Machine Learning Summit 2013

Speaker: Andrew Blake, Microsoft Research

The world of computer science and artificial intelligence can indulge in a bit of cautious celebration. There are several examples of machines that have the gift of sight, even if to a degree that is primitive on the scale of human or animal abilities. Machines can: navigate using vision; separate object from background; recognise a variety of objects, including the limbs of the human body. These abilities are great spin-offs in their own right, but are also part of an extended adventure in understanding the nature of intelligence.

One question is whether intelligent systems will turn out to depend more on theories and models, or simply on largely amorphous networks trained on data at ever greater scale? In vision systems this often boils down to the choice between two paradigms: analysis-by-synthesis versus empirical recognisers. Each approach has its strengths, and one can speculate about how deeply the two approaches may eventually be integrated.

Speaker: Judea Pearl, UCLA

The development of graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Moreover, problems that were thought to be purely statistical, are beginning to benefit from analysing their causal roots.

I will review concepts, principles and mathematical tools that were found useful in this transformation, and will demonstrate their applications in several data-intensive sciences.These include questions of confounding control, policy analysis, misspecification tests, mediation, heterogeneity, selection bias, missing data and the integration of findings from diverse studies.

The following topics will be emphasised:

• The 3-layer causal hierarchy association, intervention and counterfactuals.
• What mathematics can tell us about “external validity” or “generalising across domains”
• What causal analysis tells us about recovery from selection bias and missing data.

Speaker: Hermann Hauser, Amadeus Capital Partners

Data is accumulating at such a rate that there are no longer enough qualified humans to analyse it. Machine learning is needed to make data useful in many sectors which are drowning in it. Examples abound from healthcare, genomics, oil exploration, marketing etc. There have been 5 distinct waves of computing which all had the human at the centre of the industry. The internet of things will change this. Most communication will be between machines. To make them useful to us again they will need machine learning. This puts machine learning at the centre of the next 6th wave of computing.

Chair: Jeannette Wing, Microsoft ResearchPanellists: Eric Horvitz, Microsoft Research; Michel Cosnard, Iria; Iian Buchan, University of Manchester; Lionel Tarassenko, University of Oxford

Chair: John Bronskill, Microsoft ResearchSpeakers: John Winn, Microsoft Research

When faced with a machine learning problem, a common approach is to try to transform the data so that it can be solved using a standard tool, such as a classifier. Since each problem has unique characteristics, such a transformation will typically be imperfect and can lead to poor performance. Using model-based machine learning (MBML), we instead create a customised model which is exactly matched to the problem being solved. No transformation of the data is needed and the behaviour of the resulting machine learning algorithm can be finely tuned to the needs of the application. This talk will be a guided tour of model-based machine learning – what it is, what it can do and where it has already been put into practice.

Presentations

• Model-based Machine LearningTom Minka, Microsoft Research

Chair: Leon Bottou, Microsoft ResearchSpeakers: Francis Bach, Inria – Ecole Normale Superieure; Anatoli Juditsky, Joseph Fourier University; Alekh Agarwal, Microsoft Research

Classical statisticians used to rely on paper and pencil for both data collection and computation. Transferring the computation to electronic computers led to machine learning methods able to manage the scarcity of manually collected data relative to the capacity of the computationally feasible models. During the last decade, networked and pervasive computers have dramatically changed the data collection process. New machine learning algorithms and technologies are required as the volume of data grows faster than the available computational power.

Presentations

• A Reliable Effective Terascale Linear Learning SystemAlekh Agarwal, Microsoft Research; Olivier Chapelle, NYC Criteo; Miroslav Dudík and John Langford, Microsoft Research
• Conditional Gradient Algorithms for Regularized LearningAnatoli Juditsky, Joseph Fourier University; Zaid Harchaouiz, Inria Grenoble; Arkadi Nemirovskiy, ISyE, Georgia Tech–Atlanta
• Stochastic Gradient Methods for Machine LearningFrancis Bach, Inria – Ecole Normale Sup´erieure

Chair: Laurent Massoulie, Microsoft Research-Inria Joint CentreSpeakers: Avrim Blum, Carnegie Mellon University; Amos Storkey, University of Edinburgh; Peter Key, Microsoft Research

The growth in connectedness enabled by technology is creating situations where users or software agents are confronted by large or very large systems, for example, many on-line trading systems, on-line markets, sponsored search and online games. We would like to design large systems that both work well and are also efficient, but first we need to understand how users react to such systems. How should an agent reason about a large system and what types of behaviour of are “best” for the agent? Three promising approaches for tackling this are: mean-field games, which assume that an agent reacts to some average statistic of the other agents’ actions; regret minimisation based on local updating; and machine learning markets, where prediction markets offer a different model for aggregating information. This session seeks to explore and learn from the possible connections between these different approaches. The aim is to stimulate cross-discipline discussion and research, using these talks that take different approaches to act as catalysts for the discussion.

Presentations

• Learning and equilibria in Large SystemsPeter Key, Microsoft Research

Chair: Yann LeCun, New York UniversitySpeakers: Samy Bengio, Google; Fei-Fei Li, Stanford University; Padmanabhan Anandan, Microsoft Research India

The Internet is increasingly spawning challenging machine learning applications that could benefit from being formulated as supervised learning tasks with millions of categories or labels. For instance, photo and video annotation and Wikipedia article categorisation . The talks in the session will introduce, motivate and present state of the art techniques for learning with millions of categories and labels from the perspective of specific applications in computational advertising, computer vision and web search. The session should be of interest to researchers working on multi-class and multi-label classification, large scale learning, optimization and distributed machine learning and semi-supervised learning as well as domain experts in computational advertising and computer vision.

Presentations

• Laconic: Label Consistency for Image CategorizationSamy Bengio, Google

Chair: John Bronskill, Microsoft ResearchSpeaker: John Guiver, Microsoft Research

This tutorial will expand on the theme of model-based machine learning by looking at how to go about designing and building a model in practice. We’ll start off with data visualisation and analysis which, coupled with knowledge of how the data was collected, will give us a rich understanding of the data. This will inform the choices we make in constructing initial models. As we think more about the data and evaluate our initial models, we will get fresh ideas for how to extend and improve our model. The tutorial will concentrate on one particular data set which will allow the audience the time to understand the data and to interactively make suggestions for modelling choices.

Presentations

• Model-based Machine Learning tutorial with Infer.NetJohn Guiver, Microsoft Research

Chair: Pushmeet Kohli, Microsoft ResearchSpeakers: Foster Provost, New York University; Sharad Goel, Microsoft Research; Elad Yom-Tov, Microsoft Research

We are seeing an unprecedented increase in the time people spend interacting with machines and computational systems. These interactions take the form of time spent on social networking websites like Facebook, activities on search engines such as Bing or Google, or actions performed on mobile devices, and produce a lot of data about the user. This data can be levered through Machine Learning to not only understand the behaviour and preferences of users, but to also build intelligent interactive systems that are more effective and easy to use. In this session, we will hear about what are the key challenges and opportunities in this space.

Presentations

• Learning About Medicine by Applying Machine Learning to User Generated Content: The Case of AnorexiaElad Yom-Tov, Microsoft Research
• The Value of Social Data for Predicting BehaviorSharad Goel, Microsoft Research
• Mining (massiv) Consumer Behavior Data for Predictive MarketingFoster Provost, New York University

Chair: Silvia Chiappa, Microsoft ResearchSpeakers: David Page, University of Wisconsin-Madison; Antonio Criminisi, Microsoft Research; Bert Kappen, University of Radboud

This session will consist of three talks covering the use of advanced Machine Learning techniques to analyse clinical, genetic and medical image data.

The first talk will focus on the application of Machine Learning for predicting clinical events from electronic medical records. Specifically, it will discuss how to build predictive models of diseases and other health care events such as myocardial infarction, atrial fibrillation and adverse drug events from clinical and genetic data.

The second talk will focus on the use of Machine Learning in two different areas of genetics: the use of Bonaparte for DNA matching with application to forensics; and an approach to learning non-linear interactions in genome-wide association studies with application to psychiatric disease.

The third talk will focus on the use of Machine Learning in combination with medical expertise for automatic analysis of patients’ medical images with application to computer-aided diagnosis, personalised medicine and efficient data management.

Presentations

• Searching for Genetic Dark MatterK. Sharp, W. Wiegerinck, W. Burgers, K. Albers, A. AriasVasquez, B. Franke, and H.J. Kappen, Radboud University
• Bonaparte DVIBert Kappen, Wim Wiegerinck, and Willem Burgers, Radboud University
• Machine Learning for Medical Image AnalysisA. Criminisi and the InnerEye team at Microsoft Research
• Predicting Clinical Events from Electronic Health Records David Page, University of Wisconsin-Madison

Chair: Sebastian Nowozin, Microsoft ResearchSpeakers: Carsten Rother, Microsoft Research; Tomas Werner, Czech Technical University; Bill Freeman, Massachusetts Institute of Technology

Interpreting images and extracting high-level semantic information about natural scenes is commonly formulated as an inference problem in a graphical model. In all but the most trivial situations model specification, inference and parameter estimation are challenging and we have to turn to approximations that are computationally efficient. This session is about the application of graphical models to computer vision problems.

Presentations

• Motion Magnification – The Big World of Tiny Motions
• Bill Freeman, Massachusetts Institute of Technology
• Universality of the Local Marginal PolytopeDaniel Prusa and Tomas Werner, Center for Machine Perception Czech Technical University

Chair: Leon Bottou, Microsoft ResearchSpeakers: Isabelle Guyon, ChaLearn; Thomas Richardson, University of Washington; Leon Bottou, Microsoft Research

Machine learning systems in the real world are never without a purpose; they take actions, such as displaying a specific ad on a specific web page or actuating the controls of a self-driving vehicle. The true performance of the system depends on both the short- and long-term consequences of these actions. Unfortunately, discovering statistical correlations in the training data is not sufficient to predict the consequence of known actions in new contexts. Machine learning techniques must therefore integrate causal inference and causal discovery techniques. Many important works on multi-armed bandits and reinforcement learning represent first steps in this direction.

Presentations

• Cause-Effect Pairs ChallengeIsabelle Guyon, ChaLearn
• Counterfactual Reasoning and Learning SystemsLéon Bottou, Microsoft Research

Chair: Thore Graepel, Microsoft ResearchSpeakers: Andy Gordon, Microsoft Research; Vikash Mansinghka, Massachusetts Institute of Technology; Avi Pfeffer, Charles River Analytics; Christopher Re, University of Wisconsin-Madison

Probabilistic programming constitutes the most universal and ambitious paradigm for machine learning and inference to date. New probabilistic programming languages and frameworks such as Church, Infer.NET, IBAL and Markov Logic Networks have been proposed to empower the new probabilistic programmer.

In this session, we are planning to discuss the following questions: What progress has been made up to this point? Which probabilistic programming frameworks look most promising for which applications? What are the great research challenges that need to be addressed before probabilistic programming can go mainstream? What are the killer apps for probabilistic programming?

Presentations

• Making Probabilistic Programming Work in PracticeAvi Pfeffer, Charles River Analytics
• Model-Learner PatternAndy Gordon, Microsoft Research

Chair: Ronan Collobert, Idiap Research InstituteSpeakers: Steve Renals, University of Edinburgh; Hermann Ney, RWTH Aachen University; Alex Acero, Microsoft Research

Presentations

• Multi-domain Acoustic Modelling for Speech RecognitionSteve Renals, University of Edinburgh

Chair: Yoram Bachrach, Microsoft ResearchSpeakers: Emre Kiciman, Microsoft Research; Eyal Amir, University of Illinois at Urbana-Champaign; David Parkes, Harvard University

Data from interactions between humans and results gathered through crowdsourcing can be excellent sources of information. However, to gain knowledge from such information one must aggregate and analyse it, and the tasks must be structured so as to make the best use from the work. We will discuss techniques and approaches from artificial intelligence and machine learning which give an insight into such domains.

Presentations

• Identifying Bullies with a Computer GameEyal Amire, University of Illinois
• Investigating Bias and Incentives in Social MediaEmre Kıcıman, Microsoft Research
• Peer PredictionDavid Parks, Harvard University

Alex Acero is the Director of the Conversational Systems Research Center at Microsoft Research, Redmond, which conducts research on audio, speech and language processing. His team has contributed to Microsoft products such as Kinect. He has also managed research groups in machine translation, information retrieval, multimedia signal processing and computer vision. Dr Acero is a Fellow of IEEE and ISCA. He is President-Elect for the IEEE Signal Processing Society.

Eyal Amir is Co-Founder, CEO of startup company FasPark and Adjunct Associate Professor of Computer Science at the University of Illinois at Urbana-Champaign (UIUC). His research focuses on AI, specifically reasoning, learning, and decision making with logical and probabilistic knowledge. His company FasPark uses machine learning and probabilistic inference on graphs to speed up drivers looking for parking in metropolitan areas. He was a tenured Associate Professor 2009-2013 and Assistant Professor 2004-2009 at UIUC. Prior to that he was a postdoctoral researcher at UC Berkeley, received his Ph.D. in Computer Science from Stanford University, and received B.Sc. and M.Sc. degrees in mathematics and computer science from Bar-Ilan University, Israel in 1992 and 1994, respectively. He was a Captain at the Israel Defense Forces (1990-1995).  Eyal is a recipient of a number of awards for his academic research. Among those, he was chosen by IEEE as one of the “10 to watch in AI” (2006), and awarded the Arthur L. Samuel award for best Computer Science Ph.D. thesis (2001-2002) at Stanford University. He is a 2013 Fellow of the Startup Leadership Program.

A graduate of Ecole Polytechnique and an engineering graduate of X-Mines, Francis Bach has a M.Phil. in applied mathematics from the Ecole normale supérieure (ENS) in Cachan. He wrote an award-winning thesis on machine learning at the computer science department of the University of California (Berkeley). In 2005, he joined the Ecole des Mines, then in 2007 the ENS in Paris, from which he is seconded to Inria Paris – Rocquencourt and its Willow project team. In 2011, he created his own team, Sierra. He is a member of the peer-review committees of prestigious international journals. His numerous articles are considered references in the field.

Samy Bengio (PhD in computer science, University of Montreal, 1993) is a research scientist at Google since 2007. He works on large scale online learning, text, image and music ranking and annotation, and deep learning. He is action editor at JMLR, on the editorial board of the Machine Learning Journal, has organised several workshops (MLMI’2004-2006, NNSP’2002, several NIPS workshops), and was on the programme committee of NIPS, ICML, ECML, etc.

Andrew Blake is a Microsoft Distinguished Scientist and the Laboratory Director of Microsoft Research Cambridge, England. He joined Microsoft in 1999 as a Senior Researcher to found the Computer Vision group. In 2008 he became a Deputy Managing Director at the lab, before assuming his current position in 2010. Prior to joining Microsoft Andrew trained in mathematics and electrical engineering in Cambridge England, and studied for a doctorate in Artificial Intelligence in Edinburgh. He was an academic for 18 years, latterly on the faculty at Oxford University, where he was a pioneer in the development of the theory and algorithms that can make it possible for computers to behave as seeing machines.

Avrim Blum is Professor of Computer Science at Carnegie Mellon University. His main research interests are in Machine Learning Theory, Approximation Algorithms and Algorithmic Game Theory. He is also known for his work in AI Planning. He was a recipient of the Sloan Fellowship and NSF National Young Investigator Awards, the ICML/COLT 10-year best paper award, and is a Fellow of the ACM.

Léon received the Diplôme d’Ingénieur de l’École Polytechnique (X84) in 1987, the Magistère de Mathématiques Fondamentales et Appliquées et d’Informatique from École Normale Superieure in 1988, and a Ph.D. in Computer Science from Université de Paris-Sud in 1991. Léon joined AT&T Bell Laboratories in 1991 and went on to AT&T Labs Research and NEC Labs America. He joined the Science team of Microsoft adCenter in 2010 and Microsoft Research in 2012. Léon’s primary research interest is machine learning. Léon’s secondary research interest is data compression and coding. His best known contributions are his work on large scale learning and on the DjVu document compression technology.

John Bronskill is a Software Architect who leads the Infer.NET Engineering team at Microsoft Research Cambridge. Over the last 18 years, John has worked as a developer on a wide range of products at Microsoft including Windows, Office and Expression Studio.

Iain Buchan is Clinical Professor in Public Health Informatics and leads the Centre for Health Informatics at the University of Manchester. For Northern England he directs the MRC Health eResearch Centre. For the English National Health Service he is an honorary Public Health Physician and Chief Scientific Officer for North West e-Health. He holds qualifications in clinical medicine, pharmacology, public health and computational statistics, and runs a multi-disciplinary research team bridging health sciences, computer science, statistics, social science, and management science. Iain’s work centres on understanding and improving population health and healthcare through large scale participation in making sense of health data. The participation involves not only researchers and health professionals in modelling but also patients and communities in co-producing new forms of data capture and decision support for personal and public health. He brings together inductive and deductive approaches to modelling when forming and testing hypotheses, for example employing model-based machine learning methods alongside classical biostatistical methods in epidemiology. Iain has written widely used statistical software (www.statsdirect.com) and brings software engineers to work alongside scientists in Public Health research. He is championing Engineering in Public Health and the professionalisation of Health Informatics.

Silvia Chiappa received a Diploma in Mathematics from University of Bologna and a Ph.D. in Machine Learning from EPFL. She joined MSRC as a post-doctoral researcher in 2009, after a post-doc at the MPI for Biological Cybernetics in Tuebingen and a Marie-Curie fellowship at the Statistical Laboratory in Cambridge. Her research interests are based around probabilistic models, graphical models, approximate inference, time-series analysis and their application to real-world problems.

Antonio Criminisi joined Microsoft Research in Cambridge (Machine Learning andPerception group) as a Visiting Researcher in June 2000. In February 2001 he moved to the Interactive Visual Media Group in Redmond (WA, USA) as a Post-Doctorate Researcher. In 2002 he moved back to the Machine Learning and Perception Group in Cambridge as a Researcher. In September 2011 he became Senior Researcher and is now leading the medical image analysis team.

As president of Microsoft France, Alain Crozier is responsible for the sales and marketing activity of Microsoft in France, overseeing an organisation of 1,700 employees.

Since joining Microsoft in 1994, Crozier has held a variety of financial leadership roles in the Sales, Marketing and Services organisation including Finance & Administration Director of the France subsidiary, Regional Controller for the Americas and South Pacific region, and Worldwide Sales Controller before being promoted to SMSG CFO.

As corporate vice president and chief financial officer (CFO) of the Sales, Marketing and Services Group (SMSG) at Microsoft, Alain Crozier was responsible for the financial leadership of SMSG’s worldwide organisation of 46,000 employees located in over 100 countries, which included overseeing financial and strategic planning, reporting and analysis, controls and compliance, and financial and business performance management.

Prior to joining Microsoft, Crozier was finance, planning & analysis manager at Lesieur Alimentaire, a subsidiary of Eridania Beghin Say in Paris. He also held several audit and finance positions within Lesieur Alimentaire as well. Crozier started his career at Peat Marwick Consultants in Paris where he specialised in planning process design, functional reorganisations and process reengineering.

Crozier graduated from University Claude Bernard with a bachelor’s degree in mathematics and social sciences, and from the Institut Superieur de Gestion in Paris with a Business Administration degree.

Sharad Goel is currently a Senior Researcher at Microsoft Research — New York City. Sharad holds a PhD in Applied Mathematics and a Masters in Computer Science from Cornell, and a BS in Mathematics from the University of Chicago. Following postdoctoral positions in the math departments at Stanford and the University of Southern California, he spent five years in the Microeconomics and Social Systems group at Yahoo! Research.

Andy Gordon is a Principal Researcher at Microsoft Research Cambridge, where he co-manages the Programming Principles and Tools (PPT) group, and is a Professor at the University of Edinburgh. He has worked on a range of topics in concurrency, verification, and security, never straying too far from his roots in functional programming. His current passion is deriving machine learning algorithms from probabilistic functional programs.

Thore Graepel is a researcher at Microsoft Research Cambridge leading the Online Services and Advertising and Applied Games group. Before joining Microsoft Research, Thore was a postdoctoral researcher at the Department of Computer Science at Royal Holloway, University of London working on learning theory and machine learning algorithms. He has also worked as a postdoctoral researcher at the Institute of Computational Science (ICOS) which is part of the Department of Computer Science of the Swiss Federal Institute of Technology, Zürich (ETH) and has a doctorate (Dr. rer. nat) from the Department of Computer Science of the Technical University of Berlin.

John is a Research Software Development Engineer in the Machine Learning group at MSR Cambridge. He has been working for several years on the Infer.NET framework for Bayesian inference, both as a core developer and as a consultant and developer in product transfers and research projects that make use of the framework.

Isabelle Guyon, PhD, is an independent consultant, specialising in statistical data analysis, pattern recognition and machine learning. Her areas of expertise include computer vision and bioinformatics. Her recent interest is in applications of machine learning to the discovery of causal relationships. Prior to starting her consulting practice in 1996, Isabelle Guyon was a researcher at AT&T Bell Laboratories, where she pioneered applications of neural networks to pen computer interfaces and co-invented Support Vector Machines (SVM). She organised ten challenges in Machine Learning over the past few years supported by the EU network Pascal2, NSF, and DARPA, with prizes sponsored by Microsoft, Google, and Texas Instrument. She is president of Chalearn, a non-profit dedicated to organising challenges, vice-president of the Unipen foundation, adjunct professor at New-York University, action editor of the Journal of Machine Learning Research, and editor of the Challenges in Machine Learning book series of Microtome.

Serial Entrepreneur and Co-founder of Amadeus Capital Partners, Dr Hermann Hauser CBE has wide experience in developing and financing companies in the information technology sector. He co-founded a number of high-tech companies including Acorn Computers which spun out ARM, E-trade UK, Virata and Cambridge Network. Subsequently Hermann became vice president of research at Olivetti where he established a global network of research laboratories. Since leaving Olivetti, Hermann has founded over 20 technology companies. In 1997, he co-founded Amadeus Capital Partners.

Hermann is a Fellow of the Royal Society, the Institute of Physics, the Royal Academy of Engineering and an Honorary Fellow of King’s College, Cambridge. In 2001 he was awarded an Honorary CBE for ‘innovative service to the UK enterprise sector’. In 2004 he was made a member of the Government’s Council for Science and Technology and in 2009 took over the Chair of the East of England Stem Cell Network (EESCN) and became a member of the Government advisory panel for New Industry/New Jobs. He has honorary doctorates from the Universities of Loughborough, Bath and Anglia Ruskin.

Eric Horvitz is Distinguished Scientist and Deputy Managing Director at Microsoft Research. He has pursued research on machine learning, inference, and decision making, with contributions spanning theory and practice. His efforts have contributed to the fielding of applications and services in healthcare, information retrieval, human-computer interaction, and e-commerce. He has been elected Fellow of the American Academy of Arts and Sciences, the Association for the Advancement of Artificial Intelligence (AAAI), and the American Association for the Advancement of Science (AAAS). He has served as President of AAAI and is incoming chair of the AAAS Section on Information, Computing, and Communication. He has also served on the NSF CISE Directorate advisory board, council of Computing Community Consortium (CCC), Naval Research Advisory Committee (NRAC), and the DARPA ISAT Study Group. He received his PhD and MD degrees at Stanford University.

Anatoli Juditsky received his Candidate des Sciences (equivalent of the Ph.D.) degree in Applied Mathematics from Moscow institute of Physics and Technology 1989. He was a researcher at Inria, France (IRISA Rennes and Inria Grenoble) between 1990 and 1999, and has been a professor at University J. Fourier, Grenoble since 1999. His current research focus is on large-scale convex optimisation and its application in statistical learning.

Bert Kappen is professor of Machine Learning at the Radboud University Nijmegen, director of the foundation SNN and honorary faculty at the Gatsby Computational Neuroscience unit at UCL. He received his PhD in theoretical physics from Rockefeller University in New York. His research interests are at the interface of machine learning, control theory, statistical physics, computer science, computational biology and artificial intelligence.

Peter Key is a Principal Researcher at MSR Cambridge. He leads a Networks, Economics, and Algorithms team, which operates at the intersection of Computer Science, Economics and Game Theory. He is excited about the possibilities of applying research in this area to ad-auctions, markets and networks in Microsoft. His is a Fellow of the Association for Computing Machinery, the Institute of Electrical and Electronic Engineers, the Institution of Engineering and Technology, the Institute of Mathematics and its Applications, and the Institution of Engineering and Technology.

Emre Kiciman is a researcher in the Internet Services Research Center at Microsoft Research. His interests are in social data analysis, particularly as it relates to social search. His current projects focus on mining new kinds of information about the world from social media, and using social network data for search ranking. Emre’s previous research interests include JavaScript application monitoring and optimisation, and the Internet services architectures and operations reliability. Emre’s Ph.D. is in computer science from Stanford University.

Pushmeet Kohli is a research scientist in the Machine Learning and Perception group at Microsoft Research Cambridge, and an associate of the Psychometric Centre, University of Cambridge. Pushmeet’s research revolves around Intelligent Systems and Computational Sciences with a particular emphasis on algorithms and models for scene understanding, and the use of new sensors such as KINECT for the problems of human pose estimation and robotics. Pushmeet’s papers have appeared in SIGGRAPH, NIPS, ICCV, AAAI, CVPR, PAMI, IJCV, CVIU, ICML, AISTATS, AAMAS, UAI, ECCV, and ICVGIP and have won best paper awards in ECCV 2010, ISMAR 2011 and ICVGIP 2006, 2010. His PhD thesis, titled “Minimizing Dynamic and Higher Order Energy Functions using Graph Cuts”, was the winner of the British Machine Vision Association’s “Sullivan Doctoral Thesis Award”, and was a runner-up for the British Computer Society’s “Distinguished Dissertation Award”.

Dr. Peter Lee is the Corporate Vice President of Microsoft Research Redmond. In this role, he leads a computing research laboratory that advances the state of computing technology and collaborates with the company’s business groups to bring new technologies into products and services. Before joining Microsoft, he held key positions in both government and academia. His most recent position was at the Defense Advanced Research Projects Agency (DARPA) where he challenged conventional Department of Defense (DoD) approaches to computer science. One of the highlights of his work at DARPA was the DARPA Network Challenge, which mobilised millions of people worldwide in a hunt for red weather balloons – a unique experiment in social media and open innovation that fundamentally altered the thinking throughout the DoD on the power of social networks. Prior to joining DARPA, Lee was head of Carnegie Mellon University’s nationally top-ranked computer science department. He had also served as the university’s vice provost for research. Lee holds a Ph.D. in Computer and Communication Sciences from the University of Michigan at Ann Arbor and Bachelor’s degrees in Mathematics and Computer Sciences, also from the University of Michigan at Ann Arbor.

Prof. Fei-Fei Li is an associate professor in the Computer Science Department at Stanford University. Her main research interest is in vision, particularly high-level visual recognition. Fei-Fei graduated from Princeton University in 1999 with a physics degree. She received a PhD in electrical engineering from the California Institute of Technology in 2005. Fei-Fei is a recipient of a Microsoft Research New Faculty award, the Alfred Sloan Fellowship, a number of Google Research Awards, an NSF CAREER award, IEEE CVPR 2010 Best Paper Honorable Mention, and winner of a number of international visual computing competitions. (Fei-Fei publishes using the name L. Fei-Fei.)

Vikash Mansinghka is an Intelligence Initiative Fellow at MIT’s Computer Science and Artificial Intelligence Laboratory and Department of Brain & Cognitive Sciences, where he leads the Probabilistic Computing Project. Vikash received an SB in Mathematics, an SB in Computer Science, an MEng in Computer Science, and a PhD in Computation, all from MIT, holding graduate fellowships from the National Science Foundation and MIT’s Lincoln Laboratory. His PhD dissertation on natively probabilistic computation won the 2009 MIT George M. Sprowls award for best dissertation in computer science. He previously co-founded a venture-backed startup selling predictive database software that was ultimately acquired by Salesforce.com in 2012. He served on DARPA’s Information Science and Technology advisory board from 2010-2012.

Laurent Massoulie graduated from École Polytechnique in 1991, obtained his PhD thesis from Université Paris Sud in 1995 and his Habilitation thesis from Université Paris 7 in 2010. From 1995 to 1998 he was a researcher at France Telecom R&D, where he developed mathematical models of data transport over the Internet. Laurent joined Microsoft Research Cambridge in 1999, where he worked on distributed algorithms for Peer-to-Peer systems, and in particular on “epidemic” methods for information propagation. In 2006 he joined Technicolor, then known as Thomson. His research there dealt with design of Peer-to-Peer systems for media streaming and analysis of social networks for information dissemination. At Technicolor, he held positions of Director of the Paris Research Lab and Distinguished Scientist, and was elected as a Technicolor Fellow in 2010. In 2012, he joined Inria as Director of the Microsoft Research – Inria Joint Centre. He is the co-author of over 80 scientific papers and 16 patents and has co-authored the Best Paper Award-winning papers of IEEE INFOCOM’99, ACM SIGMETRICS’05 and ACM CONEXT’07 conferences.

Hermann Ney is a professor of computer science at RWTH Aachen University, Germany. He has worked on dynamic programming and discriminative training for speech recognition, on language modelling and on phrase-based approaches to machine translation. His work has resulted in more than 600 conference and journal papers (h-index 70, estimated using Google scholar).

Sebastian Nowozin is researcher in the Machine Learning and Perception group at Microsoft Research Cambridge. His research is at the intersection of machine learning and computer vision, in particular structured prediction models. He regularly serves as area chair or program committee member at major conferences (NIPS, ICML, CVPR, ICCV, ECCV) and is reviewer for all major computer vision and machine learning journals (TPAMI, IJCV, JMLR, MLJ).

David Page received his Ph.D. in computer science from the University of Illinois at Urbana-Champaign in 1993, where his thesis focused on relational machine learning. He became involved in biomedical applications while doing a post-doc with Stephen Muggleton in the Computing Laboratory at Oxford University. He is now a professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, where he also holds an appointment in the Computer Sciences Department. He served on the NIH study section on BioData Management and Analysis during its first 3 years as a standing study section and was on the steering committee of the International Warfarin Pharmacogenetics Consortium. He directs the Informatics Shared Service for Wisconsin’s Carbone Comprehensive Cancer Center and is on the scientific advisory board for OMOP, a joint PhARMA, FDA and FNIH initiative on identifying adverse drug events. David is a member of the Genome Center of Wisconsin, a co-director of the CIBM training program in biomedical informatics, and is UW-Madison’s scientific lead in the Wisconsin Genomics Initiative. David’s algorithm developments with colleagues include the SAYU and LUCID algorithms for change of view in statistical relational learning, skewing for learning correlation immune functions, and structure learning in continuous-time Bayesian networks via functional gradient boosting. David has experience applying machine learning to various biomedical data types including electronic health records, SNP genotypes, gene expression from next generation sequencing and micorrays, mass spectrometry proteomics, and high-throughput assays for ligand-protein binding. He currently holds NIH grants on machine learning for adverse drug events and secure sharing of clinical and genomic data.

David C. Parkes is Harvard College Professor and George F. Colony Professor of Computer Science at Harvard University, where he leads research at the interface between economics and computer science, with a focus on electronic commerce, AI and machine learning. Parkes received his Ph.D. in Computer and Information Science from U. Penn in 2001, and an M. Eng. in Engineering and Computing Science from Oxford University in 1995. Parkes has served as Program Chair of ACM EC’07 and AAMAS’08, General Chair of ACM EC’10, and currently serves as the Chair of ACM SIGecom, Editor of Games and Economic Behavior, and on the editorial boards of Journal of Autonomous Agents and Multi-agent Systems, ACM TEAC and INFORMS Journal of Computing.

Judea Pearl is a Professor of Computer Science and Statistics at UCLA. He is a graduate of the Technion, Israel, and joined the faculty of UCLA in 1970, where he currently directs the Cognitive Systems Laboratory and conducts research in artificial intelligence, causal inference and philosophy of science.

Pearl has authored several hundred research papers and three books: Heuristics (1984), Probabilistic Reasoning (1988), and Causality (2000;2009), He is a member of the National Academy of Engineering, the American Academy of Arts and Science, and a Fellow of the IEEE, AAAI and the Cognitive Science Society.

Pearl received the 2008 Benjamin Franklin Medal for Computer and Cognitive Science and the 2011 David Rumelhart Prize from the Cognitive Science Society. In 2012, he received the Technion’s Harvey Prize and the ACM A.M. Turing Award.

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Dr. Avi Pfeffer is a leading researcher on a variety of computational intelligence techniques including probabilistic reasoning, machine learning, and computational game theory. Dr. Pfeffer is one of the pioneers of the field of probabilistic programming, which enables the development of probabilistic models using the full power of programming languages. Most recently, he has developed the Figaro probabilistic programming language, which makes it easy to construct and manipulate probabilistic programs in a host language.

Foster Provost is Professor and NEC Faculty Fellow at the NYU Stern School of Business, and co-author of the book Data Science for Business. He previously was Editor-in-Chief of the journal Machine Learning, Program Chair of the ACM KDD conference, and his research has been the basis for the founding of several companies. Professor Provost’s work has won (among others) IBM Faculty Awards, a President’s Award at NYNEX Science and Technology, Best Paper awards at KDD, and the 2009 INFORMS Design Science Award for Social Network-based Marketing Systems.

Chief Research Officer, Richard (Rick) F. Rashid oversees worldwide operations for Microsoft Research, an organisation encompassing more than 850 researchers across eleven labs worldwide. Under Rashid’s leadership, Microsoft Research conducts both basic and applied research across disciplines that include algorithms and theory; human-computer interaction; machine learning; multimedia and graphics; search; security; social computing; and systems, architecture, mobility and networking. His team collaborates with the world’s foremost researchers in academia, industry and government on initiatives to expand the state of the art across the breadth of computing and to help ensure the future of Microsoft’s products.

After joining Microsoft in September 1991, Rashid served as director and vice president of the Microsoft Research division and was promoted to his current role in 2000. In his earlier roles, Rashid led research efforts on operating systems, networking and multiprocessors, and authored patents in such areas as data compression, networking and operating systems. He managed projects that catalysed the development of Microsoft’s interactive TV system and also directed Microsoft’s first e-commerce group. Rashid was the driving force behind the creation of the team that later developed into Microsoft’s Digital Media Division.

Before joining Microsoft, Rashid was professor of computer science at Carnegie Mellon University (CMU). As a faculty member, he directed the design and implementation of several influential network operating systems and published extensively about computer vision, operating systems, network protocols and communications security. During his tenure, Rashid developed the Mach multiprocessor operating system, which has been influential in the design of modern operating systems and remains at the core of several commercial systems.

Rashid’s research interests have focused on artificial intelligence, operating systems, networking and multiprocessors. He has participated in the design and implementation of the University of Rochester’s Rochester Intelligent Gateway operating system, the Rochester Virtual Terminal Management System, the CMU Distributed Sensor Network Testbed, and CMU’s SPICE distributed personal computing environment. He also co-developed of one of the earliest networked computer games, “Alto Trek,” during the mid-1970s.

Christopher (Chris) Re is an assistant professor in the department of computer sciences at the University of Wisconsin-Madison. The goal of his work is to enable users and developers to build applications that more deeply understand and exploit data. Chris received his PhD from the University of Washington in Seattle under the supervision of Dan Suciu. For his PhD work in probabilistic data management, Chris received the SIGMOD 2010 Jim Gray Dissertation Award. Chris’s papers have received four best-paper or best-of-conference citations, including best paper in PODS 2012, best-of-conference in PODS 2010 twice, and one best-of-conference in ICDE 2009). Chris received an NSF CAREER Award in 2011 and an Alfred P. Sloan fellowship in 2013.

Steve Renals is Professor of Speech Technology at the University of Edinburgh. He has research interests in speech and language technology, with over 150 publications in the area, with recent work on neural network acoustic models, cross-lingual speech recognition, and meeting recognition.

Thomas S. Richardson is a Professor of Statistics at the University of Washington, Seattle, Washington, USA. He is also the Director of the Center for Statistics and the Social Sciences at the University of Washington. His research interests include machine learning, multivariate statistics, graphical models, and causal inference. Most recently he has developed parametrisations and fitting algorithms for graphical models with both directed and bi-directed edges (); these models are designed to represent causal systems in which unmeasured ‘confounding’ variables may be present.

Professor Richardson is a Fellow of the Center for Advanced Studies in the Behavioral Sciences at Stanford University. In 2009 he received the UAI Best Paper Award; he also received the Outstanding Student Paper Award at UAI in 2004 (as co-author) and in 1996 (as author).

Carsten Rother has a Diploma degree from the University of Karlsruhe, Germany and a PhD from the Royal Institute of Technology Stockholm, Sweden. From 2003-04, he was a PostDoc at Microsoft Research Cambridge (MSRC), and since then has been a permanent researcher at MSRC. His research interests are in the field of “Markov Random Field Models for Computer Vision”, “low-level vision, such as segmentation and stereo”, and “Vision for Graphics”. He has written more than 100 articles and has an h-index of 35 with over 7000 citations. He won five “best paper (honorable) mention awards” and received the Olympus Prize by the German Society of pattern recognition (DAGM), which is the highest award for young scientists in his field. He edited a book on “Markov Random Fields for Vision and Image Processing, MIT Press 2011” and is the associated editor for TPAMI, and has been area chair and reviewer for many major conferences in the field.

Amos Storkey is a Reader in the School of Informatics, University of Edinburgh. His research focus is on Machine Learning Markets, including methods for interpreting Bayesian belief aggregation and model building via information markets. He also has extensive experience of developing machine learning methods for a diverse set of domains, especially spatial, temporal or image settings (astronomical images and databases, MRI, diffusion tensor imaging, super-resolution, image structure decomposition, functional MRI, handwriting generation and genetic epidemiology).

Manik Varma has a bachelor’s degree in Physics from St. Stephen’s College, University of Delhi in 1997 and another one in Computation from the University of Oxford in 2000 on a Rhodes Scholarship. He had a scholarship at Oxford University Scholarship and obtained a DPhil in Engineering in 2004. Before joining Microsoft Research, he was a Post-Doctoral Fellow at MSRI Berkeley. He has been an Adjunct Professor at the Indian Institute of Technology (IIT) Delhi in the Computer Science and Engineering Department since 2009 and jointly in the School of Information Technology since 2011. His research interests lie in the areas of machine learning, computational advertising and computer vision.

Tomas Werner is a researcher at the Center for Machine Perception, Czech Technical University, Prague, where he also obtained his PhD on multiple view geometry in computer vision. In 2000, he spent 1.5 years in the Visual Geometry Group at the Oxford University, UK, and then returned to Prague. Since then, his main interest has been machine learning, in particular, algorithms for inference in graphical models and how they relate to algorithms in constraint programming.

Jeannette M. Wing is the vice president, head of Microsoft Research International, and assumes responsibility for Microsoft Research’s research laboratories in Bangalore, India; Beijing, China; and Cambridge, UK.

Wing has held key positions in both academia and government, most recently at Carnegie Mellon University and the National Science Foundation (NSF).

John Winn is a Senior Researcher in the Machine Learning group at MSR Cambridge. Amongst other things, John has been working on Infer.NET for the last nine years. He is excited about making machine learning easier to use and available to a wider audience. His research interests include machine vision, computational biology and the semantic web.

Elad Yom-Tov is a Senior Researcher at Microsoft Research. Before joining Microsoft he was with Yahoo Research, IBM Research, and Rafael. Dr Yom-Tov studied at Tel-Aviv University and the Technion, Israel. His primary research interests are large-scale Machine Learning, Information Retrieval, and Social Analysis. His work has flown at four times the speed of sound, enabled people to communicate with computers using their brain-waves, and analysed cellphone records from a significant portion of the worlds’ population.