Anima Anandkumar is Bren Professor at Caltech and Senior Director of AI Research at NVIDIA. She received her B.Tech from the Indian Institute of Technology Madras, and her Ph.D. from Cornell University. She did her postdoctoral research at MIT and an assistant professorship at the University of California Irvine. She has received several honors such as the IEEE fellowship, Alfred. P. Sloan Fellowship, NSF Career Award, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum’s Expert Network.

Prof. Elizabeth Barnes is a Professor of Atmospheric Science at Colorado State University. She joined the CSU faculty in 2013 after obtaining dual B.S. degrees (Honors) in Physics and Mathematics from the University of Minnesota, obtaining her Ph.D. in Atmospheric Science from the University of Washington, and spending a year as a NOAA Climate & Global Change Fellow at the Lamont-Doherty Earth Observatory. Professor Barnes’ research is largely focused on climate variability and change and the data analysis tools used to understand them. Her current work is heavily focused on developing and applying explainable machine learning methods to better understand and anticipate climate impacts in the coming decades to support humanity as we navigate our turbulent future. Dr. Barnes received the American Geophysical Union (AGU) Macelwane Medal and became a Fellow of the AGU in 2021, received the AGU Turco Lectureship in Future Horizons in Climate Science in 2020, and the American Meteorological Society Clarence Leroy Meisinger Early Career Award in 2020.

John Chodera is an experienced computational chemist, and an Associate Member at Sloan Kettering Institute for Cancer Research (MSKKC). His research combines the disciplines of statistical mechanics, biomolecular simulation, and biophysical measurements to develop quantitative models for predicting and understanding how small molecules selectively bind biomolecular targets, how binding modulates conformation and function, and how mutations can perturb drug binding affinities to cause drug resistance.

John has authored over 75 articles in peer-reviewed journals, which have collectively received over 12,500 literature citations. John has also received numerous awards including the BIH Einstein Visiting Fellowship, Silicon Therapeutics Open Science Fellowship, Louis V. Gerstner Young Investigator Award, QB3-Berkeley Distinguished Postdoctoral Fellowship, IBM Predoctoral Fellowship, the Frank M. Goyan Award for outstanding work in Physical Chemistry at UCSF, and a HHMI Predoctoral Fellowship.

John holds a B.S. in Biology from Caltech and a Ph.D. in Biophysics from the University of California, San Francisco. He completed postdoctoral studies at Stanford University and at University of California, Berkeley as a QB3 Fellow.

As the Executive Vice President for Science, Innovation, National Laboratories, and Global Initiatives, Juan de Pablo helps drive and support the expanding reach of the University’s science, technology, and innovation efforts, along with their connection to policy and industry. He identifies and shapes emerging strategic scientific and technological initiatives, and provides oversight of entrepreneurship and innovation activities at the University’s Polsky Center for Entrepreneurship and Innovation. He also works with faculty, deans, and administrators to build global academic partnerships and international research collaborations while overseeing the University’s international centers. Juan de Pablo provides leadership for the University’s stewardship of two U.S. Department of Energy National Laboratories — Argonne and Fermilab — as institutions to advance science and technology in support of the nation’s interest. He collaborates with other leaders in research and innovation to build programs and links between and among the national laboratories and the University, as well as the Marine Biological Laboratory. A prominent molecular engineer, de Pablo focuses his research on polymers, biological macromolecules such as proteins and DNA, glasses, and liquid crystals, a diverse class of materials widely used in many fields of engineering.

I lead an interdisciplinary computational and theoretical research group working on materials self-assembly, biomolecular simulation, viral dynamics, and vaccine design. My doctoral training provided me with expertise in molecular simulation, statistical mechanics, and machine learning, in which I developed new nonlinear machine learning approaches to study the conformations and dynamics of proteins, polymers, and confined water. During my post-doctoral fellowship, I acquired knowledge and skills in immunology and viral dynamics, and developed new computational tools for structure-free prediction of antibody binding sites, and the computational design of HIV vaccines using statistical mechanical principles.

Since establishing my independent research program in 2012, I have combined these expertise to establish a dynamic research program in computational materials science and computational virology for which I have attracted over $2.9M in federal research funding, established a strong publication record (60+ papers) in leading journals, and have been recognized with a number of national awards including a 2018 Royal Society of Chemistry Molecular Systems Design and Engineering Emerging Investigator Award, 2017 Dean’s Award for Excellence in Research, 2016 AIChE CoMSEF Young Investigator Award, 2015 ACS Outstanding Junior Faculty Award, 2014 ACS Petroleum Research Fund Doctoral New Investigator Award, 2013 NSF CAREER Award, and I was named the 2013 Institution of Chemical Engineers North America “Young Chemical Engineer of the Year”. I am engaged and active within my professional organization serving on the AIChE Area 1a Programming Committee and as CoMSEF Liaison Director, and in organizing multiple scientific sessions at our national meetings. In addition to independent theoretical work, my research interests lead naturally to close collaboration with experimentalists and clinicians, teaching me the power of mutually reinforcing theoretical and experimental work and the importance of effective communication, planning, budgeting, teamwork and leadership.

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Professor Morteza (Mory) Gharib researches conventional fluid dynamics and aeronautics including vortex dynamics, active and passive flow control, nano/micro fluid dynamics, autonomous flight and underwater systems, as well as advanced flow-imaging diagnostics. The Gharib group is further interested in biomechanics and medical engineering via the study of fluid dynamics within the human cardiovascular system and opthamology, as well as the development of medical devices.

Prof. Tommi S. Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT’s Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society (IDSS), as well as an investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research focuses on machine learning, statistical inference, and methods for designing principled, interpretable solutions to large scale estimation problems involving incomplete data sources. His applied research includes design and optimization of molecules and reactions for drug development, and causal modeling in the context of strategic interactions.

Anshul Kundaje is an Assistant Professor of Genetics and Computer Science at Stanford University. His primary research area is large-scale computational regulatory genomics. The Kundaje lab specializes in developing statistical and machine learning methods for large-scale integrative analysis of heterogeneous, high-throughput functional genomic and genetic data, learning predictive regulatory network models across individuals, cell-types and species, and improving detection and interpretation of natural and disease-associated genetic variation.

Prof. Jennifer Listgarten is a Professor in the Department of Electrical Engineering and Computer Science, and Center for Computational Biology, at the University of California, Berkeley. She is also a member of the steering committee for the Berkeley AI Research (BAIR) Lab, and a Chan Zuckerberg investigator. From 2007 to 2017 she was at Microsoft Research. She completed her Ph.D. in the machine learning group in the Department of Computer Science at the University of Toronto, located in her hometown. She has two undergraduate degrees, one in Physics and one in Computer Science, from Queen’s University in Kingston, Ontario. Jennifer’s research interests are broadly at the intersection of machine learning, applied statistics, molecular biology and science.

Angela V. Olinto is the Dean of the Physical Sciences Division of the University of Chicago since 2018 and is the Albert A. Michelson Distinguished Service Professor in the Department of Astronomy and Astrophysics, the Enrico Fermi Institute, the Kavli Institute for Cosmological Physics, and the College.

Olinto is a leader of the new field of astroparticle physics. Her best-known contributions include the study of compact stars made of quarks, primordial natural inflation, the origins and evolution of cosmic magnetic fields, and the origin of the highest energy cosmic rays, gamma-rays, and neutrinos arriving on Earth from distant sources. She was a founding member of the Pierre Auger Observatory and currently leads the POEMMA (Probe Of Extreme Multi-Messenger Astrophysics) space mission and the EUSO (Extreme Universe Space Observatory) on a super pressure balloon (SPB) missions, all designed to discover the origin of the highest energy particles to study their sources and their interactions.

She is a fellow of the American Physical Society and of the American Association for the Advancement of Science, a member of American Academy of Arts & Sciences and National Academy of Sciences, a trustee of the Toyota Technological Institute at Chicago, and a member of the Argonne National Laboratory Board of Governors, and has served on many advisory committees for the National Academy of Sciences, the Department of Energy, the National Science Foundation, and the National Aeronautics and Space Administration. She received the Chaire d’Excellence Award of the French Agence Nationale de Recherche in 2006, the Llewellyn John and Harriet Manchester Quantrell Award for Excellence in Undergraduate Teaching in 2011, and the Faculty Award for Excellence in Graduate Teaching in 2015 at the University of Chicago.

John Preskill is the Richard P. Feynman Professor of Theoretical Physics at the California Institute of Technology, and Director of the Institute for Quantum Information and Matter at Caltech. Preskill received his Ph.D. in physics in 1980 from Harvard, and joined the Caltech faculty in 1983. Preskill began his career in particle physics and cosmology, but now his main research area is quantum information science. He’s interested in how to build and use quantum computers, and in how our deepening understanding of quantum information can illuminate issues in fundamental physics. Preskill is a member of the National Academy of Sciences, a fellow of the American Physical Society, and a two-time recipient of the Associated Students of Caltech Teaching Award. He has mentored more than 60 Ph.D. students and more than 60 postdoctoral scholars at Caltech, many of whom are now leaders in their research areas. You can follow him on Twitter @preskill.

Prof. Mike Pritchard is an Associate Professor At UC Irvine, and the Director Of Climate Simulation Research At NVIDIA. His research focuses on advancing understanding of how the planetary water cycle works, and how it may change in the future, focusing especially on cloud physics and moist convection processes. His tools are a blend of next-generation global atmospheric simulation algorithms, theoretical climate dynamics, and high-performance computing. Projects are guided by the problems of interest, and are intentionally explorative of new potentially breakthrough physical algorithms that attempt to avoid traditional approximations of cloud physics in global climate simulations. Lately this has meant significant exploration of emerging tools in the data sciences such as deep machine learning for physical process emulation and neural-network assisted dynamical inquiry. He now holds a partial industry appointment: In July 2022 , he began leading a new research group at NVIDIA as their Director of Climate Simulation Research, helping lead their Earth-2 climate simulation initiative.

Samantha Riesenfeld is an Assistant Professor of Molecular Engineering and of Genetic Medicine, a member of the Committee on Immunology, an Associate Member of the Comprehensive Cancer Center, and co-director of the new Computational and Systems Immunology PhD track in Immunology and Molecular Engineering. She leads an interdisciplinary research program focused on developing and applying genomics-based machine learning approaches to investigate the cellular components, transcriptional circuitry, and dynamics underlying complex biological systems, with a special interest in inflammatory immune responses and solid tumor cancer.

Dr. Gavin Schmidt is the Director of GISS and Principal Investigator for the GISS ModelE Earth System Model, with primary interests in understanding past, present and future climate and the impacts of multiple drivers of climate change, including solar irradiance, atmospheric chemistry, aerosols, and greenhouse gases. In 2021 he served as the acting Senior Climate Adviser to the NASA Administrator. He has over 150 peer-reviewed publications and was the author with Joshua Wolfe of “Climate Change: Picturing the Science” in 2009. In 2011 he was the inaugural recipient of the American Geophysical Union (AGU) Climate Communication Prize and is a fellow of the AGU and American Association for the Advancement of Science. His 2014 TED Talk on climate modeling has been viewed over a million times.

Prof. Philia Shanahan’s research interests are focussed around theoretical nuclear and particle physics. In particular, she works to understand the structure and interactions of hadrons and nuclei from the fundamental (quark and gluon) degrees of freedom encoded in the Standard Model of particle physics. Shanahan’s recent work has focused in particular on the role of gluons, the force carriers of the strong interactions described by Quantum Chromodynamics (QCD), in hadron and nuclear structure; using analytic tools and high performance supercomputing, she recently achieved the first calculation of the gluon structure of light nuclei, making predictions which will be testable in new experiments proposed at Jefferson National Accelerator Facility and at the planned Electron-Ion Collider. She has also undertaken extensive studies of the role of strange quarks in the proton and light nuclei which sharpen theory predictions for dark matter cross-sections in direct detection experiments. To overcome computational limitations in QCD calculations for hadrons and in particular for nuclei, Prof. Shanahan is pursuing a program to integrate modern machine learning techniques in computational nuclear physics studies.

Jesse Thaler is a theoretical particle physicist who fuses techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. His current research is focused on maximizing the discovery potential of the Large Hadron Collider (LHC) through new theoretical frameworks and novel data analysis techniques. Prof. Thaler is an expert in jets, which are collimated sprays of particles that are copiously produced at the LHC, and he studies the substructure of jets to enhance the search for new phenomena and illuminate the dynamics of gauge theories. Prof. Thaler joined the MIT Physics Department in 2010, and is currently a Professor in the Center for Theoretical Physics. In 2020, he became the inaugural Director of the NSF Institute for Artificial Intelligence and Fundamental Interactions.

Caroline Uhler is a full professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society at MIT. In addition, she is a core institute member at the Broad, where she co-directs the Eric and Wendy Schmidt Center. She holds an MSc in mathematics, a BSc in biology, and an MEd all from the University of Zurich. She obtained her PhD in statistics from UC Berkeley in 2011 and then spent three years as an assistant professor at IST Austria before joining MIT in 2015. She is a Simons Investigator, a Sloan Research Fellow, and an elected member of the International Statistical Institute. In addition, she received an NIH New Innovator Award, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation. Her research lies at the intersection of machine learning, statistics, and genomics, with a particular focus on causal inference, representation learning, and gene regulation.

Vincenzo Vitelli’s research interests lie in condensed matter theory broadly defined. Recent work encompasses active matter, machine learning, metamaterials, topological insulators, hydrodynamics, dynamical systems, liquid crystals, granular media, glasses and polymers. Often the rich phenomenology of these many body systems arises from the interplay between strong non-linearities, disorder and physics far from equilibrium that I explore using analytical and numerical tools in close collaboration with experimentalists.

Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointments include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate Prof. Gerard ‘t Hooft.

Rebecca Willett is a Professor of Statistics and Computer Science at the University of Chicago. She completed her PhD in Electrical and Computer Engineering at Rice University in 2005 and was an Assistant then tenured Associate Professor of Electrical and Computer Engineering at Duke University from 2005 to 2013. She was an Associate Professor of Electrical and Computer Engineering, Harvey D. Spangler Faculty Scholar, and Fellow of the Wisconsin Institutes for Discovery at the University of Wisconsin-Madison from 2013 to 2018.  Prof. Willett received the National Science Foundation CAREER Award in 2007, was a member of the DARPA Computer Science Study Group 2007-2011, and received an Air Force Office of Scientific Research Young Investigator Program award in 2010. Prof. Willett has also held visiting researcher positions at the Institute for Pure and Applied Mathematics at UCLA in 2004, the University of Wisconsin-Madison 2003-2005, the French National Institute for Research in Computer Science and Control (INRIA) in 2003, and the Applied Science Research and Development Laboratory at GE Medical Systems (now GE Healthcare) in 2002. Her research interests include network and imaging science with applications in medical imaging, wireless sensor networks, astronomy, and social networks. She is also an instructor for FEMMES (Females Excelling More in Math Engineering and Science; news article here) and a local exhibit leader for Sally Ride Festivals. She was a recipient of the National Science Foundation Graduate Research Fellowship, the Rice University Presidential Scholarship, the Society of Women Engineers Caterpillar Scholarship, and the Angier B. Duke Memorial Scholarship.

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