Stuart Russell

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

Pieter Abbeel

Professor of EECS at UC Berkeley

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.

Joseph Halpern

Professor of Computer Science and Engineering at Cornell University

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.

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.

Mark Nitzberg

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

Martin Fukui

Assistant Director

Andrew Pereira

Operations Assistant

James Paul (J.P.) Gonzales

Assistant to Stuart Russell

Julia Kerley

Assistant to Andrew Critch

Rosalie Faddoul

Operation Assistant

Andrew Critch

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

Jonathan Stray

Senior Scientist

Alex Turner

Research Fellow

Erdem Biyik

Research Fellow

Erdem is a research fellow mentored by Stuart Russell and Anca Dragan. He has received his B.Sc. degree from Bilkent University, Turkey, in 2017; and Ph.D. degree from Stanford University in 2022 where he was advised by Dorsa Sadigh. His research interests lie in the intersection of robotics, artificial intelligence, machine learning and game theory. He is interested in enabling robots to actively learn from various forms of human feedback and designing robot policies to improve the efficiency of multi-agent systems both in cooperative and competitive settings. He also worked at Google as a research intern in 2021 where he adapted his active robot learning algorithms to recommender systems. He will join the University of Southern California as an assistant professor in 2023.

George Obaido

Research Fellow

Justin Svegliato

Research Fellow

Kimin Lee

Research Fellow

Nisan Stiennon

Research Fellow

Paria Rashidinejad

Research Fellow

Joar Skalse

First Year PhD Student at Oxford University

Antoni Lorente Martinez

First Year PhD Student at Universitat Autònoma de Barcelona & King’s College London

Noyuri Mima

Professor at Future University Hakodate

Ruairidh McLennan Battleday

Fourth Year PhD Student at Princeton University

Caroline Jeanmaire

PhD Student at Oxford University, Public Policy

Director of Strategic Research and Partnerships (2019 - 2021)

Karthika Mohan

Assistant Professor at Oregon State University

Postdoctoral Scholar (2017-2021)

Cody Wild

Machine Learning Engineer, Google Research

Research Engineer (2018-2021)

Cody Wild is a ML Engineer at Google Research, previously 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.

Dan Hendrycks

Fourth Year PhD student

Dorsa Sadigh

Assistant Professor of Computer Science and Electrical Engineering at Stanford University

PhD Student, Electrical Engineering and Computer Science (2012-2017)

Dylan Hadfield-Menell

Assistant Professor, MIT

PhD Student, Computer Science (2013-2020)

Jaime Fernandez Fisac

Assistant Professor of Electrical Engineering at Princeton University

PhD Student, Control, Intelligent Systems and Robotics (2013-2018)

Rohin Shah

Research Scientist, DeepMind

PhD Student, Artificial Intelligence (2014-2020)

Smitha Milli

Postdoc at Cornell Tech

PhD Student, Electrical Engineering and Computer Science, 2017-2022

Steven Wang

Master’s Student at ETH Zurich

Research Engineer (2018-2021)

Steven is a Master’s Student in Computer Science at ETH Zurich. At CHAI, he was an ML research engineer. 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.

Thanard Kurutach

AI/ML Research Scientist at Cruise

PhD Student, Computer Science (2016-2020)

Thomas Krendl Gilbert

Postdoc at Cornell Tech

PhD Student, Machine Ethics and Epistemology (2015-2021)

Vael Gates

Postdoc at Stanford HAI / CISAC

PhD Student, Computational Cognitive Science (2016-2021)

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.

Gillian Hadfield

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

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/

Jakob Foerster

Assistant Professor of Computer Science at the University of Toronto and the Vector Institute

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

Professor of Philosophy at Princeton University
Previously Associate Professor of Philosophy at UC Berkeley

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

Nika Haghtalab

Assistant Professor in the Department of Computer Science at Cornell University

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.

Rosie Campbell

Technical Program Manager, OpenAI

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.

Tom Lenaerts

Professor at the Université Libre de Bruxelles

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.

Wesley Holliday

Associate Professor of Philosophy at UC Berkeley

Adam Gleave

Fifth Year PhD student

Alyssa Li Dayan

Second Year PhD Student

Anand Siththaranjan

Second Year PhD Student

Arnaud Fickinger

Third Year PhD Student

Cassidy Laidlaw

Third Year PhD Student

Daniel Filan

Seventh Year PhD student

Erik Jenner

First Year PhD Student

George Matheos

4th year PhD Student

Hanlin Zhu

Second Year PhD Student

Jessy Lin

Second Year PhD Student

Jessy Lin is a second year PhD student at Berkeley AI Research. She is interested in building machine learning systems that work well in the dynamic, interactive settings of the real world.

Jessy graduated with a double-major in computer science / electrical engineering and philosophy from MIT. Previously Jessy worked on human-inspired AI with the Computational Cognitive Science Group and on human-in-the-loop machine translation at Lilt. At MIT she organized many seasons of HackMIT with the hackathon community. Jessy also spent a summer with the Natural Language Understanding group at Google Research NY, advised by David Weiss. Learn more at her website.

Johannes Treutlein

First Year PhD Student

Lawrence Chan

Fourth Year PhD student

Lawrence is a fourth year PhD in EECS at UC Berkeley Advised by Anca Dragan and Stuart Russell.

His research focuses on understanding the assumptions needed to apply preference inference techniques (such as inverse reinforcement learning) to recover good specifications for AI systems. Most recently, he studied the problem of assisting people that are themselves still learning about what they want. Lawrence’s work is helpful to our understanding of how a machine can learn what a human wants the machine to do.

Meir Friedenberg

Sixth Year PhD Student at Cornell University

Micah Carroll

Third Year PhD Student

Michael Dennis

Sixth Year PhD student

Rachel Freedman

Fourth Year PhD Student

Sam Toyer

Fifth Year PhD Student

Scott Emmons

Fourth Year PhD Student

Shreyas Kapur

PhD Student

Shreyas is a PhD student at Berkeley AI. He is interested in developing algorithms that can learn to construct rich, interpretable models of the world as quickly and robustly as humans do.

Shreyas got his undergraduate and Master's degrees at MIT, where he was part of the Computational Cognitive Science Group advised by Prof. Josh Tenenbaum, working on neurosymbolic techniques for building structured world models.

During his time as an undergrad, he spent summers at DeepMind, Waymo, and worked on research projects under Prof. Pattie Maes at the Fluid Interfaces Group. He also helped organize HackMIT.

Shreyas's Webpage

Yuxi Liu

Second Year PhD Student

Yuxi Liu is a second year PhD student with interests in mathematics, theoretical physics, philosophy, and AI. Before coming to Berkeley, Yuxi graduated from Australian National University with degrees in mathematics and theoretical physics. Learn more at yuxiliu1995.github.io.

Pulkit Verma

Third Year PhD Student at Arizona State University

Alexis Wan

Intern

Cynthia Chen

Intern

Cynthia is a rising senior at the University of Hong Kong. She is deeply passionate about developing AI systems that are beneficial to society and align with human values. Broadly, she is interested in improving robustness and reliability in decision-making algorithms.

Cynthia’s research at CHAI primarily focuses on improving imitation learning agents with representation learning methods. Prior to CHAI, Cynthia has conducted research in Automatic Machine Learning (AutoML) and had her paper published at a major top conference. She has also spent her undergrad years at Stanford University and Columbia University as a Visiting Student. Currently, Cynthia is among the Organizing Committee of SafeAI workshop at AAAI and AISafety workshop at IJCAI, where she hopes to help with growing the AI alignment research field. You can find out more about Cynthia at her website.

Dylan Cope

Intern

Jiahai Feng

Intern

Jun Shern Chan

Intern

Katie Lu

Intern

Lev McKinney

Intern

Lev is an undergraduate student at the University of Toronto majoring in Computer Science focusing on Artificial Intelligence. His research interests include model-based reinforcement learning and reward learning.At CHAI, Lev is working with Adam Gleave, on improving transfer in reward learning and providing new open source baselines to accelerate research in the field. Previously, Lev has presented work at the Neurips Deep RL workshop on training dynamics models with techniques from self-supervised learning.

Lukas Berglund

Intern

Mason Nakamura

Intern

Max Kaufmann

Intern

Michael Chen

Intern

Michael graduated from Georgia Tech in 2022 with a bachelor's degree in computer science. At CHAI, he is working with Scott Emmons to train language models to understand ethical scenarios. During his undergraduate studies, he researched the application of deep learning to the design of nanophotonic structures. He's passionate about AI alignment, effective altruism, and AI safety field-building. You can learn more about Michael through his LinkedIn or schedule a chat with him on Calendly.

Oliver Daniels-Koch

Intern

Pavel Czempin

Intern

Shivam Singhal

Intern

Yawen Duan

Intern

Alex Turner

Former Intern

Beth Barnes

Former Intern

Carlo Attubato

Intern

Charlotte Roman

Former Intern

Charlotte is a PhD student at the University of Warwick at the center for Mathematics for Real-world Systems. In her PhD research, she uses game theory to study how people respond to intelligent traffic systems such as route planning apps and traffic lights. She is also interested in cooperative multiagent reinforcement learning. At CHAI, Charlotte is working on finding safe beliefs that encourage cooperation in sequential social dilemmas. Her broad interests include AI safety, fairness, and bounded rationality.

Chris Cundy

Former Intern

David Lindner

Former Intern

David is a masters student at ETH Zurich working on reinforcement learning. Broadly, his research is concerned with building reliable intelligent systems that interact with the world. Recently he has been interested in learning robust reward functions from human feedback and ways to reason about the uncertainty an RL agent has about its reward function. At CHAI, David is working with Rohin Shah on inferring human preferences from the current state of the world in complex environments. In the past, he has done work in condensed matter physics, network optimization, computational social science and bias in machine learning. His website is davidlindner.me.

Dmitrii Krasheninnikov

Former Intern

Dmitrii is a second-year masters student at the University of Amsterdam, where he studies artificial intelligence. At CHAI, Dmitrii investigates ways to combine misspecified and conflicting reward functions obtained via different specification processes (e.g. hand-specification and inverse reinforcement learning). This is Dmitrii’s third time working at CHAI. Previously he worked with Rohin Shah on an algorithm that can learn human preferences from the current state of the world, and studied properties of the maximum causal entropy inverse RL algorithm with Andrew Critch.

Before CHAI Dmitrii worked on stability of deep learning algorithms as an intern at a small startup in Finland, and designed efficient algorithms for processing next-generation sequencing data at the Bioinformatics Institute in Russia.

You can learn more about Dmitrii at his personal website.

Eric Michaud

Former Intern

Eric is a fourth-year undergraduate at UC Berkeley studying mathematics. He is broadly interested in understanding why deep learning works so well, and in investigating the general principles that give rise to intelligence in physical systems.

As a CHAI intern, Eric is working with Adam Gleave to develop interpretability techniques for reward functions. He hopes that this work will help to detect when learned reward functions fail to reflect human preferences, without having to train a policy and without knowing a ground-truth reward.

Before working at CHAI, Eric interned with the Berkeley SETI Research Center and the Lawrence Livermore National Laboratory. You can learn more about him at his personal website.

Erik Jenner

Intern

Harry Giles

Former Intern

Harry is masters graduate in mathematics from Cambridge, UK. He is interested in statistics, and has experience working in finance. In particular, he is interested in reinforcement learning, and other topics of decision making under uncertainty and/or Constraints.

At CHAI, Harry has been working with Lawrence Chan on models for suboptimal RL policies, in particular Boltzmann rationality, for which he has also been working on the IRL problem. You can contact him through his website.

Jess Reidel

Former Intern

Joe Benton

Intern

Johannes Treutlein

Former Intern

Johannes is an incoming MSc student in computer science at the University of Toronto. Previously, he studied mathematics and computer science at the Technical University of Berlin and the University of Oxford. His research interests include multi-agent learning, game theory, bargaining and decision theory. At CHAI, he is working with Jakob Foerster and Michael Dennis on whether and how cheap talk can help with zero-shot coordination in cooperative multi-agent RL. Before that, Johannes was a research assistant at the machine learning lab at his university. His website is johannestreutlein.com.

Lauro Langosco di Langosco

Intern

Matthew Farrugia-Roberts

Intern

Matthew Rahtz

Former Intern

Michael McDonald

Former Intern

Michael completed his undergraduate studies at UC Berkeley with a double major in computer science and applied mathematics. Broadly, his interests cover value alignment in multi-step/multi-objective task scenarios and how robots can learn to efficiently aid humans in such scenarios.

As an undergraduate student, Michael researched under Professor Pieter Abbeel with Dylan Hadfield-Menell on task and motion planning algorithms - initially for a laundry-robot exhibit for the Victoria and Albert Museum in London meant to examine the feasibility and challenges of integrating robotic agents into everyday life. That work then served as foundation for designing imitation-learning algorithms for accomplishing multi-objective plans with normally intractable cost functions.

This coming autumn Michael will begin his master’s here at Berkeley in the EECS 5th year M.S. program, advised by Professor Anca Dragan.

Nathan Miller

Former Intern

Neel Alex

Former Intern

Neel Nanda

Intern

Oliver Richardson

PhD Student at Cornell University

Pedro Freire

Former Intern

Pedro is a M.Sc. student in Mathematics and Computer Science at Ecole Polytechnique, France. Some of his AI interests include (i) how to make agents better at processing higher-level feedback from humans, and (ii) the development of tools for RL interpretability - in particular to better understand how we can reduce variance and increase sample-efficiency in learning, and have agents with better guarantees of generalization.Other of his interests include interactive theorem proving, software tooling, game development, virtual reality, and human-computer interaction more broadly.

Previously, he worked at Google developing tools for monitoring and performing statistical analysis on the behavior of YouTube video classifiers, and worked at Facebook on the development of Augmented Reality software. He is currently working on benchmarks specialized in evaluating properties of Inverse Reinforcement Learning algorithms.

Rafael Albert

Intern

Sergei Volodin

Former Intern

Sergei is a Master student at the Swiss Federal Institute of Technology in Lausanne (EPFL) studying Computer Science and Neuroscience. Sergei did an internship at Google which resulted in a paper on causal models in reinforcement learning. Sergei co-founded the local AI safety group in Lausanne which does research and discussions. At CHAI, Sergei is working on adversarial policies with Adam Gleave.

Shlomi Hod

Former Intern

Shlomi is a data scientist and educator. He works on developing knowledge, practices, and tools to audit and mitigate the biases of real-world AI systems. He is the developer of Responsibly an open-source toolkit that brings techniques from AI bias and fairness research to practitioners. In CHAI, Shlomi works on developing a new method for increasing the interpretability of neural networks under the supervision of Daniel Filan. Besides that, Shlomi teaches various computing classes on Python programming and machine learning. His focus is on teaching a methodical and thoughtful process of programming problem-solving. He is one of the co-founders of the Israeli Cyber Education Center, where he led the design of national computing programs for kids and teenagers. For example, he initiated and managed the development of the cybersecurity Bagrut (the Israeli matriculation exam, the equivalent of A-level), and co-authored a Computer Network textbook in tutorial approach in Hebrew Before that, Shlomi worked as an algorithmic researcher and a research team leader in cybersecurity. He holds a B.Sc.. in pure Mathematics from Bar-Ilan University. Currently, he studies for an M.Sc. in Cognitive Systems at the University of Potsdam in Germany. In January 2020, Shlomi will start a Ph.D. in Computer Science in Boston Univesity.

Sören Mindermann

Former Intern

Stephen Casper

Former Intern

Stephen Casper is a rising senior at Harvard College pursuing a degree in statistics and a minor in mathematics. In addition to interning at CHAI under Daniel Filan, he is affiliated with the MIT/Harvard Center for Brains, Minds, and Machines and the Harvard Kreiman Lab. Broadly, his research interests align with the goals of making AI systems safe and aligned. More specifically, areas that he is interested in include understanding compressible features and generalization in deep networks, robust reinforcement learning, and decision theory.

Stephen’s work with CHAI is focusing on mechanistic transparency via modular interpretations of neural networks. However, he is also thinking about adversaries in contexts of deep reinforcement learning and decision theory.

In addition to being fascinated by questions in machine intelligence, Stephen’s primary motivations for working on AI alignment involve using it as a strategy for shaping the future impactfully for the better. He is excited about the Effective Altruism community and the paradigm of maximizing the good that one can do in the world.

For more information and contact info, please see his website.

Thomas Woodside

Intern

Yulong Lin

Intern