Dr. Allyson Ettinger’s research is focused on language processing in humans and in artificial intelligence systems, motivated by a combination of scientific and engineering goals. For studying humans, her research uses computational methods to model and test hypotheses about mechanisms underlying the brain’s processing of language in real time. In the engineering domain, her research uses insights and methods from cognitive science, linguistics, and neuroscience in order to analyze, evaluate, and improve natural language understanding capacities in artificial intelligence systems. In both of these threads of research, the primary focus is on the processing and representation of linguistic meaning.

Chenhao Tan is an assistant professor at the Department of Computer Science and the UChicago Data Science Institute. His main research interests include language and social dynamics, human-centered machine learning, and multi-community engagement. He is also broadly interested in computational social science, natural language processing, and artificial intelligence.

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Nick Feamster is Neubauer Professor in the Department of Computer Science and the College and the Faculty Director of Research for the Data Science Institute. He researches computer networking and networked systems, with a particular interest in Internet censorship, privacy, and the Internet of Things. His work on experimental networked systems and security aims to make networks easier to manage, more secure, and more available.

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I am an Assistant Professor of Computer Science at the University of Chicago. I founded and direct 3DL (threedle! ), a group of enthusiastic researchers passionate about 3D, machine learning, and visual computing. I obtained my Ph.D. in 2021 from Tel Aviv University under the supervision of Daniel Cohen-Or and Raja Giryes.

My research is focused on building artificial intelligence for 3D data, spanning the fields of computer graphics, machine learning, and computer vision. Deep learning, the most popular form of artificial intelligence, has unlocked remarkable success on structured data (such as text, images, and video), and I am interested in harnessing the potential of these techniques to enable effective operation on unstructured 3D geometric data.

We have developed a convolutional neural network designed specifically for meshes, and also explored how to learn from the internal data within a single shape (for surface reconstruction, geometric texture synthesis, and point cloud consolidation) – and I am interested in broader applications related to these areas. Additional research directions that I am aiming to explore include: intertwining human and machine-based creativity to advance our capabilities in 3D shape modeling and animation; learning with less supervision, for example to extract patterns and relationships from large shape collections; and making 3D neural networks more “interpretable/explainable”.

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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I am an assistant professor of Statistics and Data Science at the University of Chicago and a research scientist at Google Cambridge. My recent work revolves around the intersection of machine learning and causal inference, as well as the design and evaluation of safe and credible AI systems. Other noteable areas of interests include network data, and the foundations of learning and statistical inference.

I was previously a Distinguished Postdoctoral Researcher in the department of statistics at Columbia University, where I worked with the groups of David Blei and Peter Orbanz. I completed my Ph.D. in statistics at the University of Toronto, where I was advised by Daniel Roy. In a previous life, I worked on quantum computing at the University of Waterloo. I won a number of awards, including the Pierre Robillard award for best statistics thesis in Canada.

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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I am an assistant professor of Statistics and Data Science at the University of Chicago and a research scientist at Google Cambridge. My recent work revolves around the intersection of machine learning and causal inference, as well as the design and evaluation of safe and credible AI systems. Other noteable areas of interests include network data, and the foundations of learning and statistical inference.

I was previously a Distinguished Postdoctoral Researcher in the department of statistics at Columbia University, where I worked with the groups of David Blei and Peter Orbanz. I completed my Ph.D. in statistics at the University of Toronto, where I was advised by Daniel Roy. In a previous life, I worked on quantum computing at the University of Waterloo. I won a number of awards, including the Pierre Robillard award for best statistics thesis in Canada.

Project: Machine Learning to Improve Targeted Cancer Therapy

Advances in genomics have created many new cancer therapies that target specific genetic or molecular features, raising the potential for personalized treatment that improves effectiveness and decreases side effects. However, the majority of patients treated with targeted therapies do not respond as predicted, and detailed patient genomic information is expensive to acquire.

This collaboration proposes to improve targeting of cancer therapies by developing new AI approaches that recommend the best treatment based on a combination of genetic and pathology data. Researchers will build new methods drawing upon computer vision and machine learning to gather essential contextual information about individual cancers from tumor samples, utilizing both genomic and image-based features. This work will advance both artificial intelligence and cancer-focused data science by developing innovative solutions to emerging machine learning problems in cancer research, ultimately benefiting patients through more targeted, effective treatments.

Mentors: Samantha Riesenfeld and Alex Pearson

Medical oncologist Alexander T. Pearson, MD, PhD, cares for adults with head and neck cancers, especially squamous cell carcinoma, a common form of cancer that develops in the squamous cells that make up the middle and outer layer lining the mouth and throat. He also specializes in salivary gland cancers.

His research combines laboratory experiments and mathematical models to more fully understand how head and neck cancers form and how to better design treatments for these cancers. He is currently the principal investigator on an NIH-funded study on the development of combination therapies for head and neck cancer.

Dr. Pearson has more than a dozen peer-reviewed publications in the primary literature, has had several abstracts selected for oral presentation and is editor of the Oncology Boards Flash Review, 2nd edition.

Dana Suskind, MD, Professor of Surgery and Pediatrics, Director, Pediatric Cochlear Implantation Program, Co-Director, Thirty Million Words (TMW) Center for Early Learning + Public Health

Dana Suskind, MD, is a pediatric otolaryngologist who specializes in hearing loss and cochlea implantation. She directs the University of Chicago Medicine’s Pediatric Hearing Loss and Cochlear Implant program.

Recognized as a national thought leader in early language development, Dr. Suskind has dedicated her research and clinical life to optimizing foundational brain development and preventing early cognitive disparities and their lifelong impact. She is founder and co-director of the TMW Center for Early Learning + Public Health, which aims to create a population-level shift in the knowledge and behavior of parents and caregivers to optimize the foundational brain development in children from birth to five years of age, particularly those born into poverty.

Her book “Thirty Million Words: Building a Child’s Brain” was published in 2015.

Dr. Suskind has received several awards for her work, including the Weizmann Women for Science Vision and Impact award, the SENTAC Gray Humanitarian Award, the LENA Research Foundation Making a Difference Award, the Chairman’s Award from the Alexander Graham Bell Association for the Deaf and Hard of Hearing in 2018, and the John D. Arnold, MD Mentor Award for Sustained Excellence from the Pritzker School of Medicine.

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.

Samuel L. Volchenboum, MD, PhD, MS, is an expert in pediatric cancers and blood disorders. He has a special interest in treating children with neuroblastoma, a tumor of the sympathetic nervous system.

In addition to caring for patients, Dr. Volchenboum studies ways to harness computers to enable research and foster innovation using large data sets. He directs the development of the International Neuroblastoma Risk Group Database project, which connects international patient data with external information such as genomic data and tissue availability. The Center he runs provides computational support for the Biological Sciences Division at the University of Chicago, including high-performance computing, applications development, bioinformatics, and access to the clinical research data warehouse.

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Sendhil Mullainathan is the Roman Family University Professor of Computation and Behavioral Science at Chicago Booth. His latest research is on computational medicine—applying machine learning and other data science tools to produce biomedical insights. In past work he has combined insights from behavioral science with empirical methods—experiments, causal inference tools, and machine learning—to study social problems such as discrimination and poverty. He currently teaches a course on Artificial Intelligence. Outside of research, he co-founded a non-profit to apply behavioral science (ideas42), a center to promote the use of randomized control trials in development (the Abdul Latif Jameel Poverty Action Lab), has worked in government in various roles, and currently serves on the board of the MacArthur Foundation board. He is also a regular contributor to the New York Times.

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Aaron is an Assistant Professor in the Statistics Department and Data Science Institute at UChicago. His research develops methodology in Bayesian statistics, causal inference, and machine learning for applied problems in political science, economics, and genetics, among other fields. Prior to joining UChicago, Aaron was a postdoctoral fellow in the Data Science Institute at Columbia University. He received his PhD in Computer Science from UMass Amherst, as well as an MA in Linguistics and a BA in Political Science. He is on Twitter @AaronSchein.

Chenhao Tan is an assistant professor at the Department of Computer Science and the UChicago Data Science Institute. His main research interests include language and social dynamics, human-centered machine learning, and multi-community engagement. He is also broadly interested in computational social science, natural language processing, and artificial intelligence.

Website

Dacheng Xiu’s research interests include developing statistical methodologies and applying them to financial data, while exploring their economic implications. His earlier research involved risk measurement and portfolio management with high-frequency data and econometric modeling of derivatives. His current work focuses on developing machine learning solutions to big-data problems in empirical asset pricing.

Xiu’s work has appeared in Econometrica, the Journal of Econometrics, the Journal of the American Statistical Association, the Annals of Statistics, and the Journal of Finance. He is a Co-Editor for the Journal of Financial Econometrics, an Associate Editor for the Journal of Econometrics, the Journal of Business & Economic Statistics, the Journal of Empirical Finance, and Statistica Sinica, and also referees for several journals in the fields of econometrics, statistics, and finance. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, the Fellow of the Journal of Econometrics, the 2018 Swiss Finance Institute Outstanding Paper Award, the 2018 AQR Insight Award, and the Best Conference Paper Prize at the 2017 Annual Meeting of the European Finance Association.

In 2017, Xiu launched a website that provides up-to-date realized volatilities of individual stocks, as well as equity, currency, and commodity futures. These daily volatilities are calculated from the intraday transactions and the methodologies are based on his research of high-frequency data.

Xiu earned his PhD and MA in applied mathematics from Princeton University, where he was also a student at the Bendheim Center for Finance. Prior to his graduate studies, he obtained a BS in mathematics from the University of Science and Technology of China.

Project: Combining human and machine intelligence for policy impact

The success of artificial intelligence (AI) for engineering and commercial applications has led to growing interest in using these tools to help solve important social problems like inequality in income, education, health, or criminal justice system involvement. But any realistic assessment of how AI will be used in these areas suggests it will be a complement to, not substitute for, human judgment. That is, AI will be used as decision aids, not decision makers. In previous work (Kleinberg, Lakkaraju, Leskovec, Ludwig and Mullainathan, 2018 Quarterly Journal of Economics) we have found in the context of criminal justice system decision-making that humans on net add negative value to the machine’s predictions of defendant risk, although in principle the private information humans can access that algorithms cannot (such as courtroom discussion about the details of the case) could help the human add positive value in at least some cases. Similar issues arise in numerous other policy domains such as medical diagnosis, hiring, credit, and education admissions. The goal of this project is to better understand the potential sources of human and machine comparative advantage by measuring the private information humans have access to in different decision-making domains, trying to understand what are useful sources of signal versus sources of noise for human decisions about when to follow versus over-ride the algorithm’s recommendations, and then try to build decision-making systems that lead to the human plus machine together to outperform the decisions implied by the machine’s predictions alone.

Mentor: Jens Ludwig, Edwin A. and Betty L. Bergman Distinguished Service Professor, Harris School of Public Policy, Director of University of Chicago Crime Lab, Co-director Education Lab

Jens Ludwig is the Edwin A. and Betty L. Bergman Distinguished Service Professor, director of the University of Chicago’s Crime Lab, codirector of the Education Lab, and codirector of the National Bureau of Economic Research’s working group on the economics of crime.

In the area of urban poverty, Ludwig has participated since 1995 on the evaluation of a HUD-funded randomized residential-mobility experiment known as Moving to Opportunity (MTO), which provides low-income public housing families the opportunity to relocate to private-market housing in less disadvantaged neighborhoods. In the area of education he has written extensively about early childhood interventions, and about the role of social conditions in affecting children’s schooling outcomes. In the area of crime, Ludwig has written extensively about gun-violence prevention. Through the Crime Lab he is also involved in partnering with policymakers in Chicago, New York City, and across the country to use tools from social science, behavioral science, and computer science to identify effective (and cost-effective) ways to help prevent crime and violence. This includes studies of various social programs, helping the Chicago Police Department use data to reduce gun violence and strengthen police-community relations, and work underway to use data science to help New York City build and implement a new pretrial risk tool as part of the city’s goal to close Riker’s Island. Crime Lab projects have helped redirect millions of dollars of public-sector resources to evidence-based strategies and have been featured in national news outlets such as the New York Times, Washington Post, Wall Street Journal, PBS News Hour and National Public Radio. In 2014 the Crime Lab was the recipient of a $1 million MacArthur Award for Creative and Effective Institutions, the organizational equivalent of the foundation’s “genius prize.”

His research has been published in leading scientific journals across a range of disciplines including Science, New England Journal of Medicine, Journal of the American Medical Association, American Economic Review, Quarterly Journal of Economics, the Economic Journal, and the American Journal of Sociology. His coauthored article on race, peer norms, and education with Philip Cook was awarded the Vernon Prize for the best article in the Journal of Policy Analysis and Management. He is also coauthor with Cook of Gun Violence: The Real Costs (Oxford University Press, 2000), coeditor with Cook of Evaluating Gun Policy (Brookings Institution Press, 2003), and coeditor with Cook and Justin McCray of Controlling Crime: Strategies and Tradeoffs (University of Chicago Press, 2012).

Prior to coming to Harris, Ludwig was a professor of public policy at Georgetown University. He is currently on the editorial board of the American Economic Review and was formerly coeditor of the Journal of Human Resources, and currently serves on the National Academy of Sciences Committee on the Neurobiological and Socio-behavioral Science of Adolescent Development and Its Applications. In 2012 he was elected vice president of the Association for Public Policy Analysis and Management (APPAM), the professional society for public policy schools. Ludwig received his BA in economics from Rutgers College and his MA and PhD in economics from Duke University. In 2006 he was awarded APPAM’s David N. Kershaw Prize for Contributions to Public Policy by Age 40. In 2012 he was elected to the Institute of Medicine of the National Academies of Science.

Marianne Bertrand, Chris P. Dialynas Distinguished Service Professor of Economics and Willard Graham Faculty Scholar, Booth School of Business

Marianne Bertrand is the Chris P. Dialynas Distinguished Service Professor of Economics at the University of Chicago Booth School of Business. She is a Research Fellow at the National Bureau of Economic Research, the Center for Economic Policy Research, and the Institute for the Study of Labor.

Professor Bertrand is an applied micro-economist whose research covers the fields of labor economics, corporate finance, and development economics. Her research in these areas has been published widely, including numerous research articles in the Quarterly Journal of Economics, the Journal of Political Economy, the American Economic Review, and the Journal of Finance.

Professor Bertrand is Faculty Director of Chicago Booth’s Rustandy Center for Social Sector Innovation and the Faculty Director of the Poverty Lab at the University of Chicago Urban Labs. Professor Bertrand also serves as co-editor of the American Economic Review.

She has received several awards and honors, including the 2004 Elaine Bennett Research Prize, awarded by the American Economic Association to recognize and honor outstanding research in any field of economics by a woman at the beginning of her career, and the 2012 Society of Labor Economists’ Rosen Prize for Outstanding Contributions to Labor Economics. She is a Fellow of the American Academy of Arts and Sciences.

Born in Belgium, Professor Bertrand received a Bachelor’s Degree in economics from Belgium’s Universite Libre de Bruxelles in 1991, followed by a Master’s Degree in econometrics from the same institution the next year. She moved to the United States in 1993 and earned a Ph.D. in economics from Harvard University in 1998. She was a faculty member in the Department of Economics at Princeton University for two years before joining Chicago Booth in 2000.

Marshini Chetty is an assistant professor in the Department of Computer Science at the University of Chicago, where she co-directs the Amyoli Internet Research Lab or AIR lab. She specializes in human-computer interaction, usable privacy and security, and ubiquitous computing. Marshini designs, implements, and evaluates technologies to help users manage different aspects of Internet use from privacy and security to performance, and costs. She often works in resource-constrained settings and uses her work to help inform Internet policy. She has a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, USA and a Masters and Bachelors in Computer Science from the University of Cape Town, South Africa. In her former lives, Marshini was on the faculty in the Computer Science Department at Princeton University and the College of Information Studies at the University of Maryland, College Park. Her work has won best paper awards at SOUPS, CHI, and CSCW and has been funded by the National Science Foundation, the National Security Agency, Intel, Microsoft, Facebook, and multiple Google Faculty Research Awards.

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Michael Greenstone, Milton Friedman Distinguished Service Professor in Economics, the College, and the Harris School, University of Chicago; Director, Becker Friedman Institute for Research in Economics; Director, Energy Policy Institute at the University of Chicago (EPIC); Director, Tata Center for Development at the University of Chicago

I am an Assistant Professor in the Department of Political Science at The University of Chicago.

I work on quantitative methodology for social science research, with a focus on causal inference, machine learning, and experimental design–particularly for adaptive experiments. My PhD is from Yale, joint in Political Science and Statistics & Data Science.

Previously, I was a post-doctoral fellow in Susan Athey’s Golub Capital Social Impact Lab at the Stanford Graduate School of Business.

In addition to the PhD, I hold a Masters in Statistics, also from Yale, and a Masters in Public Affairs, from the Princeton School of Public and International Affairs. My undergraduate degree was in cultural anthropology from Grinnell College; after college, I spent a year in Lesotho, teaching high school students, and two years in Madagascar, as a Peace Corps volunteer.

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Nick Feamster is Neubauer Professor in the Department of Computer Science and the College and the Faculty Director of Research for the Data Science Institute. He researches computer networking and networked systems, with a particular interest in Internet censorship, privacy, and the Internet of Things. His work on experimental networked systems and security aims to make networks easier to manage, more secure, and more available.

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Chibueze Amanchukwu is a Neubauer Family Assistant Professor at the Pritzker School of Molecular Engineering at the University of Chicago. He received his bachelor’s degree in chemical engineering from Texas A&M University as the department’s Outstanding Graduating Student, and his PhD in chemical engineering from the Massachusetts Institute of Technology.

As a graduate student with Paula Hammond, he elucidated polymer degradation mechanisms and tuned polymer electrolyte behavior in lithium-air batteries. His graduate work was supported by the National Defense Science and Engineering Graduate (NDSEG) Fellowship, GEM Fellowship, and the Alfred P. Sloan Minority Fellowship. As a postdoctoral fellow with Zhenan Bao at Stanford University, he developed new small molecule electrolytes that decoupled ionic conductivity from electrochemical instability for lithium metal batteries. His postdoctoral work was supported by the TomKat Center Postdoctoral Fellowship in Sustainable Energy at Stanford. His research has been recognized with awards from the American Chemical Society (Excellence in Graduate Polymer Research) and the American Institute of Chemical Engineers (Session’s Best Paper).

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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Raul Castro Fernandez is an Assistant Professor of Computer Science at the University of Chicago. In his research he builds systems for discovering, preparing, and processing data. The goal of his research is to understand and exploit the value of data. He often uses techniques from data management, statistics, and machine learning. His main effort these days is on building platforms to support markets of data. This is part of a larger research effort on understanding the Economics of Data. He’s part of ChiData, the data systems research group at The University of Chicago.

Homepage.

Chibueze Amanchukwu is a Neubauer Family Assistant Professor at the Pritzker School of Molecular Engineering at the University of Chicago. He received his bachelor’s degree in chemical engineering from Texas A&M University as the department’s Outstanding Graduating Student, and his PhD in chemical engineering from the Massachusetts Institute of Technology.

As a graduate student with Paula Hammond, he elucidated polymer degradation mechanisms and tuned polymer electrolyte behavior in lithium-air batteries. His graduate work was supported by the National Defense Science and Engineering Graduate (NDSEG) Fellowship, GEM Fellowship, and the Alfred P. Sloan Minority Fellowship. As a postdoctoral fellow with Zhenan Bao at Stanford University, he developed new small molecule electrolytes that decoupled ionic conductivity from electrochemical instability for lithium metal batteries. His postdoctoral work was supported by the TomKat Center Postdoctoral Fellowship in Sustainable Energy at Stanford. His research has been recognized with awards from the American Chemical Society (Excellence in Graduate Polymer Research) and the American Institute of Chemical Engineers (Session’s Best Paper).

Raul Castro Fernandez is an Assistant Professor of Computer Science at the University of Chicago. In his research he builds systems for discovering, preparing, and processing data. The goal of his research is to understand and exploit the value of data. He often uses techniques from data management, statistics, and machine learning. His main effort these days is on building platforms to support markets of data. This is part of a larger research effort on understanding the Economics of Data. He’s part of ChiData, the data systems research group at The University of Chicago.

Homepage.

I am an Assistant Professor of Computer Science at the University of Chicago. I founded and direct 3DL (threedle! ), a group of enthusiastic researchers passionate about 3D, machine learning, and visual computing. I obtained my Ph.D. in 2021 from Tel Aviv University under the supervision of Daniel Cohen-Or and Raja Giryes.

My research is focused on building artificial intelligence for 3D data, spanning the fields of computer graphics, machine learning, and computer vision. Deep learning, the most popular form of artificial intelligence, has unlocked remarkable success on structured data (such as text, images, and video), and I am interested in harnessing the potential of these techniques to enable effective operation on unstructured 3D geometric data.

We have developed a convolutional neural network designed specifically for meshes, and also explored how to learn from the internal data within a single shape (for surface reconstruction, geometric texture synthesis, and point cloud consolidation) – and I am interested in broader applications related to these areas. Additional research directions that I am aiming to explore include: intertwining human and machine-based creativity to advance our capabilities in 3D shape modeling and animation; learning with less supervision, for example to extract patterns and relationships from large shape collections; and making 3D neural networks more “interpretable/explainable”.

Sanjay Krishnan is an Assistant Professor of Computer Science. His research group studies the theory and practice of building decision systems that are robust to corrupted, missing, or otherwise uncertain data. His research brings together ideas from statistics/machine learning and database systems. His research group is currently studying systems that can analyze large amounts of video, certifiable accuracy guarantees in partially complete databases, and theoretical lower-bounds for lossy compression in relational databases.

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Aaron is an Assistant Professor in the Statistics Department and Data Science Institute at UChicago. His research develops methodology in Bayesian statistics, causal inference, and machine learning for applied problems in political science, economics, and genetics, among other fields. Prior to joining UChicago, Aaron was a postdoctoral fellow in the Data Science Institute at Columbia University. He received his PhD in Computer Science from UMass Amherst, as well as an MA in Linguistics and a BA in Political Science. He is on Twitter @AaronSchein.

Chenhao Tan is an assistant professor at the Department of Computer Science and the UChicago Data Science Institute. His main research interests include language and social dynamics, human-centered machine learning, and multi-community engagement. He is also broadly interested in computational social science, natural language processing, and artificial intelligence.

Website

Samuel L. Volchenboum, MD, PhD, MS, is an expert in pediatric cancers and blood disorders. He has a special interest in treating children with neuroblastoma, a tumor of the sympathetic nervous system.

In addition to caring for patients, Dr. Volchenboum studies ways to harness computers to enable research and foster innovation using large data sets. He directs the development of the International Neuroblastoma Risk Group Database project, which connects international patient data with external information such as genomic data and tissue availability. The Center he runs provides computational support for the Biological Sciences Division at the University of Chicago, including high-performance computing, applications development, bioinformatics, and access to the clinical research data warehouse.

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