Computing in the 21st Century 2019

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.

Raj Reddy is a University Professor of Computer Science and Robotics, and Moza Bint Nasser Chair at Carnegie Mellon University. He was an Assistant Professor at Stanford from 1966-69 and Faculty Member at Carnegie Mellon since 1969. He served as the founding Director of the Robotics Institute from 1979 to 1991 and the Dean of School of Computer Science from 1991 to 1999.

He has been active in AI research for over five decades in the areas of AI, Speech Understanding, Image Understanding, Robotics, Multi-sensor Fusion, and Intelligent Agents.Dr. Reddy’s current research interests include: Technology in Service of Society, Voice Computing for the 3B semi-literate populations at the bottom of the pyramid, Digital Democracy, and Learning Science and Technologies.He is a member of the National Academy of Engineering and a foreign member of China Academy Engineering. He served as co-chair of President Clinton’s Information Technology Advisory Committee (PITAC) from 1999 to 2001. Dr. Reddy is the recipient of the Legion of Honor in 1984, the ACM Turing Award in 1994, the Padma Bhushan in 2001, the Honda Prize in 2005 and Vannevar Bush Award in 2006.

Back to the Future: Past, Present and Future of AI

There has been a lot hype and misinformation about AI in the media. Many of these predictions will not happen. To try to predict what might happen we go back 60 years to the beginning of AI research, starting with funding by DARPA. My main thesis is that over the next 60 years, we will be working on similar topics as before, except we will have million times more computing power and million times more memory. This will enable invention of new techniques and paradigms leading to unexpected advances in AI, such as learning from a single example and the emergence of a world without language barriers. Mainly, we will see the emergence of Knowledge as a Service (KaaS) industry, where new startups will create Apps that are always ON and always LEARNING resulting in Intelligent Assistants such as Cognition Amplifiers that will enable us to do many daily tasks faster and with less effort and Guardian Angels that will enable us to do tasks previously impossible for humans, providing us with superhuman capabilities. We will present a possible Architecture of Intelligent Agents and the creation of Intelligent Agent market place.

Harry Shum is executive vice president of Microsoft’s Artificial Intelligence (AI) and Research group. He is responsible for driving the company’s overall AI strategy and forward-looking research and development efforts spanning infrastructure, services, apps and agents. He oversees AI-focused product groups including Bing and Cortana. He also leads Microsoft Research, one of the world’s premier computer science research organizations, and its integration with the engineering teams across the company.

Previously, Dr. Shum served as the corporate vice president responsible for Bing search product development from 2007 to 2013. Prior to his engineering leadership role at Bing and online services, he oversaw the research activities at Microsoft Research Asia and the lab’s collaborations with universities in the Asia Pacific region, and was responsible for the Internet Services Research Center, an applied research organization dedicated to advanced technology investment in search and advertising at Microsoft.

Dr. Shum joined Microsoft Research in 1996 as a researcher based in Redmond, Washington. In 1998 he moved to Beijing as one of the founding members of Microsoft Research China (later renamed Microsoft Research Asia). There he began a nine-year tenure as a researcher, subsequently moving on to become research manager, assistant managing director and managing director of Microsoft Research Asia and a Distinguished Engineer.

Dr. Shum is an IEEE Fellow and an ACM Fellow for his contributions to computer vision and computer graphics. He received his Ph.D. in robotics from the School of Computer Science at Carnegie Mellon University. In 2017, he was elected to the National Academy of Engineering of the United States. In September 2018, he was elected an International Fellow of the Royal Academy of Engineering in honor of his outstanding engineering achievements.

Demetri Terzopoulos is a Chancellor’s Professor of Computer Science at the University of California, Los Angeles, where he holds the rank of Distinguished Professor and directs the UCLA Computer Graphics & Vision Laboratory. He is also Co-Founder and Chief Scientist of VoxelCloud, Inc., a multinational company that applies artificial intelligence to healthcare. He graduated from McGill University, received his PhD degree (’84) in Artificial Intelligence from the Massachusetts Institute of Technology (MIT), and remained a Research Scientist at the MIT Artificial Intelligence Laboratory through 1985. 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 Society of Canada, a member of the European Academy of Sciences and the New York Academy of Sciences, and a life member of Sigma Xi.

His many awards include an Academy Award for Technical Achievement from the Academy of Motion Picture Arts and Sciences for his pioneering work 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 ISI and other indexes list him among the most highly-cited authors in engineering and computer science, with more than 400 published research papers and several volumes, primarily in computer graphics, computer vision, medical imaging, computer-aided design, and artificial intelligence/life. He has given approximately 500 invited talks around the world about his research, including well over 100 distinguished lectures and keynote/plenary addresses. He joined UCLA in 2005 from New York University, where he held the Henry and Lucy Moses Professorship in Science and was Professor of Computer Science and Mathematics at NYU’s Courant Institute of Mathematical Sciences. Previously, he was Professor of Computer Science and Professor of Electrical & Computer Engineering at the University of Toronto. Before becoming an academic in 1989, he was a Program Leader at Schlumberger corporate research centers in California and Texas.

AI in Medical Imaging for Healthcare: Past, Present, Future

Automated medical image analysis sits on the cusp of revolutionizing the healthcare industry in the 21st century. I will present a personal retrospective of the major milestones in bringing computer vision and other aspects of artificial intelligence to bear on the difficult challenges of computer-aided medical image analytics and diagnostics, dating back to my earliest work in the domain from the late 1970s. The major paradigms range from statistical pattern recognition methods in those early years, to model-based methods from the late 1980s through Y2K, to data-driven (deep) machine learning methods that are currently garnering much excitement and substantial entrepreneurial activity. Finally, I will discuss the increasingly evident limitations of the latter, which motivates our recent work aimed at achieving even higher performance levels on important automated medical image analysis tasks and opens up new avenues for future research.

Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Adjunct Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology. Professor Wing’s general research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current interests are in the foundations of security and privacy, with a new focus on trustworthy AI. She was or is on the editorial board of twelve journals, including the Journal of the ACM and Communications of the ACM. She is currently a member of: the National Library of Medicine Blue Ribbon Panel; the Science, Engineering, and Technology Advisory Committee for the American Academy for Arts and Sciences; the Board of Trustees for the Institute of Pure and Applied Mathematics; the Advisory Board for the Association for Women in Mathematics; and the Alibaba DAMO Technical Advisory Board. She has been chair and/or a member of many other academic, government, and industry advisory boards. She received the CRA Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).

Data for Good: Ensuring the Responsible Use of Data to Benefit Society

Every field has data. We use data to discover new knowledge, to interpret the world, to make decisions, and even to predict the future. The recent convergence of big data, cloud computing, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars, treat disease, and keep us safe. At the same time, such capabilities risk leading to biased, inappropriate, or unintended action. The design of data science solutions requires both excellence in the fundamentals of the field and expertise to develop applications which meet human challenges without creating even greater risk.

The Data Science Institute at Columbia University promotes “Data for Good”: using data to address societal challenges and bringing humanistic perspectives as—not after—new science and technology is invented. Started in 2012, the Institute is now a university-level institute representing over 350 affiliated faculty from 16 different schools and institutes across campus. Data science literally touches every corner of the university.

In this talk, I will present the mission of the Institute and highlights of our educational and research activities—all with the aim of ensuring the responsible use of data to benefit society.

Yuanyuan is a Qualcomm Chair Professor in Mobile Computing at University of California, San Diego (UCSD) since 2009. Her area of expertise includes computer reliability, data center management,  and mobile systems. She obtained her MS and Ph.D from Princeton University.  She is an ACM Fellow (2013) and IEEE Fellow (2015), Sloan Research Fellow (2007) and the winner of ACM Mark Weiser award (2015). She is always proud of her former and current Ph.D students, six of whom have joined top universities as tenured or tenure-track faculty.    In parallel to her academic career, she has also co-founded three companies, with the first two successfully acquired by public companies such as VmWare.  Since 2014, she has been busy with her third startup, Whova. It has gained substantial customer traction worldwide and has helped more than 8000 conferences/events in 85 countries,  providing her deeper insights in understanding mobile app  development process and its unique challenges.

The Human Dimension of Cloud Computing

Cloud computing has become the typical way to deliver enterprise applications. As today’s cloud infrastructure becomes more and more complex with hybrid cloud as well as AI and advanced data processing integrated in the platform, human errors has become one of the major causes of failures in cloud and Internet systems, as reported by many system vendors and service providers. While various fault tolerance and recovery mechanisms are useful in handling hardware and software failures, they are less effective in handling system administrators’ human errors.

The very recent outage in Facebook on March 13th, 2019 was also caused by a server configuration error, affecting millions of users. In addition to reliability, configuration errors also can lead to security issues. OWASP reports misconfiguration as one of the top 10 most critical web security risks. In 2017, a configuration error of Amazon S3 storage exposed personal information of 200 million users. In this talk, I will focus a few current challenges on the human dimension of cloud computing and management. Due to legacy and various other reasons, most today’s data center system management requirement (in particular system configuration) do not follow the primary design principals of human-computer interaction (HCI), namely (i) simplicity, (ii) feedback, and (iii) consistency, making cloud management error prone for system admins.