Stuart Russell

Professor of EECS at UC Berkeley
Holder of the Smith Zadeh Chair in Engineering
Author of Artificial Intelligence: A Modern Approach

Stuart Russell received his B.A. with first-class honours in physics from Oxford University in 1982 and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a Professor (and formerly Chair) of Electrical Engineering and Computer Sciences and holder of the Smith-Zadeh Chair in Engineering. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book Artificial Intelligence: A Modern Approach (with Peter Norvig) is the standard text in AI; it has been translated into 13 languages and is used in over 1300 universities in 118 countries. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, and philosophical foundations. His current concerns include the threat of autonomous weapons and the long-term future of artificial intelligence and its relation to humanity.

You can read more about Prof. Russell’s work and accomplishments at his website.

Pieter Abbeel

Professor of EECS at UC Berkeley

Pieter Abbeel has been a Professor at UC Berkeley (EECS, BAIR) since 2008 and was a Research Scientist at OpenAI during 2016-2017. Pieter has developed apprenticeship learning algorithms that have enabled advanced helicopter aerobatics, including maneuvers such as tic-tocs, chaos and auto-rotation, which only exceptional human pilots can perform. His group has enabled the first end-to-end completion of reliably picking up a crumpled laundry article and folding it and has pioneered deep reinforcement learning for robotics, including learning locomotion and visuomotor skills. His current research focuses on robotics and machine learning with particular focus on deep reinforcement learning, deep imitation learning, deep unsupervised learning, meta-learning, learning-to-learn, and AI safety.

You can read more about Prof. Abbeel’s work and accomplishments at his website.

Anca Dragan

Assistant Professor of EECS at UC Berkeley
Founder, InterACT Lab

Anca Dragan got her B.Sc. in Computer Science from Jacobs University Bremen in Germany, after which she went to Carnegie Mellon for her Ph.D. in Robotics. She is now an Assistant Professor in the EECS Department at UC Berkeley. Her goal is to enable robots to work with, around, and in support of people. She runs the InterACT Lab, which focuses on algorithms for human-robot interaction—algorithms that move beyond the robot’s function in isolation, and generate robot behavior that also accounts for interaction and coordination with end-users. She also helped found and serves on the steering committee for the Berkeley AI Research (BAIR) Lab.

You can read more about Prof. Dragan’s work and accomplishments at her website.

Bart Selman

Professor of Computer Science and Engineering at Cornell University

Bart Selman is a Professor of Computer Science at Cornell University; previously, he worked at AT&T Bell Laboratories. His research interests include computational sustainability, efficient reasoning procedures, planning, knowledge representation, and connections between computer science and statistical physics. He has (co-)authored over 100 publications, including six which won best paper awards. He is a Fellow of the American Association for Artificial Intelligence and a Fellow of the American Association for the Advancement of Science.

You can read more about Prof. Selman’s work and accomplishments at his website.

Tom Griffiths

Professor of Psychology and Cognitive Science at Princeton
Previously Director of the Computational Cognitive Science Lab
Previously Director of the Institute of Cognitive and Brain Sciences

Tom Griffiths is a Professor of Psychology and Cognitive Science at Princeton, and previously the Director of the Computational Cognitive Science Lab and the Institute of Cognitive and Brain Sciences at the University of California, Berkeley. He is interested in developing mathematical models of higher level cognition and understanding the formal principles that underlie our ability to solve the computational problems we face in everyday life. His current focus is on inductive problems, such as probabilistic reasoning, learning causal relationships, acquiring and using language, and inferring the structure of categories. He tries to analyze these aspects of human cognition by comparing human behavior to optimal or “rational” solutions to the underlying computational problems. For inductive problems, this usually means exploring how ideas from artificial intelligence, machine learning, and statistics (particularly Bayesian statistics) connect to human cognition.

You can read more about Prof. Griffiths’ work and accomplishments at his website.

Joseph Halpern

Professor of Computer Science and Engineering at Cornell University

Joseph Halpern is a Professor of Computer Science at Cornell University. His research focuses on the interface between game and decision theory and computer science, on reasoning about knowledge and uncertainty, and on causality. He has also done work on and continues to think actively about security, (fault tolerant) distributed computing, and modal logic.

You can read more about Prof. Halpern’s work and accomplishments at his website.

Michael Wellman

Professor of Computer Science and Engineering at the University of Michigan

Michael P. Wellman is a Professor of Computer Science & Engineering at the University of Michigan. He received a Ph.D. from the Massachusetts Institute of Technology in 1988 for his work in qualitative probabilistic reasoning and decision-theoretic planning. From 1988 to 1992, Wellman conducted research in these areas at the USAF’s Wright Laboratory. For the past 25 years, his research has focused on computational market mechanisms and game-theoretic reasoning methods, with applications in electronic commerce, finance, and cyber-security. Wellman previously served as Chair of the ACM Special Interest Group on Electronic Commerce (SIGecom) and as Executive Editor of the Journal of Artificial Intelligence Research. He is a Fellow of the Association for the Advancement of Artificial Intelligence and the Association for Computing Machinery.

You can read more about Prof. Wellman’s work and accomplishments at his website.

Satinder Singh Baveja

Professor of Computer Science and Engineering at the University of Michigan
Director of the Artificial Intelligence Laboratory

Satinder Singh Baveja is a Professor in the Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, as well as Director of the AI Lab there. His main research interest is in the old-fashioned goal of Artificial Intelligence (AI), that of building autonomous agents that can learn to be broadly competent in complex, dynamic, and uncertain environments. The field of reinforcement learning (RL) has focused on this goal and accordingly his deepest contributions are in RL. From time to time, he takes seriously the challenge of building agents that can interact with other agents and even humans in both artificial and natural environments. This has led to research in computational game theory / mechanism design, cognitive science, and human-computer interaction.

You can read more about Prof. Singh’s work and accomplishments at his website.

Tania Lombrozo

Professor of Psychology at UC Berkeley

Tania Lombrozo received her Ph.D. from Harvard University and is a Professor of Psychology at UC Berkeley. She will be joining the Department of Psychology at Princeton University in July 2018. Her research aims to address basic questions about learning, reasoning, and decision-making using the empirical tools of experimental psychology and the conceptual tools of analytic philosophy. You can read more about Prof. Lombrozo’s work here or here.

Mark Nitzberg

Executive Director of CHAI
Head of Strategic Outreach for Berkeley AI Research

Dr. Mark Nitzberg is currently Executive Director of CHAI, as well as head of strategic outreach for Berkeley AI Research (BAIR). He began studying AI as a stowaway student at MIT in the early 1980’s and wrote his Ph.D. in Computer Vision and Human Perception in 1991 under David Mumford at Harvard University. Dr. Nitzberg has built companies and products in the areas of computer vision, machine learning, financial portfolio optimization, workflow efficiencies, online commerce, development aid data capture and analytics, and film and theatre. Most recently he was the Director of Computer Vision Products at A9/Amazon following their acquisition of The Blindsight Corporation, a maker of assistive technologies for low vision and active aging, where Mark was founding CEO. You can read more about Dr. Nitzberg’s work at his LinkedIn.

Martin Fukui

Assistant Director

Martin currently serves as the Assistant Director at CHAI. Prior to his current appointment, he served as the Interim Assistant Director and the Operations and Events Manager.

Before joining CHAI, he worked at several different IP transactional law firms in the Bay Area and mostly dealt with contract drafting, contract negotiations, terms of use, terms of service, and privacy policies. He also worked for the California Bar Association by organizing events related to entertainment law and digital media. While he was in law school, he served as the President of the Association for Communication, Sports, and Entertainment Law (ACSEL) and the Co-Editor-In-Chief for the Hastings Communications and Entertainment Law Journal.

After working in the IP transactional legal field for several years, he came across AI safety and decided to change his career path and use his skillset to help AI safety research organizations flourish and grow.

He received his B.A. from University of California, Berkeley and J.D. from University of California, Hastings.

Andrew Critch

Research Scientist at CHAI
Founder & President of the Berkeley Existential Risk Initiative

Dr. Andrew Critch is currently a full-time Research Scientist at CHAI, and spends around a ½ day per week volunteering for the Berkeley Existetial Risk initiative. He earned his Ph.D. in mathematics at UC Berkeley studying applications of algebraic geometry to machine learning models. During that time, he cofounded the Center for Applied Rationality and SPARC. Dr. Critch has been offered university faculty and research positions in mathematics, mathematical biosciences, and philosophy, worked as an algorithmic stock trader at Jane Street Capital’s New York City office, and as a Research Fellow at the Machine Intelligence Research Institute. His current research interests include logical uncertainty, open source game theory, and mitigating race dynamics between companies and nations in AI development.

You can read more about Dr. Critch’s work and accomplishments at his website.

Caroline Jeanmaire

Director of Strategic Research and Partnerships

Caroline leads CHAI’s partnership and external relations strategy, focusing on building a research community around AI safety and relationships with key stakeholders. She also researches models of international coordination to ensure the safety and reliability of AI systems. Before working at CHAI, she was an AI Policy Researcher and Project Manager at The Future Society, a think-tank incubated at Harvard’s Kennedy School of Government. She notably supported the organization of the first and second Global Governance of AI Forums at the World Government Summit in Dubai, with over 200 attendees. In the 2019 edition, she managed the Geopolitics of AI and International Panel on AI research committees.

Caroline is experienced in multi-party coordination and negotiation. She was a Youth Delegate to the United Nations for two years with the French delegation. She participated in numerous climate negotiations and technical intersessions (including COP21, COP22, COP23 and COP24).

Caroline has a dual master’s degree in International Relations from Peking University and Sciences Po Paris and a bachelor’s degree in political sciences from Sciences Po Paris. She also studied at Tufts University and at the Graduate Fletcher School of Law and Diplomacy. Caroline speaks English, French, Spanish and Mandarin Chinese.

Karthika Mohan

Postdoc

Karthika Mohan is a postdoctoral scholar at CHAI mentored by Stuart Russell. Karthika received her PhD in Computer Science (Artificial Intelligence) from UCLA where she was advised by Judea Pearl. Her research is of an interdisciplinary nature and her areas of interest include Causal Inference, Graphical Models and AI Safety. She was awarded the Google Outstanding Graduate Research Award, 2017 which is a UCLA Commencement Award. Currently she serves on the editorial board of the Journal of Causal Inference. In addition she is active on program committees of leading AI/ML conferences and reviews for journals in varied disciplines such as Machine Learning, Psychology and Philosophy. For more details, please visit here

Noor Brody

Operations Assistant

Noor is supported via the BERI-CHAI collaboration and works alongside Martin and Caroline to support CHAI researchers. Their work has included helping Caroline plan a virtual AI safety conference, managing a book distribution project, and writing newsletters. Noor recently graduated from UC Berkeley with a degree in Computer Science and has been working at CHAI since January 2020.

Cody Wild

Machine Learning Engineer

Cody Wild is a Research Engineer at CHAI. She holds a B.S. in Statistical Economics from Tulane University, and a M.S. in Analytics from the University of San Francisco. In the years after finishing her Masters program, Cody worked as an industry Data Scientist, first at LendUp, and then on an applied R&D team for Sophos Labs working on developing deep learning models to detect viruses and other malicious file types. Outside of formal employment, she’s worked to deepen her knowledge in Machine Learning by reading and synthesizing technical content from the field, which can be found online in both long and short form. She is passionate about precise understanding, clear arguments, and clean code, and is glad to be helping support a research team that’s thinking critically about the ways their work and AI writ large could negatively impact the world.

Steven Wang

Machine Learning Engineer

Steven is an ML research engineer at CHAI. He became interested in AI Safety after reading the 80000 Hours career guide.

During his undergraduate studies at UC Berkeley, Steven worked with Anca Dragan and Jaime Fisac on confidence-based human predictions. As an intern at CHAI, he worked with Dylan Hadfield-Menell on extensions to Inverse Reward Design.

You can check out what he’s been coding up lately on his GitHub.

Dylan Hadfield-Menell

Dylan will be starting as an Assistant Professor at the Massachusetts Institute of Technology in July 2021.

While at CHAI, Dylan was a Ph.D. student at UC Berkeley advised by Anca Dragan, Pieter Abbeel, and Stuart Russell. His research focused on algorithms that facilitate human-compatible artificial intelligence. In particular, he tried to develop frameworks that account for uncertainty about the objective being optimized.

Before coming to Berkeley, Dylan did a Master’s of Engineering with Leslie Kaelbling and Tomás Lozano-Pérez at MIT. At Berkeley, Dylan’s research has taken a turn to focus more on AI safety and, thinking longer term, AI value alignment.

In 2016, he and his advisors formally described a cooperative inverse reinforcement learning problem (paper). The problem serves as a tool to help researchers consider how robots could learn humans’ values via cooperative instruction. While robots could learn humans’ values by observing humans, cooperative instruction is likely to be significantly faster.

In 2017, Dylan and his advisors described an “off-switch game,” a simplified problem describing scenarios in which a human would like to turn off a robot, but the robot is able to disable its off-switch (paper). They showed that a robot who is uncertain about the utility derived from various outcomes in the game is more likely to allow a human to turn it off.

You can learn more about Dylan’s other professional work at his website and follow him on Twitter here.

Jaime Fernandez Fisac

Jaime is currently an Assistant Professor in Electrical Engineering at Princeton University. Prior to this appointment, he worked at Waymo as a Research Scientist

While Jaime was at CHAI as a PhD student, he worked on autonomous robots in both academia and industry, with a particular focus on collision avoidance and multi-agent systems. Broadly, his research focused on safely introducing robotics into society.

Under the guidance of CHAI Professors Anca Dragan and Tom Griffiths, and along with fellow CHAI graduate students Vael Gates and Dylan Hadfield-Menell, Jaime worked on solving the cooperative inverse reinforcement learning (CIRL) dynamic game using well-established models of human inference, decision making, and theory of mind from the cognitive science literature. Previous solutions have relied on modelling both the human and robot as perfectly rational and able to coordinate in advance, which are nontrivial assumptions in the real world. Instead, Jaime and his colleagues’ work models the human as pedagogic (i.e., her behaviour will aim to be instructive) and the robot as pragmatic (i.e., it knows the human is not perfectly rational but is still trying to teach it). Results suggest this formulation produces robots that are more competent collaborators (paper). In the past, Jaime has also researched how we can have robots choose their course of action in a way that will be easy for a human observer to anticipate (paper) and how incorporating uncertainty into a safety framework for robotic systems that works in conjunction with their learning process can provide meaningful safety guarantees (paper).

At Princeton, Jaime intends to look into how AI systems can utilize models of human cognition and behavior to ensure safe interaction with people. He believes that the safer robots will be those that engage their users and procure their cooperation, rather than try to protect against their indifference. He hopes that designing safe human-centered robotic systems in the short term will give us key insights to tackle the broader, long-term AI safety problem. Also, he has done research on having AI treat their models as fallible to protect against overconfidence. You can explore more about this topic by watching this video.

You can learn more about Jaime at his website.

Gillian Hadfield

Professor of Law and Professor of Strategic Management, University of Toronto

Gillian Hadfield holds a J.D. from Stanford Law School and a Ph.D. in economics from Stanford University. She is a leading proponent of the reform and redesign of legal systems for a rapidly changing world facing tremendous challenge from globalization and technology. Her extensive research examines how to make law more accessible, effective, and capable of fulfilling its role in balancing innovation, growth, and fairness. Prof. Hadfield is a member of the World Economic Forum’s Global Future Council on the Future of Technology, Values and Policy and co-curates the Forum’s Transformation Map for Justice and Legal Infrastructure. She was appointed in 2017 to the American Bar Association’s Commission on the Future of Legal Education, serves as Director of the USC Center for Law and Social Science and is a member of the World Justice Project’s Research Consortium. Her book, Rules for a Flat World: Why Humans Invented Law and How to Reinvent It for a Complex Global Economy, was published by Oxford University Press in November 2016.

You can read more about Prof. Hadfield’s work and accomplishments at her website.

Rosie Campbell

Strategy and Operations Advisor

Rosie Campbell is the Strategy and Operations Advisor for CHAI. She is also currently working at Partnerships on AI as a researcher and used to be the Assistant Director of CHAI. Her academic background is in Computer Science (MSc) and Physics (BSc) and includes some Philosophy and Machine Learning. She is motivated by the long-term and short-term challenges of aligning AI with human interests, but optimistic about the benefits of friendly AI.

Before joining CHAI, Rosie worked as a Research Engineer at BBC R&D in Manchester, UK. Here, she worked on a variety of technical research and development projects, including a leading role on a team exploring artificially intelligent production systems. While in Manchester Rosie co-founded the BBC’s Machine Learning Special Interest Group, as well as Manchester Futurists, a thriving community group exploring the impact of emerging technology on society and the future.

Siddharth Srivastava

Assistant Professor of Computer Science at Arizona State University

Siddharth Srivastava is an Assistant Professor of Computer Science at the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University. He received his Ph.D. in Computer Science from the University of Massachusetts Amherst. Previously, Prof. Srivastava was a Staff Scientist at the United Technologies Research Center in Berkeley. Prior to that, he was a postdoctoral researcher in the RUGS group at the University of California Berkeley. His research objective is to develop intelligent robots and software agents that assist humans in their daily lives. Towards this objective, his research focuses on developing formal frameworks, algorithms and implementations that allow autonomous agents to reason and act efficiently under uncertainty. His dissertation work received a Best Paper award at the International Conference on Automated Planning and Scheduling (ICAPS) and an Outstanding Dissertation award from the Department of Computer Science at UMass Amherst.

You can read more about Prof. Srivastava’s work and accomplishments at his website.

Alison Gopnik

Professor of Psychology at UC Berkeley
Affiliate Professor of Philosophy at UC Berkeley

Alison Gopnik is a professor of psychology and affiliate professor of philosophy at the University of California at Berkeley. She received her BA from McGill University and her PhD. from Oxford University. She is an internationally recognized leader in the study of cognitive science and of children’s learning and development and was one of the founders of the field of “theory of mind”, an originator of the “theory theory” of children’s development and more recently introduced the idea that probabilistic models and Bayesian inference could be applied to children’s learning. She has held a Center for Advanced Studies in the Behavioral Sciences Fellowship, the Moore Distinguished Scholar fellowship at the California Institute of Technology, the All Souls College Distinguished Visiting Fellowship at Oxford, and King’s College Distinguished Visiting Fellowship at Cambridge. She is an elected member of the Society of Experimental Psychologists, and the American Academy of Arts and Sciences and a fellow of the Cognitive Science Society and the American Association for the Advancement of Science. She has been continuously supported by the NSF and was PI on a 2.5 million dollar interdisciplinary collaborative grant on causal learning from the McDonnell Foundation.

She is the author or coauthor of over 100 journal articles and several books including “Words, thoughts and theories” MIT Press, 1997, and the bestselling and critically acclaimed popular books “The Scientist in the Crib” William Morrow, 1999, “The Philosophical Baby; What children’s minds tell us about love, truth and the meaning of life”, and “The Gardener and the Carpenter”, Farrar, Strauss and Giroux, the latter two won the Cognitive Development Society Best Book Prize in 2009 and 2016. She has also written widely about cognitive science and psychology for The New York Times, The Atlantic, The New Yorker, Science, Scientific American, The Times Literary Supplement, The New York Review of Books, New Scientist and Slate, among others. Her TED talk on her work has been viewed more than 3 million times. And she has frequently appeared on TV and radio including “The Charlie Rose Show” and “The Colbert Report”. Since 2013 she has written the Mind and Matter column for the Wall Street Journal. She lives in Berkeley with her husband Alvy Ray Smith, and has three children and three grandchildren.

Brian Christian

Author of The Most Human Human, Algorithms to Live By, and The Alignment Problem

Brian Christian is the author of The Most Human Human, which was named a Wall Street Journal bestseller, a New York Times Editors’ Choice, and a New Yorker favorite book of the year. He is the author, with Tom Griffiths, of Algorithms to Live By, a #1 Audible bestseller, Amazon best science book of the year and MIT Technology Review best book of the year.

His third book, The Alignment Problem, was released in October of 2020.

Christian’s writing has been translated into nineteen languages, and has appeared in The New Yorker, The Atlantic, Wired, The Wall Street Journal, The Guardian, The Paris Review, and in scientific journals such as Cognitive Science. Christian has been featured on The Daily Show with Jon Stewart, Radiolab, and The Charlie Rose Show, and has lectured at Google, Facebook, Microsoft, the Santa Fe Institute, and the London School of Economics. His work has won several awards, including fellowships at Yaddo and the MacDowell Colony, publication in Best American Science & Nature Writing, and an award from the Academy of American Poets.

Born in Wilmington, Delaware, Christian holds degrees in philosophy, computer science, and poetry from Brown University and the University of Washington. A Visiting Scholar at the University of California, Berkeley, he lives in San Francisco.

Charis Thompson

Chancellor's Professor of Gender & Women's Studies at UC Berkeley

Charis Thompson is Chancellor’s Professor of Gender & Women’s Studies, and a former founding director of the Science, Technology, and Society Center at the University of California, Berkeley; and Professor of Sociology, London School of Economics and Political Science. Her current book in progress, Getting Ahead, focuses on minds, bodies, and emotions in an age of populism and technology elites. Getting Ahead is the third in her book series on technology and democracy. The first, Making Parents (2005), won the Rachel Carson Prize from the Society for the Social Study of Science, and examined gender, race, and agency in relation to reproductive technologies. The second book, Good Science (2013), examined the geopolitics and bioethics of pro-cures innovation economies. Thompson served on the Nuffield Council on Bioethics Working Group on Genome Editing, and currently serves on the World Economic Forum’s Global Technology Council on Technology, Values, and Policy, as well as UC Berkeley’s Stem Cell Research Oversight Committee, and the faculty advisory board of the Center for Race and Gender. She is a recipient of the Social Science Division Distinguished Teaching Award, and in 2017 received an Honorary Doctorate from the Norwegian Science and Technology University.

Dawn Song

Professor of EECS at UC Berkeley

Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Her research interest lies in deep learning and security. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, distributed systems security, applied cryptography, blockchain and smart contracts, to the intersection of machine learning and security. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the Faculty Research Award from IBM, Google and other major tech companies, and Best Paper Awards from top conferences in Computer Security and Deep Learning. She is a winner of the AMiner Most Influential Scholar Award, as the most cited scholar in Computer Security. She obtained her Ph.D. degree from UC Berkeley. Prior to joining UC Berkeley as a faculty, she was a faculty at Carnegie Mellon University from 2002 to 2007.

You can read more about Prof. Song’s work and accomplishments here.

Demian Pouzo

Associate Professor of Economics at UC Berkeley

Demian Pouzo is an associated professor in the Department of Economics at UC Berkeley. Demian joined the faculty at Berkeley in 2009 as an assistant professor after receiving his PhD in Economics from NYU. He also holds an MA and BA in Economics from Universidad Torcuato Di Tella (Argentina). Pouzo’s research interests include econometrics as well as other fields such as economic theory and macroeconomics.

Jacob Steinhardt

Assistant Professor of Statistics at UC Berkeley

Professor Steinhardt’s goal is to make the conceptual advances necessary for machine learning systems to be reliable and aligned with human values. This includes the following directions:

Robustness: How can we build models robust to distributional shift, to adversaries, to model mis-specification, and to approximations imposed by computational constraints? What is the right way to evaluate such models?

Reward specification and reward hacking: Human values are too complex to be specified by hand. How can we infer complex value functions from data? How should an agent make decisions when its value function is approximate due to noise in the data or inadequacies in the model? How can we prevent reward hacking–degenerate policies that exploit differences between the inferred and true reward?

Scalable alignment: Modern ML systems are often too large, and deployed too broadly, for any single person to reason about in detail, posing challenges to both design and monitoring. How can we design ML systems that conform to interpretable abstractions? How do we enable meaningful human oversight at training and deployment time despite the large scale? How will these large-scale systems affect societal equilibria?

These challenges require rethinking both the theoretical and empirical paradigms of ML. Theories of statistical generalization do not account for the extreme types of generalization considered above, and decision theory does not account for cases where the reward function is only approximate. Meanwhile, measuring empirical test accuracy on a fixed distribution is insufficient to analyze phenomena such as robustness to distributional shift.

For more information about Professor Steinhardt, please visit: https://www.stat.berkeley.edu/~jsteinhardt/

John Zysman

Professor Emeritus in Political Science at UC Berkeley

John Zysman is Professor Emeritus in the Department of Political Science. He received his B.A. from Harvard University and his Ph.D. from the Massachusetts Institute of Technology. He has written extensively on European and Japanese political economy, policy and corporate strategy.

His most recent articles include:

The Next Phase in the Digital Revolution: Abundant Computing, Platforms, Growth and Employment in February 2018 CACM
Intelligent Tools and Digital Platforms: Implications for Work and Employment in Intereconomics Review of European Economic Policy (Issue 6 2017)
The Rise of the Platform Economy In NAS Issues in Science and Technoogy, Spring 2016
Entrepreneurial Finance in the Era of Intelligent Tools and Digital Platforms: Implications and Consequences for Work

Recent books include:

The Third Globalization: Can Wealthy Countries Stay Rich Ed. with Dan Brezniz (Oxford University Press 2013)
Can Green Sustain Growth: From the Religion to the Reality To Sustainable Prosperity Ed. with Mark Huberty (Stanford Unversity Press. 2013)

Earlier books include:

The Highest Stakes: The Economic Foundations of the Next Security System (Oxford University Press, 1992)
Manufacturing Matters: The Myth of the Post-Industrial Economy (Basic Books, 1987)
Governments, Markets, and Growth: Finance and the Politics of Industrial Change (Cornell University Press, 1983)

Juliana Schroeder

Assistant Professor of Haas School of Business at UC Berkeley

Juliana Schroeder researches how people navigate their social worlds: first, how people form inferences about others’ mental states and mental capacities and, second, how these inferences influence their interactions.

Juliana’s research has been published in journals such as Journal of Personality and Social Psychology, Journal of Experimental Psychology, and Psychological Science. It has been featured by outlets such as the New York Times, Newsweek, NBC, and the Today Show, and has been funded by the National Science Foundation.

Juliana received her B.A. in psychology and economics from The University of Virginia. She received a M.A. in social psychology and advanced methods from the University of Chicago, and an M.B.A. from The Chicago Booth School of Business. Her Ph.D. is in Psychology and Business from the University of Chicago. Juliana is currently an Assistant Professor at the Berkeley Haas School of Business.

You can read more about Prof. Schroeder’s work and accomplishments here.

Ken Goldberg

Professor and Chair of the Industrial Engineering and Operations Research Department at UC Berkeley

Ken Goldberg is an artist, inventor, and UC Berkeley Professor focusing on robotics. He was appointed the William S. Floyd Jr Distinguished Chair in Engineering and serves as Chair of the Industrial Engineering and Operations Research Department. He has secondary appointments in EECS, Art Practice, the School of Information, and Radiation Oncology at the UCSF Medical School. Ken is Director of the CITRIS “People and Robots” Initiative and the UC Berkeley AUTOLAB where he and his students pursue research in machine learning for robotics and automation in warehouses, homes, and operating rooms. Ken developed the first provably complete algorithms for part feeding and part fixturing and the first robot on the Internet. Despite agonizingly slow progress, he persists in trying to make robots less clumsy. He has over 250 peer-reviewed publications and 8 U.S. Patents. He co-founded and served as Editor-in-Chief of the IEEE Transactions on Automation Science and Engineering. Ken’s artwork has appeared in 70 exhibits including the Whitney Biennial and films he has co-written have been selected for Sundance and nominated for an Emmy Award. Ken was awarded the NSF PECASE (Presidential Faculty Fellowship) from President Bill Clinton in 1995, elected IEEE Fellow in 2005 and selected by the IEEE Robotics and Automation Society for the George Saridis Leadership Award in 2016. He lives in the Bay Area and is madly in love with his wife, filmmaker and Webby Awards founder Tiffany Shlain, and their two daughters.

You can read more about Prof. Goldberg’s work and accomplishments here.

Lara Buchak

Associate Professor of Philosophy at UC Berkeley

Lara Buchak is an Associate Professor of Philosophy at UC Berkeley. She received her Ph.D. in Philosophy from Princeton University in 2009. Her primary research interests are in decision, game, and rational choice theory. Her book Risk and Rationality (Oxford 2013) concerns how an individual ought to take risk into account when making decisions. It vindicates the ordinary decision-maker from the point of view of even ideal rationality. She is currently working on applications of this work to central questions in ethics, including the question of how resources in a society should be distributed and the question of when one is allowed to take a risk that affects other people. Other topics she has written on include the relationship between assigning probability to a hypothesis and believing that hypothesis outright, and the conditions under which one ought to stop one’s search for evidence.

Mariano Florentino Cuéllar

Chair, Board of the Directors at the Center for Advanced Study in the Behavioral Sciences
Former Stanley Morrison Professor of Law at Stanford University

Justice Mariano-Florentino Cuéllar was nominated by Governor Jerry Brown and began serving on the California Supreme Court in January 2015. A naturalized U.S. citizen born in Northern Mexico, Cuéllar received a B.A. from Harvard magna cum laude, a J.D. from Yale Law School, and a Ph.D. in Political Science from Stanford. Before serving on the Court, he was the Stanley Morrison Professor of Law and Professor (by courtesy) of Political Science at Stanford University. A member of the Stanford faculty since 2001, Cuéllar has written books and articles on administrative law and legislation, criminal law, international law, cyberlaw, immigration, public health law, and the history of institutions. Between 2004 and 2015, Cuéllar also held leadership positions at Stanford’s Freeman Spogli Institute for International Studies. During his tenure leading the Institute and, earlier, its Center for International Security and Cooperation, Cuéllar grew the Institute’s faculty, expanded Stanford’s role in nuclear security research, launched university-wide initiatives on global poverty and cyber security, and broadened opportunities for student and faculty research abroad. You can read more about Justice Cuéllar’s work and accomplishments here or here.

Marion Fourcade

Professor of Sociology at UC Berkeley

Marion Fourcade received her PhD from Harvard University (2000) and taught at New York University and Princeton University before joining the Berkeley sociology department in 2003. A comparative sociologist by training and taste, she is interested in variations in economic and political knowledge and practice across nations. Her first book, Economists and Societies (Princeton University Press 2009), explored the distinctive character of the discipline and profession of economics in three countries. A second book, The Ordinal Society (with Kieran Healy), is under contract. This book investigates new forms of social stratification and morality in the digital economy. Other recent research focuses on the valuation of nature in comparative perspective; the moral regulation of states; the comparative study of political organization (with Evan Schofer and Brian Lande); the microsociology of courtroom exchanges (with Roi Livne); the sociology of economics, with Etienne Ollion and Yann Algan, and with Rakesh Khurana; the politics of wine classifications in France and the United States (with Rebecca Elliott and Olivier Jacquet). A final book-length project, Measure for Measure: Social Ontologies of Classification, will examine the cultural and institutional logic of what we may call “national classificatory styles” across a range of empirical domains.

Fourcade is also an Associate Fellow of the Max Planck-Sciences Po Center on Coping with Instability in Market Societies (Maxpo), and a past President of the Society for the Advancement of Socio-Economics (2016).

You can read more about Prof. Fourcade’s work and accomplishments here.

Michael Littman

Professor of Computer Science at Brown University

Michael Lederman Littman works mainly in reinforcement learning, but has done work in machine learning, game theory, computer networking, partially observable Markov decision process solving, computer solving of analogy problems and other areas. He is currently a professor of computer science at Brown University.

Before graduate school, Michael worked with Thomas Landauer at Bellcore and was granted a patent for one of the earliest systems for Cross-language information retrieval. Michael received his Ph.D. in computer science from Brown University in 1996. From 1996 to 1999, he was a professor at Duke University. During his time at Duke, he worked on an automated crossword solver PROVERB, which won an Outstanding Paper Award in 1999 from AAAI and competed in the American Crossword Puzzle Tournament. From 2000 to 2002, he worked at AT&T. From 2002 to 2012, he was a professor at Rutgers University; he chaired the department from 2009-12. In Summer 2012 he returned to Brown University as a full professor.

Moritz Hardt

Assistant Professor of EECS at UC Berkeley

Moritz Hardt is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Hardt investigates algorithms and machine learning with a focus on reliability, validity, and societal impact. After obtaining a PhD in Computer Science from Princeton University, he held positions at IBM Research Almaden, Google Research and Google Brain. Hardt is a co-founder of the Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) and a co-author of the forthcoming textbook “Fairness and Machine Learning”. He has received an NSF CAREER award, a Sloan fellowship, and best paper awards at ICML 2018 and ICLR 2017.

You can read more about Prof. Hardt’s work and accomplishments here.

Nika Haghtalab

Assistant Professor in the Department of Computer Science at Cornell University

Nika Haghtalab an Assistant Professor in the Department of Computer Science at Cornell University. She works broadly on the theoretical aspects of machine learning and algorithmic economics. She especially cares about developing a theory for machine learning that accounts for its interactions with people and organizations, and the wide range of social and economic limitations, aspiration, and behavior they demonstrate.

Prior to Cornell, Nika was a postdoctoral researcher at Microsoft Research, New England, in 2018-2019. She received her Ph.D. from the Computer Science Department of Carnegie Mellon University, co-advised by Avrim Blum and Ariel Procaccia. Her thesis titled Foundation of Machine Learning, by the People, for the People received the CMU School of Computer Science Dissertation Award (2018) and a SIGecom Dissertation Honorable Mention Award (2019).

Niko Kolodny

Professor of Philosophy at UC Berkeley

Niko Kolodny is Professor of Philosophy at UC Berkeley. He works in moral and political philosophy. He has written on the ethical significance of personal relationships and the nature of rationality, especially instrumental rationality. More recent papers focus on the value of democracy, the justification of the state, and the future of humanity. He has designed a new course for both philosophy and data science majors, “Moral Questions of Data Science,” which explores, among other things, the implications of replacing human judgment in social decision-making with statistical inference and automated algorithms.

Rediet Abebe

Junior Fellow at the Harvard Society of Fellows
Assistant Professor of Computer Science at the University of California, Berkeley

Rediet Abebe is a Junior Fellow at the Harvard Society of Fellows and an incoming Assistant Professor of Computer Science at the University of California, Berkeley. Abebe holds a Ph.D. in computer science from Cornell University as well as graduate degrees from Harvard University and the University of Cambridge.

Her research is in the fields of artificial intelligence and algorithms, with a focus on equity and justice concerns. She co-founded and co-organizes Mechanism Design for Social Good (MD4SG), a multi-institutional, interdisciplinary research initiative working to improve access to opportunity for historically disadvantaged communities. Abebe’s research has informed policy and practice at the National Institute of Health (NIH) and the Ethiopian Ministry of Education. Abebe has been honored in the MIT Technology Reviews’ 35 Innovators Under 35, ELLE, and the Bloomberg 50 list as a “one to watch.” She has presented her research in venues including National Academy of Sciences, the United Nations, and the Museum of Modern Art. Abebe co-founded Black in AI, a non-profit organization tackling representation and inclusion issues in AI. Her research is deeply influenced by her upbringing in her hometown of Addis Ababa, Ethiopia.

Vincent Corruble

Associate Professor at Sorbonne Université

Vincent Corruble received degrees in Engineering (Ecole Centrale de Lille, France), a M.S. in Systems Engineering (University of Virginia, 1992), a Ph.D. in Artificial Intelligence (UPMC, Paris 6, 1996). After postdocs in South Africa, in the US, and in the UK, he joined the faculty of UPMC (now Sorbonne Université), LIP6 laboratory.

He has contributed to the field of Machine Learning and Pattern Recognition, Machine discovery and Data Science, with applications to history of medicine and medical research. In the early 2000’s, he turned his attention to intelligent agents, especially learning agents and multi-agent systems, with a special interest for multi-agent reinforcement learning in complex systems. This found significant applications in the area of Game AI and later in city modeling and urban simulation. This also led to a focus on virtual characters and agent architectures, with emphasis on affective computing to model emotions and their interplay with personality and social interactions.

Lately Vincent Corruble has developed interest in AI safety, and more specifically on how agents and humans can develop safe cooperation and trust over time. In this context he visited CHAI during the summer of 2019. He has been member of the Multi-Agent Systems group at LIP6 (Sorbonne Université, Paris) since 2007.