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SPEAKERS AND MODERATORS

David McAllester

Professor, Toyota Technological Institute at Chicago

David McAllester is a Professor of Computer Science at the Toyota Technological Institute at Chicago (TTIC), where his research areas include machine learning, the theory of programming languages, automated reasoning, AI planning, computer game playing (computer chess), computational linguistics and computer vision. For example, a 1991 paper on AI planning proved to be one of the most influential papers of the decade in that area, and a 1993 paper on computer game algorithms influenced the design of the algorithms used in the Deep Blue system that defeated Gary Kasparov. He served on the faculty of Cornell University for the academic year of 1987-1988 and served on the faculty of MIT from 1988 to 1995. He was a member of technical staff at AT&T Labs-Research from 1995 to 2002. He has been a fellow of the Association for the Advancement of Artificial Intelligence (AAAI) since 1997. From 2002 to 2017 he was Chief Academic Officer at the Toyota Technological Institute at Chicago (TTIC) where he is currently a Professor. He has received three "test of time" awards --- for a paper on systematic nonlinear planning at the AAAI conference, a paper on interval methods for constraint solving at the International Conference of Logic Programming, and a paper on the deformable part model in computer vision from the the conference on Computer Vision and Pattern Recognition (CVPR). He his B.S., M.S., and Ph.D. degrees from the Massachusetts Institute of Technology in 1978, 1979, and 1987 respectively.

Melanie Mitchell

Davis Professor, Santa Fe Institute

Melanie Mitchell is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).

Melanie originated the Santa Fe Institute's Complexity Explorer platform, which offers online courses and other educational resources related to the field of complex systems. Her online course “Introduction to Complexity” has been taken by over 25,000 students, and is one of Course Central’s “top fifty online courses of all time”.

Veronika Rockova

Professor of Econometrics and Statistics, University of Chicago

Veronika Rockova's research brings together statistics and machine learning to develop tools for learning from large datasets, particularly at the intersection of Bayesian and frequentist statistics, including: variable selection, uncertainty quantification, Bayesian nonparametrics, factor and dynamic models, and high-dimensional decision theory and inference. Her research was recognized by the prestigious CAREER Award for early-career faculty by the National Science Foundation in 2020, and she is on the Editorial Board of the Annals of Statistics.

Stuart Russell

Professor of Computer Science and Smith-Zadeh Professor in Engineering, University of California, Berkeley and Honorary Fellow, Wadham College, Oxford

Stuart Russell is a Professor of Computer Science at the University of California at Berkeley, holder of the Smith-Zadeh Chair in Engineering, and Director of the Center for Human-Compatible AI. He is a recipient of the IJCAI Computers and Thought Award and held the Chaire Blaise Pascal in Paris. In 2021 he received the OBE from Her Majesty Queen Elizabeth and gave the Reith Lectures. He is an Honorary Fellow of Wadham College, Oxford, an Andrew Carnegie Fellow, and 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, used in 1500 universities in 135 countries. His research covers a wide range of topics in artificial intelligence, with a current emphasis on the long-term future of artificial intelligence and its relation to humanity. He has developed a new global seismic monitoring system for the nuclear-test-ban treaty and is currently working to ban lethal autonomous weapons.

Rebecca Willett

Professor of Statistics and Computer Science & Director of AI at the Data Science Institute, University of Chicago

Rebecca Willett’s work in machine learning and signal processing reflects broad and interdisciplinary expertise and perspectives. She is known internationally for her contributions to the mathematical foundations of machine learning, large-scale data science, and computational imaging.

In particular, Rebecca studies methods to learn and leverage hidden structure in large-scale datasets; representing data in terms of these structures allows ML methods to produce more accurate predictions when data contain missing entries, are subject to constrained sensing or communication resources, correspond to rare events, or reflect indirect measurements of complex physical phenomena. These challenges are pervasive in science and technology data, and Rebecca's work in this space has had important implications in national security, medical imaging, materials science, astronomy, climate science, and several other fields. Her group has made contributions both in the mathematical foundations of signal processing and machine learning and in their application to a variety of real-world problems.

Alexandra Chouldechova

Estella Loomis McCandless Assistant Professor of Statistics and Public Policy at Heinz College, Carnegie Mellon University

Alexandra Chouldechova is the Estella Loomis McCandless Assistant Professor of Statistics and Public Policy at Carnegie Mellon University's Heinz College of Information Systems and Public Policy. Her research investigates questions of algorithmic fairness and accountability in data-driven decision-making systems, with a domain focus on criminal justice and human services. Her work has been supported through funding from organizations including the Hillman Foundation, the MacArthur Foundation, and the NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon. She is a member of the executive committee for the ACM Conference on Fairness, Accountability and Transparency (FAccT), and previously served as a Program Committee co-Chair for the conference.

Dr. Chouldechova is a 2020 Research Fellow with the Partnership on AI, where she is working on understanding factors that drive racial bias in algorithmic risk assessment tools being developed for use in pre-trial, parole and sentencing contexts. She is also a member of the Pittsburgh Task Force on Public Algorithms.

Iason Gabriel

Staff Research Scientist,
DeepMind

Iason Gabriel is a Senior Research Scientist at DeepMind where he works in the Ethics Research Team. His research focuses on the applied ethics of artificial intelligence, human rights, and the question of how to align technology with human values. Before joining DeepMind, Iason was a Fellow in Politics at St John’s College, Oxford. He holds a doctorate in Political Theory from the University of Oxford and spent a number of years working for the United Nations in post-conflict environments.

Melanie Jeske

Postdoctoral Fellow, Institute on the Formation of Knowledge, University of Chicago

Melanie Jeske is a postdoctoral fellow at the Institute on the Formation of Knowledge at the University of Chicago. Situated at the intersection of sociology of medicine and science and technology studies, her research explores social, political and ethical dimensions of knowledge systems, emergent biotechnologies, and expertise. Her work across these areas has been published in journals including Science, Technology & Human Values, Social Science & Medicine, BioSocieties, PLOS ONE, and Engaging Science, Technology, and Society. She obtained her PhD in Sociology at the University of California, San Francisco (UCSF). She also holds a master of science degree in Science, Technology, and Society from Drexel University.

Karrie Karahalios

Professor in the Department of Computer Science, University of Illinois at Urbana-Champaign

Karrie Karahalios is noted for her work on the impact of computer science on people and society, analyses of social media, and algorithm auditing. She is co-founder of the Center for People and Infrastructures at the University of Illinois at Urbana-Champaign.

Andre Uhl

Postdoctoral Researcher at the Rank of Instructor, Institute on the Formation of Knowledge, University of Chicago

Andre Uhl is a scholar of critical AI studies whose work draws new connections between media arts and sciences and tech policy and activism. Before joining the Institute on the Formation of Knowledge, he earned his PhD in Art, Film, and Visual Studies with a Secondary Field in Science, Technology, and Society from Harvard University.

Sarah Brayne

Associate Professor of Sociology, University of Texas at Austin

Sarah Brayne's first book, Predict and Surveil: Data, Discretion, and the Future of Policing (Oxford University Press), draws on ethnographic research with a large, urban police department to understand how law enforcement uses predictive analytics and new surveillance technologies to allocate resources, identify suspects, and conduct investigations. She demonstrates how the adoption of big data analytics transforms organizational practices and how the police themselves respond to these new data-driven strategies. In previous research, she developed a theory of "system avoidance," using survey data to test the relationship between criminal legal contact and involvement in medical, financial, labor market, and educational institutions. Brayne's research has appeared in the American Sociological Review, Social Problems, Law and Social Inquiry, the Annual Review of Law and Social Science, and the Annual Review of Criminology, and has received awards from the American Sociological Association, the Law and Society Association, and the American Society of Criminology.

Brayne is the founder and director of the Texas Prison Education Initiative, a group of faculty and students who volunteer teach college classes in prisons in Texas. She has been teaching college classes in prisons since 2012. Prior to joining the faculty at UT-Austin, Brayne was a Postdoctoral Researcher at Microsoft Research. She received her Ph.D. in Sociology and Social Policy from Princeton University.

Ishanu Chattopadhyay

Assistant Professor, University of Chicago

Ishanu Chattopadhyay’s research focuses on the theory of unsupervised machine learning and the interplay of stochastic processes and formal language theory in exploring the mathematical underpinnings of the question of inferring causality from data. His most visible contributions include the algorithms for data smashing, inverse Gillespie inference, and nonparametric nonlinear and zero-knowledge implementations of Granger causal analysis that have crucial implications for biomedical informatics, data-enabled discovery in biomedicine, and personalized precision health care. His current work focuses on analyzing massive clinical databases of disparate variables to distill patterns indicative of hitherto unknown etiologies, dependencies, and relationships, potentially addressing the daunting computational challenge of scale and making way for ab initio and de novo modeling in an age of ubiquitous data. Chattopadhyay received an MS and PhD in mechanical engineering, as well as an MA in mathematics, from the Pennsylvania State University. He completed his postdoctoral training and served as a research associate in the Department of Mechanical Engineering at Penn State. He also held a postdoctoral fellowship simultaneously at the Department of Computer Science and the Sibley School of Mechanical and Aerospace Engineering at Cornell University.

Steven Feldstein

Senior Fellow, Carnegie’s Democracy, Conflict, and Governance Program

Feldstein has published research on how artificial intelligence is reshaping repression, the geopolitics of technology, China’s role in advancing digital authoritarianism, and the changing patterns of internet shutdowns. He also released a global AI surveillance index to track the proliferation of advanced big data tools used by governments.

Julia Koschinsky

Executive Director and Senior Research Associate, Center for Spatial Data Science, University of Chicago

Julia Koschinsky is the Executive Director and Senior Research Associate of the Center for Spatial Data Science at the University of Chicago and has been part of the GeoDa team for over 18 years. She has been conducting and managing research funded through federal awards of over $8 million to gain insights from the spatial dimensions of urban challenges in housing, health, and the built environment.

Dewey Murdick

Director, Georgetown’s Center for Security and Emerging Technology

Dewey's research interests include connecting research and emerging technology to future capabilities, emerging technology forecasting, strategic planning, research portfolio management, and policymaker support. He holds a Ph.D. in Engineering Physics from the University of Virginia and a B.S. in Physics from Andrews University.

Ted Chiang

Writer

Ted Chiang’s fiction has won four Hugo, four Nebula, and four Locus awards, and has been featured in The Best American Short Stories. His debut collection, Stories of Your Life and Others, has been translated into twenty-one languages, and the piece "Story of Your Life" was the basis of the 2016 Hollywood film Arrival. His work touches on themes of philosophy, science, technology, spirituality, and humanity. He was born in Port Jefferson, New York, and currently lives near Seattle, Washington.

Alexei (Alyosha) Efros

Professor in the Department of Electrical Engineering and Computer Sciences, UC Berkeley

The central goal of Alexei Efros' research is to use vast amounts of unlabelled visual data to understand, model, and recreate the visual world around us. His research has been mainly in data-driven computer vision, as well as its projection onto computer graphics and computational photography. In the last five years, his lab has been at the forefront of reviving self-supervised learning. Other interests include human vision, visual data mining, robotics, and the applications of computer vision to the visual arts and the humanities. He also enjoys making scientific bets.

Daniel Rockmore

William H. Neukom 1964 Distinguished Professor of Computational Science, Director of the Neukom Institute for Computational Science, and Associate Dean for the Sciences, Dartmouth College

Rockmore's research Interests are complex systems, network analysis, machine learning, cultural evolution, and group theoretic transforms.

Jason Salavon

Associate Professor, University of Chicago

Jason Salavon is an Associate Professor of Visual Arts at the University of Chicago. He uses custom computer software processes of his own design to manipulate and reconfigure preexisting media and data to create new visual works. The final compositions are exhibited as art objects, such as photographic prints and video installations, while others exist in a real-time software context. Born in Indiana, raised in Texas, and based in Chicago, Salavon earned his MFA at The School of the Art Institute of Chicago and his BA from The University of Texas at Austin.

Avery Slater

Assistant Professor of Twentieth- and Twenty-First Century American Literature, University of Toronto St. George

Avery Slater’s teaching focuses on twentieth- and twenty-first century literature in a global context. Her research investigates the re-conceptualization of human and nonhuman forms of language following the rise of information and computational technologies, with specific attention to the history of artificial intelligence and machine learning. Her book project Apparatus Poetics explores how mid-twentieth-century poets revise and reinvent modernist theories of poetic process in response to emerging technologies of language (computation, artificial intelligence, machine translation, information theory). She spent the academic year of 2016-2017 at the University of Pennsylvania’s Penn Humanities Forum, researching the literary and philosophical contexts of postwar machine translation.

James Evans

Professor, University of Chicago, Santa Fe Institute

James Evans is Professor of Sociology, Director of Knowledge Lab, and Faculty Director of Computational Social Science at the University of Chicago. His research uses large-scale data, machine learning and generative models to understand how collectives think and what they know. This involves inquiry into the emergence of ideas, shared patterns of reasoning, and processes of attention, communication, agreement, and certainty. Thinking and knowing collectives like science, Wikipedia or the Web involve complex networks of diverse human and machine intelligences, collaborating and competing to achieve overlapping aims. Evans' work connects the interaction of these agents with the knowledge they produce and its value for themselves and the system.

Maryellen L. Giger

A.N. Pritzker Distinguished Service Professor of Radiology, Committee on Medical Physics, and the College, University of Chicago

For over 30 years, Maryellen Giger has conducted research on computer-aided diagnosis, including computer vision, machine learning, and deep learning, in the areas of breast cancer, lung cancer, prostate cancer, lupus, and bone diseases.

Her research in computational image-based analyses of breast cancer for risk assessment, diagnosis, prognosis, and response to therapy has yielded various translated components, and she is now using these image-based phenotypes, i.e., these “virtual biopsies” in imaging genomics association studies for discovery.

Ross King

Professor of Machine Intelligence, University of Cambridge and Chalmers Institute of Technology

Ross D. King is a professor at the University of Cambridge and Chalmers Institute of Technology, specializing in the interface between computer science and science. He is known for the "robot scientist" integration of artificial intelligence and robotics into biomedical laboratory research. In 2009, his lab's invention, "Adam", was featured as one of TIME magazine's Top 10 Scientific Discoveries.

Sendhil Mullainathan

Roman Family University Professor of Computation and Behavioral Science, University of Chicago

Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth. His current research uses machine learning to understand complex problems in human behavior, social policy, and especially medicine, where computational techniques have the potential to uncover biomedical insights from large-scale health data. He currently teaches a course on Artificial Intelligence.

Juan de Pablo

Liew Family Professor of Molecular Engineering, University of Chicago

Juan de Pablo is the Liew Family Professor in Molecular Engineering at the University of Chicago’s Pritzker School for Molecular Engineering (PME), Executive Vice President for Science, Innovation, National Laboratories, and Global Initiatives, and Senior Scientist at Argonne National Laboratory.

Much of Juan de Pablo’s work entails conducting supercomputer simulations to understand and design new materials from scratch and to find applications for them. He is a leader of simulations of polymeric materials, including DNA dynamics — how DNA molecules arrange and organize themselves and interact with other DNA molecules. He also studies protein aggregation and its poorly understood relationship to various diseases, including type II diabetes and neurodegenerative disorders.