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Stanford
The Future of Artificial Intelligence
Emerging Topics and Societal Benefit
A Partner Event of the 2016 Global Entrepreneurship Summit
Hosted by The White House Office of Science and Technology Policy and Stanford University
June 23, 2016
5:30–9:30 p.m.
Paul Brest Hall
555 Salvatierra Walk
Stanford University
ABOUT THIS EVENT
Welcome to The Future of Artificial Intelligence Partner Event of the 2016 Global
Entrepreneurship Summit at Stanford University. Today leading artificial intelligence (AI)
researchers will discuss the most impactful research topics in AI and highlight the challenges
and potentials of artificial intelligence.
There is a lot of excitement about AI and how to create computers capable of intelligent
behavior. After years of steady but slow progress on making computers “smarter” at everyday
tasks, a series of breakthroughs in the research community and industry have recently
spurred momentum and investment in the development of this field.
There is a sense that AI has made sufficient inroads into everyday life that we should pause
and take stock of the great opportunities and challenges before us. We are aware of selfdriving cars, intelligent assistants on phones and mobile devices, and the use of data for
many activities in academia, government and industry. Now is a good time to think about
the anticipated evolution of these capabilities and how they might impact economic, social,
political and cultural activities. We look forward to engaging with you about how best to
harness the innovations and the array of considerations brought by artificial intelligence.
Best Regards,
Russ Altman, Co-Chair
Professor, Bioengineering, Genetics, Medicine, and
Computer Science (by courtesy); Chair, Biomedical
Informatics Training Program; Faculty Director, One
Hundred Year Study on Artificial Intelligence (AI100)
Fei-Fei Li, Co-Chair
Associate Professor, Computer Science and
Psychology (by courtesy); Director, Stanford Artificial
Intelligence Lab; Director, Stanford-Toyota Center for
AI Research
2016 Conference Agenda
Thursday, June 23, 2016
5:30 p.m.
Reception in the Rehnquist Courtyard outside of Paul Brest Hall
6:30 p.m.
Opening Remarks and Keynote Presentations
Introduction: Russ Altman (Stanford)
Government: Megan Smith (Office of Science and Technology Policy)
Academic: Fei-Fei Li (Stanford)
7:05 p.m.
Invited Talks
Daniela Rus (MIT)
Anshul Kundaje (Stanford)
Chris Manning (Stanford)
7:30 p.m.
Panel: 100 Year Study of Artificial Intelligence
Moderator: Russ Altman
Barbara Grosz (Harvard), panelist
Yoav Shoham (Stanford), panelist
Milind Tambe (USC), panelist
8:15 p.m.
Invited Talks
Finale Doshi-Velz (Harvard)
Stefano Ermon (Stanford)
John Duchi (Stanford)
Dieter Fox (University of Washington)
Chris Ré (Stanford)
8:55 p.m.
Keynote Presentation
Industry: Arvind Krishna (IBM)
9:10 p.m.
Closing Remarks
9:15 p.m.
Meeting ends
Speakers
Russ Biagio Altman
Kenneth Fong Professor of Bioengineering, Genetics, Medicine and, by courtesy, of Computer Science, Stanford University
Russ Biagio Altman is a professor of bioengineering, genetics, & medicine (and of computer science, by courtesy)
and past chairman of the Bioengineering Department at Stanford University. His primary research interests are in the
application of computing and informatics technologies to problems relevant to medicine. Dr. Altman holds an A.B. from
Harvard College, and M.D. from Stanford Medical School, and a Ph.D. in Medical Information Sciences from Stanford.
He received the U.S. Presidential Early Career Award for Scientists and Engineers and a National Science Foundation
CAREER Award. He has chaired the Science Board advising the FDA Commissioner, and currently serves on the NIH Director’s Advisory
Committee. He is an organizer of the annual Pacific Symposium on Biocomputing (http://psb.stanford.edu/), and a founder of Personalis, Inc.
Dr. Altman is board certified in Internal Medicine and in Clinical Informatics. Altman is the faculty director of the 100 year study of Artificial
Intelligence (ai100.stanford.edu). https://profiles.stanford.edu/russ-altman
Finale Doshi-Velez
Assistant Professor, Harvard University
Finale Doshi-Velez leads the Data to Actionable Knowledge group at the Harvard Paulson School of Engineering and
Applied Science. She completed her Ph.D. from MIT and her postdoc at Harvard Medical School. She was a Marshall
Scholar at the University of Cambridge and was named one of IEEE’s “AI Top 10 to Watch” in 2013. Doshi-Velez is
excited about methods to turn data into actionable knowledge. Her core research in machine learning, computational
statistics, and data science is inspired by—and often applied to—the objective of accelerating scientific progress and
practical impact in healthcare and other domains. https://www.seas.harvard.edu/directory/finale
John C. Duchi
Assistant Professor of Statistics and Electrical Engineering, Stanford University
John C. Duchi completed his Ph.D. in computer science at Berkeley in 2014. His research interests are a bit eclectic,
and they span statistics, computation, optimization, and machine learning. At Berkeley, he worked in the Statistical
Artificial Intelligence Lab (SAIL) under the joint supervision of Michael Jordan and Martin Wainwright. He obtained his
master’s degree (MA) in statistics in Fall 2012. He was also an undergrad and a master’s student at Stanford University,
where he worked with Daphne Koller in her research group, DAGS. He also spends some time at Google Research,
where he had (and continue to have) the great fortune to work with Yoram Singer. https://profiles.stanford.edu/john-duchi
Stefano Ermon
Assistant Professor of Computer Science, Stanford University
Stefano Ermon is Assistant Professor in the Department of Computer Science at Stanford University, where he is
affiliated with the Artificial Intelligence Laboratory and a fellow of the Woods Institute for the Environment. Ermon
received his Bachelors and Master’s degrees in Electrical and Electronic Engineering from the Università degli Studi
di Padova and he completed his Ph.D. in computer science at Cornell in 2014. His research is centered on techniques
for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial
optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging
field of computational sustainability. Stefano has won several awards, including two Best Student Paper Awards, one Runner-Up Prize, and a
McMullen Fellowship. https://cs.stanford.edu/~ermon/
Dieter Fox
Professor of Computer Science, University of Washington
Dieter Fox is a Professor in the Department of Computer Science & Engineering at the University of Washington,
Seattle, where he heads the UW Robotics and State Estimation Lab. From 2009 to 2011, he was also Director of the
Intel Research Labs Seattle. Fox obtained his Ph.D. from the University of Bonn, Germany. Before joining the faculty
of UW, he spent two years as a postdoctoral researcher at the CMU Robot Learning Lab. Fox’s research is in robotics
and artificial intelligence, with a focus on state estimation and perception applied to various problems in robotics
and activity recognition. Dieter is an IEEE Fellow, a Fellow of the AAAI, and he received several best paper awards at major robotics, AI, and
computer vision conferences. He was an editor of the IEEE Transactions on Robotics, program co-chair of the 2008 AAAI Conference on
Artificial Intelligence, and program chair of the 2013 Robotics: Science and Systems conference. https://homes.cs.washington.edu/~fox/
Speakers, continued
Barbara J. Grosz
Higgins Professor of Natural Sciences, Harvard University
Barbara J. Grosz’s contributions to AI include establishing the research field of computational modeling of discourse,
developing some of the earliest computer dialogue systems, pioneering models of collaboration, and the development
of collaborative multi-agent systems and collaborative systems for human-computer communication. Grosz is a
member of the National Academy of Engineering, the American Philosophical Society, and the American Academy of
Arts and Sciences and a corresponding fellow of the Royal Society of Edinburgh, and she is a fellow of the Association
for the Advancement of Artificial Intelligence (AAAI), the Association for Computing Machinery (ACM) and the American Association for the
Advancement of Science. She is recipient of the University of California, Berkeley Distinguished Alumna Award in Computer Sciences and
Engineering (1997), the ACM/AAAI Allen Newell Award (2009), and the 2015 IJCAI Research Excellence Award. http://grosz.seas.harvard.edu/
Arvind Krishna
Senior Vice President and Director, IBM Research
Arvind Krishna helps guide IBM’s overall technical strategy in core and emerging technologies, including cognitive
computing, quantum computing, cloud platform services, data-driven solutions and blockchain. Krishna was general
manager of IBM Systems and Technology Group’s development and manufacturing organization, responsible for
the advanced engineering and development of a full technology portfolio, ranging from advanced semiconductor
materials to leading-edge microprocessors, servers and storage systems. Krishna has an undergraduate degree
from the Indian Institute of Technology, Kanpur, and a Ph.D. from the University of Illinois at Urbana-Champaign. He is the recipient of a
distinguished alumni award from the University of Illinois, is the co-author of 15 patents, has been the editor of IEEE and ACM journals, and has
published extensively in technical conferences and journals. https://www-03.ibm.com/press/us/en/biography/45780.wss
Anshul Kundaje
Assistant Professor of Genetics and Computer Science, Stanford University
Anshul Kundaje’s research focuses on deciphering the molecular and genetic basis of disease by integrative analysis
of diverse types of large-scale genomic data. His lab develops statistical and machine learning methods to decipher
functional elements in the human genome, understand their effects on cellular function across diverse cell types and
interpret the molecular impact of natural and disease-associated genetic variation. Kundaje completed his Ph.D. in
Computer Science in 2008 from Columbia University. As a postdoc at Stanford University from 2012-2014 and a research
scientist at MIT and the Broad Institute from 2012-2014, he led the integrative analysis efforts for two of the largest functional genomics
consortia - The Encyclopedia of DNA Elements (ENCODE) and The Roadmap Epigenomics Project. Dr. Kundaje is also a recipient of the 2014
Alfred Sloan Fellowship. https://profiles.stanford.edu/anshul-kundaje
Fei-Fei Li
Associate Professor, Computer Science and Psychology (by courtesy); Director, Stanford Artificial Intelligence Lab,
Director, Stanford-Toyota Center for AI Research
Li’s main research areas are in machine learning, computer vision and cognitive and computational neuroscience.
She has published more than 100 scientific articles, including Nature, PNAS, Journal of Neuroscience, CVPR, ICCV, NIPS,
ECCV, IJCV, IEEE-PAMI. Li obtained her B.A. degree in physics from Princeton in 1999 with High Honors, and her Ph.D.
degree in electrical engineering from California Institute of Technology (Caltech) in 2005. Li joined Stanford in 2009 and
was promoted to associate professor with tenure in 2012. Prior to that, she was on faculty at Princeton University (2007-2009) and University
of Illinois Urbana-Champaign (2005-2006). Li was a speaker at the TED2015 main conference, a recipient of the 2016 Nvidia Pioneer in AI Award,
2014 IBM Faculty Fellow Award, 2011 Alfred Sloan Faculty Award, 2012 Yahoo Labs FREP award, 2009 NSF CAREER award, the 2006 Microsoft
Research New Faculty Fellowship and a number of Google Research awards. https://profiles.stanford.edu/fei-fei-li
Christopher Manning
Professor of Linguistics and Computer Science, Stanford University
Christopher Manning’s Ph.D. is from Stanford and he held faculty positions at Carnegie Mellon University and the
University of Sydney before returning to Stanford. His research goal is computers that can intelligently process,
understand, and generate human language material. Manning concentrates on machine learning approaches to
computational linguistic problems, including syntactic parsing, computational semantics and pragmatics, textual
inference, machine translation, and deep learning for NLP. He is an ACM Fellow, a AAAI Fellow, and an ACL Fellow, and
has coauthored leading textbooks on statistical natural language processing and information retrieval. He is a member of the Stanford NLP
group (@stanfordnlp). https://profiles.stanford.edu/chris-manning
Speakers, continued
Christopher Ré
Assistant Professor of Computer Science, Stanford University
Christopher (Chris) Ré’s work goal is to enable users and developers to build applications that more deeply understand
and exploit data. Chris received his Ph.D. from the University of Washington in Seattle. He then spent four wonderful
years on the faculty of the University of Wisconsin, Madison, before coming to Stanford in 2013. He helped discover
the first join algorithm with worst-case optimal running time, which won the best paper at PODS 2012. He also helped
develop a framework for feature engineering that won the best paper at SIGMOD 2014. Work from his group has been
incorporated into scientific efforts including the IceCube neutrino detector and PaleoDeepDive, and into Cloudera’s Impala and products
from Oracle, Pivotal, and Microsoft’s Adam. He received an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore
Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, and the MacArthur Foundation Fellowship in 2015.
https://profiles.stanford.edu/christopher-re
Daniela Rus
Director, CSAIL, Andrew (1956) and Erna Viterbi Professor, Massachusetts Institute of Technology (MIT)
Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and
Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus’s research interests
are in robotics, mobile computing, and data science. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM,
AAAI and IEEE, and a member of the National Academy of Engineering. She earned her Ph.D. in Computer
Science from Cornell University. Prior to joining MIT, Rus was a professor in the Computer Science Department
at Dartmouth College. http://danielarus.csail.mit.edu/
Yoav Shoham
Professor Emeritus, Computer Science, Stanford University
Yoav Shoham is Professor (emeritus) of Computer Science at Stanford University and Principal Scientist at Google.
Previously a director of the Stanford AI Lab, Yoav’s research has focused on knowledge representation, game theory,
and electronic commerce. He is Fellow of AAAI and of the ACM, and among his awards are AAAI/ACM Allen Newell award
and the ACM/SIGART Autonomous Agents Research Award. He has published or edited five books and many articles,
and the online Game Theory courses he co-teaches have had over 500,000 registrants. A serial entrepreneur, Yoav
was co-founder and chairman of TradingDynamics (ARBA), Katango (GOOG), and Timeful (GOOG). Dr. Shoham is a member of the Standing
Committee of the One Hundred Year Study of AI. http://robotics.stanford.edu/users/shoham/
Megan Smith
United States Chief Technology Officer (CTO) in the Office of Science and Technology Policy
Megan Smith focuses on how technology policy, data and innovation can advance the future of our nation. Smith
is an award-winning entrepreneur, engineer, and tech evangelist. She most recently served as a Vice President at
Google, first leading New Business Development—where she managed early-stage partnerships, pilot explorations,
and technology licensing across Google’s global engineering and product teams. Smith previously served as CEO
of PlanetOut, a leading LGBT online community. Smith was a member of the Massachusetts Institute of Technology
(MIT) student team that designed, built, and raced a solar car 2000 miles across the Australian outback. She has served on the boards of MIT,
MIT Media Lab, MIT Technology Review, and Vital Voices; as a member of the USAID Advisory Committee on Voluntary Foreign Aid; and as an
advisor to the Joan Ganz Cooney Center and the Malala Fund. Smith holds a bachelor’s and master’s degrees in mechanical engineering from
the Massachusetts Institute of Technology (MIT). https://www.whitehouse.gov/administration/eop/ostp/about/leadershipstaff/smith
Milind Tambe
Helen N. and Emmett H. Jones Professor, University of Southern California (USC)
Milind Tambe is Helen N. and Emmett H. Jones Professor in Engineering at the University of Southern California(USC),
and Professor in the Computer Science and Industrial and Systems Engineering Departments. He is a fellow of AAAI
(Association for Advancement of Artificial Intelligence) (2007), fellow of ACM (Association for Computing Machinery)
(2013), recipient of the ACM Autonomous Agents Research Award (2005), Christopher Columbus Fellowship Foundation
Homeland security award(2010), the INFORMS Wagner prize for excellence in Operations Research practice (2012), the
Rist Prize of the Military Operations Research Society (2011), IBM Faculty Award(2012), Okawa foundation faculty research award (2004), the
RoboCup scientific challenge award(1999), Orange County Engineering Council Outstanding Project Achievement Award (2015), USC Associates
Award for Creativity in Research (2014) and USC Viterbi School of Engineering use-inspired research award (2009). Dr. Tambe is a member of the
current Study Panel for the One Hundred Year Study of AI. http://teamcore.usc.edu/tambe/
About the
Stanford Artificial
Intelligence Lab
Artificial Intelligence comprises
the complete loop from
sensing to perception, learning,
communications, and action.
Stanford’s Artificial Intelligence
Lab is devoted to the design of
intelligent machines that serve,
extend, expand, and improve
human endeavor, making life more
productive, safer, and healthier.
These intelligent machines will learn
everything about anything using
multi-sensory information and the
entire cyber world of information
and knowledge.
The faculty members of the
Stanford AI Lab are changing the
world. Their research includes
deep learning and machine
learning; robotics; natural language
processing; vision, haptics, and
sensing; big data and knowledge
base; and genomics, medicine,
and healthcare. The approach is
personalized, adaptive, anticipatory,
communicative, and context aware.
Please contact Steve Eglash,
Executive Director, Data Science
Programs, Stanford University,
[email protected], for
further information.
Stanford Artificial Intelligence Lab
Gates Computer Science Building
Stanford, CA 94305
ai.stanford.edu
Stanford
The Future of Artificial Intelligence
Emerging Topics and Societal Benefit
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