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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.


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.


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.


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.


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.

Mindi has experience managing, sourcing and scoping a portfolio of data science experiential learning partnerships across social impact organizations, corporate, civic and government entities. Mindi managed earlier iterations of our work with the 11th Hour Project at the University of San Francisco where she served as the Senior Director of Strategy and Operations of the Data Institute. Mindi is a member (inactive) of the California and Illinois bars and received her JD from UC Hastings and her BS, Political Science from Santa Clara University.

Jess joined the University of Chicago in October 2021, after 8 years working in public health policy and emergency response programs at the Centers for Disease Control and Prevention in Atlanta, GA. Bringing years of experience working on major science initiatives, Jess leads the Data Science Institutes research program strategy, collaborating with faculty, postdocs, students and partners to help push forward initiative goals and translate analysis into real world solutions to complex scientific and societal challenges. Jess received her MPA from the University of Georgia, and BA in International Political Economy and Religion from the University of Puget Sound.


Aloni Cohen is an Assistant Professor of Computer Science and Data Science at the University of Chicago. Previously, Cohen was a Postdoctoral Associate at Boston University, with a joint appointment at the Hariri Institute for Computing and the School of Law. His research explores the interplay between theoretical cryptography, privacy, law, and policy. Aloni earned his PhD in electrical engineering and computer science at MIT where he was advised by Shafi Goldwasser and supported by a Facebook Fellowship and an NSF Graduate Student Fellowship. Aloni is a former affiliate at the Berkman Klein Center for Internet & Society and a Fellow at the Aspen Tech Policy Hub.

My research explores the interplay between theoretical cryptography, privacy, law, and policy. Specifically, I aim to understand and resolve the tensions between the theory of cryptography and the privacy and surveillance law that governs its eventual real-world context. Right now, I’m thinking about differential privacy, GDPR, the Fifth Amendment, encryption, multiparty computation, and the Census.

Greg Green is Senior Instructional Professor and Director of the MScA Program at the University of Chicago, and Senior Director for Industrial Partnerships and Strategy at the Data Science Institute. Dr. Green helps the University of Chicago professional data science students learn to apply data science to solve complex industry problems with greater impact.

Dr. Green is reshaping the content and approaches used to educate the next generation of professional data scientists at the University of Chicago.  Additionally, Greg is designing new, creative offerings more deeply connected to MS and PhD research programs in Data Science, Computer Science, Statistics, and Financial Mathematics. New course offerings developed launched since joining the University of Chicago include an innovative approach to “Leadership in Data Science and Artificial Intelligence”, “Consulting in Data Science” and “Your Career in Data Science”.

Throughout his professional career, Greg has used his expertise in digital strategies, business analytics, and new product development to drive rapid revenue growth and accelerate business transformation. His previous work bringing innovation to an academic environment included authoring a Marketing Analytics course, designing a pre-requisite applied statistics course and serving as a lecturer for Marketing Analytics at Northwestern University.

Greg’s industry roles include Chief Analytics Officer at Harland Clarke Holdings, Director at Google, EVP/Managing Director at Publicis Groupe, and Analytics Practice Lead at PwC. Greg’s patented cloud-based media analytics platform was highlighted in Harvard Business Review and Fast Company.

Greg holds a Doctor of Philosophy in Mathematics from Claremont Graduate School and a Master of Science in Statistics from Michigan State University. Born in Owosso, Michigan, Greg is married to Jill, an artist, and their adult children include two more artists, a teacher, and an engineer. Greg and his family enjoy snowboarding, snow/water skiing and live theatre—as well as good food and friendships. Their passion for the environment is reflected in a love for Lake Michigan where they like to spend as much of the summer as possible.

Alex Kale is an Assistant Professor in Computer Science and the Data Science Institute at the University of Chicago. Previously, he earned his PhD at the University of Washington where he worked with Jessica Hullman. Alex leads the Data Cognition Lab, focused on creating data visualization and analysis software that explicitly represents the user’s cognitive processes.

Alex creates and evaluates tools for helping people think with data, specializing in data visualization and reasoning with uncertainty. He publishes in top human-computer interaction and data visualization venues such as ACM CHI and IEEE VIS, where his work has won Best Paper and Honorable Mention Awards. Alex’s work addresses gaps in dominant theories and models of what makes visualization effective for inferences and decision making.


Amy Nussbaum joined the University of Chicago in Fall 2022. She holds a PhD in Statistical Sciences from Southern Methodist University, where she researched latent variables—those that cannot be measured directly, like happiness or stress. Specifically, she studies the assessment of personality traits. Most recently, she held the position of Visiting Lecturer of Statistics at Mount Holyoke College.

In addition to academia and research, she encourages understanding the use of statistics in government and industry. After graduation, she served as the inaugural Science Policy Fellow at the American Statistical Association, working to promote the practice and profession of statistics by advocating for evidence-based policy making and the federal statistical agencies, as well the lead statistician for a medical device company developing a novel imager able to detect diseased human tissue using artificial intelligence.

Nick Ross is an experienced data science executive with significant leadership experience in both industry and academic settings. Nick has successfully built and managed data science teams at companies ranging from small startups to multinational corporations, and he continues to advise organizations on all things data and engineering.  Before joining The University of Chicago, he led the data science and backend engineering teams at an e-sports startup based in San Francisco.

Academically, Nick was most recently an Assistant Professor of Data Science at The University of San Francisco (USF). In addition to his to teaching and research duties, Nick was the Director of USF’s Data Science Practicum Program and oversaw industry outreach and partnerships for USF’s newly formed Data Institute.

Nick is a proud product of the University of California system, having received his undergraduate degree in Math from  UC Berkeley, his Masters in Economics from UC Davis, and his Ph.D. from UCLA’s Anderson School of Management.

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.


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.


Haifeng Xu is an assistant professor in the Department of Computer Science and the Data Science Institute at UChicago. He directs the Strategic IntelliGence for Machine Agents (SIGMA) research lab which focuses on designing algorithms/systems that can effectively elicit, process and exploit information, particularly in strategic environments. Haifeng has published more than 55 publications at leading venues on computational economics, machine learning and theoretical computer science, such as EC, ICML, NeurIPS, STOC and SODA. His research has been recognized by multiple awards, including the Google Faculty Research Award, ACM SIGecom Dissertation Award (honorable mention), IFAAMAS Victor Lesser Distinguished Dissertation Award (runner-up), Google PhD fellowship, and multiple best paper awards.

The following research themes are the recent focus of our research lab. Please refer to our lab’s website for more details.

  • The economics of data/information, including selling, acquiring, and exploiting information
  • Machine learning in multi-agent setups under information asymmetry, incentive conflicts, and deception
  • Resource allocation in adversarial domains, with applications to security and privacy protection


Evelyn  is a preceptor in data science focusing on data science education as a joint instructor for both the University of Chicago and City Colleges of Chicago. She obtained her PhD in Microbiology from the University of Chicago in 2022 and her BS in Biology from Rider University in 2016. She enjoys reading, writing, and talking with friends and family.

Amanda is a preceptor in data science focusing on data science education as a joint instructor for both the University of Chicago and City Colleges of Chicago. Prior to joining the DSI, Amanda earned her PhD in Computational and Data Sciences from Washington University in St. Louis, co-advised by Sanmay Das and Patrick Fowler. Her research interests involve the intersection of computer science and the social sciences with a focus on fair applications of AI to social-scientific questions. Her current work combines machine learning and human decision-making to inform fair and efficient service allocations for homeless families. She received her bachelor’s degree in Psychology and Statistics and her master’s degree in Data Analytics and Statistics both from Washington University in St. Louis.

Admin Staff

Rob Mitchum is Associate Director of Communications for Data Science and Computing at the University of Chicago, covering research and faculty for the Department of Computer Science, the Data Science Institute, and other computational initiatives.

Katie Rosengarten is Program Manager at the Data Science Institute, responsible for overseeing strategic partnerships, management, execution, and evaluation of student research engagement opportunities for early high school learners through PhD students.

Technical Staff

Kewon Bell is Project Manager for the Capacity Accelerator Network grant at the Data Science Institute. He has 20 years of Project Management experience and previously served as the Print Program Manager for UChicago. He has a Bachelors in Management from Northern Illinois University and is DEI Workplace Certified with a concentration in Recruitment & Retention and Community Outreach.
Grace Chu is a Community Project Manager supporting the Internet Access & Equity Initiative at the University of Chicago. She helps to build relationships with City and neighborhood-based partners to integrate research and community collaboration.

Launa is a software engineer responsible for executing Data Clinic projects with student teams in conjunction with the 11th Hour Project, as well as internal projects for the DSI. She received her bachelor’s degree in the humanities at Princeton University and her master’s degree in Computational Analysis and Public Policy at the University of Chicago. Prior to joining the University, she worked as an adult education instructor and then as a software consultant at a Microsoft partner company.

Tim Hannifan is Assistant Clinic Director for the Data Science Institute at the University of Chicago. He is responsible for managing the execution of an experiential learning program for University students, recruiting and retaining data science partner organizations, and mentoring student teams to implement data science solutions for government, industry, and social impact organizations. Prior to joining the DSI, Tim worked on data science initiatives at Mathematica Policy Research and The World Bank. He holds a MS in Computational Analysis and Public Policy and a BA in Economics from the University of Chicago.

Jesse London is a software engineer at the Center for Data and Computing, where he contributes to the CDAC Open-Source Initiative.

Guilherme Martins is the Senior Programming Specialist at CDAC. Guilherme has contributed to critical research projects bringing an extensive domain knowledge expertise acquired over his years in the industry and academia. His skillset includes embedded software development for specialized hardware, data engineering, and software automation.  Most recently, he developed a method for summarizing network traffic utilization while retaining critical information of over-the-top consumer internet applications such as Netflix, Youtube, Amazon, and Facebook. With fine-grain information about their internet utilization, internet consumers are now empowered to make data-driven decisions regarding their home internet infrastructure. The design and implementation of the software allow extensibility and open an interface to new applications ranging from performance monitoring to network security. He’ll be working closely with Nick Feamster and other Principal Investigators to establish data analysis pipelines.

Rahim Rasool is a Data Scientist supporting the work on Capacity Accelerator Network (CAN). He also works with social impact organizations in increasing their data capacity and implementing data science projects. Rahim got his BE in Electrical Engineering from National University of Sciences and Technology and MS in Computational Analysis and Public Policy from the University of Chicago.

Marc is the technical project manager for the Internet Access and Equity Initiative. He is responsible for working with our researchers and engineers on building the measurement tools and public-facing portal as well as overseeing the In-Home Internet Study. Marc recently graduated from the UChicago CAPP program, and prior to that he was a project manager at a DC-based economic consulting company.

Trevor is an 11th Hour Software Engineer with the DSI. He works with social impact organizations on implementing data science projects that further their mission. Additionally, Trevor mentors student teams for the Civic Data and Technology Clinic. Before the DSI, Trevor worked with computational materials science at Argonne National Laboratory and received his BS from MIT.