2015年“二十一世纪的计算”学术研讨会

Lorenzo Alvisi holds an Endowed Professorship in Computer Science at the University of Texas at Austin, where he co-leads the Laboratory for Advanced Systems Research (LASR), and, since 2011, a Visiting Chair Professor at Shanghai Jiao Tong University. He received a Ph.D. in Computer Science from Cornell University, which he joined after earning a Laurea degree Summa cum Laude in Physics from the University of Bologna, Italy. His research interests are in the theory and practice of distributed computing, with a particular focus on dependability. He is a Fellow of the ACM, an Alfred P. Sloan Foundation Fellow, and the recipient of a Humboldt Research Award, an NSF Career Award, and several teaching awards, including the UT System Regents' Outstanding Teaching Award. He serves on the editorial boards of ACM TOCS and Springer’s Distributed Computing.
In addition to distributed computing, he is passionate about western classical music and red Italian motorcycles.

The Pit and the Pendulum

Since the elegant foundations of transaction processing were established in the mid 70’s with the notion of serializability and the codification of the ACID (Atomicity, Consistency, Isolation, Durability) paradigm, performance has not been considered one of ACID’s strong suits, especially for distributed data stores. Indeed, the NoSQL/BASE movement of the last decade was born out of frustration with the limited scalability of traditional ACID solutions, only to become itself a source of frustration once the challenges of programming applications in this new paradigm began to sink in. But how fundamental is the dichotomy between performance and ease of programming?

In this talk, I will share the insights I have gained in trying to unlock the performance potential of the ACID transactional paradigm without sacrificing the generality and ease of programming that define it.

An IEEE Fellow and a Distinguished Scientist of Microsoft, Dr. Hon is an internationally recognized expert in speech technology. He serves on the editorial board of the international journal Communications of the ACM. 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 many universities all over the world. Dr. Hon holds three dozen patents in several technical areas.
Dr. Hon has been with Microsoft since 1995. He joined Microsoft Research Asia in 2004 as a Deputy Managing Director, and was promoted as Managing Director in 2007. In 2014, Dr. Hon was appointed as Chairman of Microsoft Asia-Pacific R&D Group. In addition, he founded and managed the Microsoft Search Technology Center (STC) from 2005 to 2007 and led development of the Microsoft internet search product (Bing) in Asia Pacific.
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 all architectural and technical aspects of the award winning Microsoft® Speech Server product, Natural User Interface Platform and Microsoft Assistance Platform, he is 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 Computer, where he led research and development for Apple’s Chinese Dictation Kit.
Dr. Hon received a PhD in Computer Science from Carnegie Mellon University and a B.S. in Electrical Engineering from National Taiwan University.

Age of AI: Artificial Intelligence, Agglomerative Intelligence, Adaptive Intelligence, and Ambient Intelligence

The building of a computer that can intelligently carry out tasks that require “human level intelligence” has been a goal for computer scientists since the 1950’s. Since that time, tasks that were considered to exhibit human level intelligence such as understanding natural language, carrying out conversational speech, playing master level chess, and interpreting images have all seen a great deal of improvements and many have seen real world applications. In this talk, I will provide an overview of the progress of artificial intelligence, highlight some recent milestone results in image recognition and natural language understanding, and predict the progress of AI in research and application over the next 5-10 years.

Thorsten Joachims is a Professor in the Department of Computer Science and the Department of Information Science at Cornell University. His research interests center on a synthesis of theory and system building in machine learning, with applications in information access, language technology, and recommendation. His past research focused on support vector machines, text classification, structured output prediction, convex optimization, learning to rank, learning with preferences, and learning from implicit feedback. In 2001, he finished his dissertation advised by Prof. Katharina Morik at the University of Dortmund. From 1994 to 1996 he was a visiting scholar with Prof. Tom Mitchell at Carnegie Mellon University. He is an ACM Fellow, AAAI Fellow, and Humboldt Fellow.

Learning from Rational(*) Behavior

Machine Learning is increasingly becoming a technology that directly interacts with human users. In fact, much of the Big Data we collect today are the decisions that people make when they use the systems we built. This is already evident in search engines, recommender systems, and electronic commerce, while other applications are likely to follow in the near future (e.g., autonomous robots, smart homes, games). In this talk, I argue that learning from human interactions requires learning algorithms that explicitly account for human behavior and motivation. Towards this goal, the talk explores how integrating microeconomic models of human behavior into the learning process leads to new learning models that no longer misrepresent the user as a “labeling subroutine”. This motivates an interesting area for theoretical, algorithmic, and applied machine learning research with connections to rational choice theory, econometrics, and behavioral economics.

(*) Restrictions apply. Some modeling required.

Lamport is best known for his seminal work in distributed systems and as the initial developer of the document preparation system LaTeX. Leslie Lamport was the winner of the 2013 Turing Award for imposing clear, well-defined coherence on the seemingly chaotic behavior of distributed computing systems, in which several autonomous computers communicate with each other by passing messages. He devised important algorithms and developed formal modeling and verification protocols that improve the quality of real distributed systems. These contributions have resulted in improved correctness, performance, and reliability of computer systems. Lamport received the 2013 ACM A.M. Turing Award for “fundamental contributions to the theory and practice of distributed and concurrent systems, notably the invention of concepts such as causality and logical clocks, safety and liveness, replicated state machines, and sequential consistency” in 2014. He was elected to Fellow of ACM (2014). In 2011, he was elected to the United States National Academy of Sciences. He was named ACM Fellow 2014 for fundamental contributions to the theory and practice of distributed and concurrent systems.

A Mathematical View of Computer Systems
Mathematics provides what I believe to be the simplest and most powerful way to describe computer systems.

Dr. Peter Lee, Corporate Vice President, Microsoft Research, is responsible for Microsoft Research New Experiences and Technologies, or MSR NExT, an organization of world-class researchers, engineers, and designers devoted to creating potentially disruptive technologies for Microsoft and the world. While NExT will continue to advance the field of computing research and produce work with significant scholarly impact, its priority is developing technologies that benefit Microsoft and the world more broadly.
In this role, Lee oversees Microsoft Research Asia, Microsoft Research Technologies, FUSE Labs, and Microsoft Research Special Projects, along with several incubation project teams.
Prior to joining Microsoft, Lee has held key positions in both government and academia. His most recent position was at the Defense Advanced Research Projects Agency (DARPA), where he founded and directed a major technology office that supported research in computing and related areas in the social and physical sciences. One of the highlights of his work at DARPA was the DARPA Network Challenge, which mobilized 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 Department of Defense on the power of social networks.

Before DARPA, Lee served as head of Carnegie Mellon University’s nationally top-ranked computer science department. He also served as the university’s vice provost for research. At CMU, he carried out research in software reliability, program analysis, security, and language design. He is well-known for his co-development of proof-carrying code techniques for enhanced software security, and has tackled problems as diverse as programming for large-scale modular robotics systems and shape analysis for C programs.
Lee is a Fellow of the Association for Computing Machinery and serves the research community at the national level, including policy contributions to the President’s Council of Advisors on Science and Technology and membership on both the National Research Council’s Computer Science and Telecommunications Board and the Advisory Council of the Computer and Information Science and Engineering Directorate of the National Science Foundation. He was the former chair of the Computing Research Association and has testified before both the US House Science and Technology Committee and the US Senate Commerce Committee.
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.

The New Industrial Revolution: Computing in Pole Position

We are living in a world that is witnessing an incredible pace of innovation, where computing research is playing the central role. Ambitions are not just high from a technical perspective, but also from a business impact perspective. This talk will outline the excitement in the industry that surrounds these eventful times, and the exhilarating journey that researchers are pursuing at Microsoft.

Dr. Stonebraker has been a pioneer of data base research and technology for more than a quarter of a century. He was the main architect of the INGRES relational DBMS, and the object-relational DBMS, POSTGRES. These prototypes were developed at the University of California at Berkeley where Stonebraker was a Professor of Computer Science for twenty five years. More recently at M.I.T. he was a co-architect of the Aurora/Borealis stream processing engine, the C-Store column-oriented DBMS, the H-Store transaction processing engine, the SciDB array DBMS, and the Data Tamer data curation system. Presently he serves as Chief Technology Officer of Paradigm4 and Tamr, Inc.
Professor Stonebraker was awarded the ACM System Software Award in 1992 for his work on INGRES. Additionally, he was awarded the first annual SIGMOD Innovation award in 1994, and was elected to the National Academy of Engineering in 1997. He was awarded the IEEE John Von Neumann award in 2005, and is presently an Adjunct Professor of Computer Science at M.I.T, where he is co-director of the Intel Science and Technology Center focused on big data.

What Does ‘Big Data’ Mean and Who Will Win?

“Big Data” means different things to different people. To me, it means one of four totally different problems:
Big volumes of data, but “small” analytics. The traditional data warehouse vendors support SQL analytics on very large volumes of data. In this talk, I make a few comments on where I see this market going.
Big analytics on big volumes of data. By big analytics, I mean data clustering, regressions, machine learning, and other much more complex analytics on very large amounts of data. I will explain the various approaches to integrating complex analytics into DBMSs, and discuss which ones seem more promising. In addition, I will explore why Hadoop, in its current form, will not be a player in this market.
Big velocity. By this I mean being able to absorb and process a firehose of incoming data for applications like electronic trading. In this market, the traditional SQL vendors are a non-starter. I will discuss alternatives including complex event processing (CEP), NoSQL and NewSQL systems. I will also make a few comments about the “internet of things”.
Big Diversity. Many enterprises are faced with integrating a larger and larger number of data sources with diverse data (spreadsheets, web sources, XML, traditional DBMSs). The traditional ETL products do not appear up to the challenges of this new world, and I talk about an alternate way to go.

Demetri Terzopoulos (PhD ’84 MIT), Chancellor’s Professor of Computer Science at UCLA, holds the rank of Distinguished Professor and directs the UCLA Computer Graphics & Vision Laboratory. He is or was a Guggenheim Fellow, a Fellow of the ACM, a Fellow of the IEEE, a Fellow of the Royal Society of London, a Fellow of the Royal Societies of London and Canada, a member of the European Academy of Sciences and the New York Academy of Sciences, and a life member of Sigma Xi. One of the most highly cited authors in engineering and computer science, his many awards include an Academy Award for Technical Achievement from the Academy of Motion Picture Arts and Sciences for his pioneering research on physics-based computer animation, and the inaugural Computer Vision Distinguished Researcher Award from the IEEE for his pioneering and sustained research on deformable models and their applications.

The Computer Simulation of People

The computer simulation of people is a grand challenge problem with potentially profound impact across many disciplines. In the field of computer graphics, it is especially relevant to the animation of realistic human characters for a variety of applications in the interactive computer game and motion picture industries. I will overview our progress on realistic human modeling and simulation, whose scope spans the biomechanical, behavioral, and social levels. In particular, I will review the state-of-the-art in musculoskeletal physics-based simulation and neuromuscular control of the human body, as well as the artificial life approach to multi-human simulation yielding 3D virtual worlds populated by lifelike autonomous pedestrians with some proper social etiquette. Finally, I will discuss the profound scientific and computational challenges that remain in comprehensively simulating humans as individuals and in collectives.