research Asia
Speakers: Yuqi Bai, Tsinghua University; Hao-Hua Chu, National Taiwan University; Rajesh Krishna Balan, Singapre Management University; Huayi Wu, Wuhan University
With the proliferation of the IoT (Internet of Things) ecosystem, mobile sensors, and “soft” sensors, we are increasingly seeing opportunities to collect rich data sets that were previously unavailable. In turn, the market demands an end-to-end Big Sensor Data (BSD) platform for data collection, data storage, data-driven analysis, and feedback loop. The significance of BSD is not just in the data quantity, but also in the real-world insights that they reveal. In this session, we will discuss research challenges that are related to the four stages of BSD above.
First, how have the limits and boundaries of sensing and actuation been pushed by technological advances, such as the maturity of cloud-computing and the miniaturization of devices? Second, how should techniques and analytical tools from years of Big Data research impact the work on BSD, and streamline the BSD adaption? Third, from the pioneering work on modeling parts of our physical world with data, what do we need to model the missing pieces?
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Data-Constrained Environmental Modelling: FetchClimate, Filzbach, and Distribution Modeller | video
Chair: Drew Purves, Microsoft Research Cambridge
Speakers: Youngryel Ryu, Seoul National University; Peng Gong, Tsinghua University; Drew Purves, Microsoft Research Cambridge
There is an obvious and urgent need to build predictive models of important environmental phenomena. Such models need to describe how variation in different aspects of the environment—such as climate and soil—affect the phenomenon of interest, for example, primary productivity of plants, agricultural yield, or even land-use change. But to date, the building of such predictive models has been held back by a host of technical barriers, placing it outside the reach of many environmental scientists (and making it annoyingly difficult and slow for the rest!).
In this session, we’ll continue to walk you through three useful tools: FetchClimate, Filzbach, and Distribution Modeller, and will demonstrate how and why researchers are using them in real-world research.
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From Smart Sensors to City OS (II)
Chair: Xing Xie, Microsoft Research Asia
Speakers: Ryosuke Shibasaki, University of Tokyo; Lei Chen, Hong Kong University of Science and Technology; Guangzhong Sun, University of Science and Technology of China; Zhen Liu, Microsoft Asia-Pacific R&D Group
In recent years, real-world data reflecting city dynamics has become widely available, including users’ mobile phone signal, GPS traces of vehicles and people, ticketing data in public transportation systems, data from transportation sensor networks (camera and loop sensors), and environment sensor networks (temperature and air quality), as well as data from the Internet of Things. As a result, we are ready to carry out real urban computing activities that lead to better and smarter cities. By better sensing and understanding the city dynamics, we are more likely to design effective strategies and intelligent systems for improving life in urban areas. In this session, we are interested in discussing how we use the huge amount of sensor data to benefit different urban application scenarios, including, but not limited to, e-campus, environment, transportation, and smart building.
Friday, October 25, 2013
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Large-Scale Data Analysis for Biomedical and Social Sciences
Chair: Yi Ma, Microsoft Research Asia
Speakers: Kun Huang, Ohio State University; Takayuki Okatani, Tohoku University; Tom Cai, University of Sydney