Rebecca Willett is a Worah Family 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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Risi Kondor is an Associate Professor in the Department of Computer Science, Statistics, and the Computational and Applied Mathematics Initiative at the University of Chicago. He joined the Flatiron Institute in 2019 as a Senior Research Scientist with the Center for Computational Mathematics. His research interests include computational harmonic analysis and machine learning. Kondor holds a Ph.D. in Computer Science from Columbia University, an MS in Knowledge Discovery and Data Mining from Carnegie Mellon University, and a BA in Mathematics from the University of Cambridge. He also holds a diploma in Computational Fluid Dynamics from the Von Karman Institute for Fluid Dynamics and a diploma in Physics from Eötvös Loránd University in Budapest.

David Miller’s research focuses on answering open questions about the fundamental structure of matter. By studying the quarks and gluons -—the particles that comprise everyday protons and neutrons —produced in the energetic collisions of protons at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland, Miller conducts measurements using the ATLAS Detector that will seek out the existence of never-before-seen particles, and characterize the particles and forces that we know of with greater precision. Miller’s work into the properties and measurements of the experimental signatures of these quarks and gluons –or jets” –is an integral piece of the puzzle used in the recent discovery of the Higgs bosons, searches for new massive particles that decay into boosted top quarks, as well as the hints that the elusive quark-gluon-plasma may have finally been observed in collisions of lead ions.

Besides studying these phenomena, Miller has worked extensively on the construction and operation of the ATLAS detector, including the calorimeter and tracking systems that allow for these detailed measurements. Upgrades to these systems involving colleagues at Argonne National Laboratory, CERN, and elsewhere present an enormous challenge and a significant amount of research over the next several years. Miller is also working with state-of-the art high-speed electronics for quickly deciphering the data collected by the ATLAS detector.

Miller received his PhD from Stanford University in 2011 and his BA in Physics from the University of Chicago in 2005. He was a McCormick Fellow in the Enrico Fermi Institute from 2011-2013.

David Freedman is a Chair of the Department of Neurobiology and a Professor of Neurobiology and the Neuroscience Institute at the University of Chicago. He has a broad background in cognitive, systems, and computational neuroscience, with expertise in electrophysiological approaches for recording neuronal population activity in awake non-human primates trained to perform complex behavioral tasks, which require learning, memory, and decision-making. His research program also has a major focus on artificial intelligence (AI) approaches for studying neuroscience-related questions in artificial neural networks, and on designing novel biologically-inspired AI approaches. His research, supported by NIH, NSF, DOD, and private foundations, investigates the neuronal computations of higher-order perceptual and cognitive functions. Following graduate and postdoctoral training at MIT and Harvard Medical School, he established his laboratory at the University of Chicago in 2008, from which he has trained numerous graduate students and postdoctoral scholars that have successfully established their own independent research careers. His work has been recognized by the Troland Research Award from the National Academy of Sciences, the Vannevar Bush Faculty Fellowship from the Department of Defense, the NSF Career Award, and Faculty Fellowship Awards from the Sloan, McKnight, and Brain Research Foundations. In 2018, he received the University of Chicago Faculty Award for Excellence in Graduate Teaching and Mentoring.

Joshua Frieman is a Professor and Chair of the Department of Astronomy & Astrophysics at the University of Chicago, where he is a Senior Member of the Kavli Institute for Cosmological Physics. He is also a Distinguished Scientist and former Head of the Particle Physics Division at Fermilab. Frieman’s research spans theoretical and observational cosmology, including studies of the early universe, large-scale structure, gravitational lensing, supernovae, dark matter and dark energy. His research has increasingly relied on the use of machine learning techniques in the analysis of cosmic surveys, e.g., in estimating photometric redshifts, in automated artifact filtering of astronomical time-domain images, and in the discovery and modeling of strong gravitational lens systems. The co-author of over 600 publications, he was a co-founder and later Director of the Dark Energy Survey (DES), an international collaboration of 500 scientists from 25 institutions in 7 countries that carried out a six-year survey to map the Universe using a 570-megapixel camera it built for a 4-meter telescope in Chile. DES has cataloged several hundred million galaxies and discovered several thousand supernovae, yielding state-of-the-art measurements of cosmological parameters. Frieman previously played leadership roles in the Sloan Digital Sky Survey (SDSS) and led the SDSS-II Supernova Survey. Over 30 years, he has mentored over 40 postdocs and 20 graduate students at UChicago and Fermilab. He is active in outreach through public lectures (his “Probing the Dark Universe” has 7.5 million views on YouTube), K-12 school presentations, podcast interviews, and venues such as the World Science Festival. He is actively engaged in improving diversity and inclusion in STEM institutions.

Michael J. Franklin is the inaugural holder of the Liew Family Chair of Computer Science. An authority on databases, data analytics, data management and distributed systems, he also serves as senior advisor to the provost on computation and data science.

Previously, Franklin was the Thomas M. Siebel Professor of Computer Science and chair of the Computer Science Division of the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. There, he co-founded Berkeley’s Algorithms, Machines and People Laboratory (AMPLab), a leading academic big data analytics research center. The AMPLab won a National Science Foundation CISE “Expeditions in Computing” award, which was announced as part of the White House Big Data Research initiative in March 2012, and received support from over 30 industrial sponsors. AMPLab created industry-changing open source Big Data software including Apache Spark and BDAS, the Berkeley Data Analytics Stack. At Berkeley, he also served as an executive committee member for the Berkeley Institute for Data Science, a campus-wide initiative to advance data science environments.

An energetic entrepreneur in addition to his academic work, Franklin founded and became chief technology officer of Truviso, a data analytics company acquired by Cisco Systems. He serves on the technical advisory boards of various data-driven technology companies and organizations.

Franklin is a Fellow of the Association for Computing Machinery and a two-time recipient of the ACM SIGMOD (Special Interest Group on Management of Data) “Test of Time” award. His many other honors include the outstanding advisor award from Berkeley’s Computer Science Graduate Student Association. He received the Ph.D. in Computer Science from the University of Wisconsin in 1993, a Master of Software Engineering from the Wang Institute of Graduate Studies in 1986, and the B.S. in Computer and Information Science from the University of Massachusetts in 1983.

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Dan Nicolae obtained his Ph.D. in statistics from The University of Chicago and has been a faculty at the same institution since 1999, with appointments in Statistics (since 1999) and Medicine (since 2006). His research focus is on developing statistical and computational methods for understanding the human genetic variation and its influence on the risk for complex traits, with an emphasis on asthma related phenotypes. The current focus in his statistical genetics research is centered on data integration and system-level approaches using large datasets that include clinical and environmental data as well as various genetics/genomics data types: DNA variation, gene expression (RNA-seq), methylation and microbiome.

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David Uminsky joined the University of Chicago in September 2020 as a senior research associate and Executive Director of Data Science. He was previously an associate professor of Mathematics and Executive Director of the Data Institute at University of San Francisco (USF). His research interests are in machine learning, signal processing, pattern formation, and dynamical systems.  David is an associate editor of the Harvard Data Science Review.  He was selected in 2015 by the National Academy of Sciences as a Kavli Frontiers of Science Fellow. He is also the founding Director of the BS in Data Science at USF and served as Director of the MS in Data Science program from 2014-2019. During the summer of 2018, David served as the Director of Research for the Mathematical Science Research Institute Undergrad Program on the topic of Mathematical Data Science.

Before joining USF he was a combined NSF and UC President’s Fellow at UCLA, where he was awarded the Chancellor’s Award for outstanding postdoctoral research. He holds a Ph.D. in Mathematics from Boston University and a BS in Mathematics from Harvey Mudd College.

Victoria is the Director of the AI+Science Research Initiative where she will manage grants, pursue funding opportunities, and cultivate strategic partnerships. Victoria has experience applying for and managing federal grants, including training grants from NIH, and liaising with internal and external stakeholders through the Leadership Alliance. Prior to her current role, Victoria directed pathway programs to support the academic and career success for persons traditionally underrepresented in higher education. Victoria received her PhD in Ecology and Evolution from the University of Chicago in 2018 and her bachelor’s degree in Psychology and Hispanic Studies from Brown University.

Molly Long is the Research Program Administrator at the Data Science Institute after starting in May 2023 as the Research Team Coordinator. They are responsible for a variety of research-oriented events, educational programming, and outreach. Before moving to Chicago, they worked as a wildfire biologist with the U.S. Geological Survey in Boise, Idaho. Molly has experience in data science, classical violin performance, biological research, and college admissions. They earned a Bachelor of Arts in Biology and Music from Lawrence University in Appleton, Wisconsin.

Chris Redmond is an Open Source Software Engineer at the Data Science Institute and the AI & Science Initiative. He provides engineering support for research efforts in the natural sciences and helps social impact organizations expand their data science capabilities. Chris graduated from Haverford College with a BS in Mathematics and worked as an engineer at JPMorgan Chase before joining the institute.

Mark Schulze is the Special Events Manager for the Data Science Institute. He has deep experience as a meeting and event planner in the Chicagoland area. Prior to joining the University of Chicago, Mark was a senior executive meeting and event planner for 16 years at The Boeing Company with the Corporate office and Boeing Commercial Airplanes. While at Boeing he supported offices for the CEO, CFO, Board of Directors, and Executive Council members planning and executing conferences, meetings, and events, including annual meetings for Investors and Shareholders. Professional highlights include supporting international airshows at Farnborough, Paris, and Dubai, and the Invictus Games.

Marisa is the Assistant Director of The Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship Program, where she leads the program in several key functions, including faculty leadership support, developing partnerships across all Divisions and the National Laboratories, coordinating postdoctoral recruitment and hiring across academic departments, and managing program staff and operations. With deep expertise in program management, she has successfully led initiatives that connect scholars and researchers across disciplines, foster innovation, and build strong, supportive communities. Prior to this role, Marisa managed programming for fellows and postdoctoral scholars at the National Institute for Theory and Mathematics in Biology (NITMB) and launched their inaugural Summer Undergraduate Research Program.  She has also led all aspects of student experience, from recruitment through graduation, for the Medical Scientist Training Program, an interdisciplinary MD/PhD program at the University of Chicago. Marisa holds an M.Ed. in Counseling Psychology from the University of Missouri and a B.A. in Sociology from Spelman College.