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  • Overview

    2022 Program Dates: June 13th – August 19th, 2022

    Questions? Check out the FAQ or email us at data-science@uchicago.edu.

    OVERVIEW

    A summer research opportunity for high school, undergraduate, and UChicago Masters students focusing on rigorous, applied, interdisciplinary data science research and rooted in a cohort community.

    The Data Science Institute Summer Lab program (launched in 2018 as the Data & Computing Summer Lab) is an immersive 10-week paid summer research program at the University of Chicago. In the program, high school, undergraduate, and UChicago Masters students are paired with a data science mentor in various domains, including: computer science, data science, social science, climate and energy policy, public policy, materials science, and biomedical research. Through this pairing the research assistant will engage with and hone their skills in research methodologies, practices, and teamwork. UChicago Masters students specifically, as well as eligible high school and undergraduate students, are paired with projects in the Social Impact Track (more details below). We encourage participation from a broad range of students, and require no prior research experience to apply.

    BENEFITS

    Students in the program are immersed in a research lab and given unparalleled, first-hand access to impactful, applied data science research. Students will gain not only an understanding of fundamental data science methodologies but specialized training within the application areas specific to their lab’s research thrust. Students are asked to practice communicating their research findings throughout the summer, culminating in final videos. The final videos are presented during an end-of-summer symposium, which is run like a professional conference and provides students a chance to field questions about their project and share the outcomes of their research projects. Students also engage in professional development and training that can help them prepare for future careers in data science and computing. Additionally, many alumni continue research work with their mentor after the program ends.

    COHORT

    In the program, students are welcomed into a cohort of their peers who represent diverse backgrounds, interests, and ambitions. Through near-peer mentoring, social gatherings, and group work on projects, students in this cohort not only become better trained data and computational scientists, but better equipped to tackle any challenges ahead through their experience with group work and collaboration. Students meet weekly in small thematic groups called “clusters” to discuss progress, ask questions, and hear about each others’ projects.

    MOTIVATION

    Broadening participation in data science, especially among historically underrepresented and marginalized groups, is essential not only for equalizing opportunities but envisioning – and creating – a future that is truly representative of the world around us. Creating an inclusive, diverse, and welcoming cohort for students to become a part of is a critical component of Summer Lab, as well as providing opportunities for students to see themselves represented in program mentors, guest speakers, and leadership. Computational work is often stereotyped as people working alone writing code, when in reality data science is a team sport, inherently interdisciplinary, and in constant conversation with real-world issues to achieve measurable, meaningful impact. We aim to not train and immerse students in research methodologies, data science skills, and domain expertise, but also to prepare them for critical transitions and sustained career paths.

    PROGRAMMING

    To supplement their research work, we provide an exciting array of programming for students during the summer. A highlight of the summer programming is a weekly speaker series featuring researchers at the forefront of data science. Speakers address topics ranging from their own unique and unconventional paths to data science research, to their innovative approaches to tackling important, impactful research questions. Students have the chance not only to hear from first-class speakers but also to introduce and be in conversation with them. In the 2021 program, we hosted 28 different speakers from a wide array of data science domains. You can watch select talks from the 2021 speaker series here.

    SOCIAL IMPACT TRACK

    The Social Impact Track is an opportunity for students to work as a part of a team on a data science project, with topics ranging from energy, food and agriculture, human rights, to marine technology. The projects are scoped and run in coordination with organizations who have been awarded grants by the 11th Hour Project, a grant making foundation serving the nonprofit community. Teams in the social impact track serve as a centralized hub for software and data science for the organizations – providing both open-source and custom data-driven solutions.All student types – high school, undergraduate, and Masters students – are eligible to participate in the Social Impact Track. 1st year UChicago Masters students are only eligible for projects through the Social Impact Track.

    ALUMNI

    Summer Lab alumni have been co-authors on published papers and posters, created apps and software tools used by thousands of people, and pursued a variety of future paths within research and beyond. Check out the Project Profiles to learn more about previous student cohorts, and watch videos overviewing their summer research projects. Summer Lab alumna Aarthi Koripelly (‘19, ‘20) shared this about her experience in the program:

    Summer Lab was a great experience for me to have exposure to the applications of computer science in other domains and gain technical knowledge. My projects have helped me hone my research and communication skills in writing reports, presenting to others, and submitting to a conference, which would not have been possible without the opportunities the program has provided.

    Read about the 2021 Summer Lab program.

    2021 Summer Lab Cohort
  • Project Profiles

    See 2019-2021 Cohort Project Profiles here.

    2022 Cohort

    Mentor: Ken Nakagaki

    Project: MorphGlove: Shape-Changing Assistive Goalkeeper Gloves for Training
    Hand Positions

    Mentors: Yanjing Li, Yuming Liu

    Project:  Understanding Hardware Failures in Deep Learning Networks for
    Biomedical Applications

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer, Trevor Spreadbury (Social Impact Track)

    Project: BankTrack Commercial Loan Pipeline

    Mentor: Chenhao Tan

    Project: Divisiveness of Language in Politics

    Mentors: David Uminsky, Daniel Grzenda, Tim Hannifan, Amanda Kube (Social Impact Track)

    Project: BPI Traffic Stops in Chicago

    Mentors: David Uminsky, Daniel Grzenda, Trevor Spreadbury (Social Impact Track)

    Project: mBio – Natural Language Processing Analysis of African Biotechnology Media Coverage

    Mentors: Michael Maire, Anna Woodard

    Project: Self-Supervised Deep Learning for Breast Cancer Treatment Response Prediction

    Mentor: Ken Nakagaki

    Project: WALL-ES: Designing Interactions with Swarm Robots Moving on the Wall

    Mentors: David Uminsky, Daniel Grzenda, Trevor Spreadbury (Social Impact Track)

    Project: Underwater Computer Vision – Schmidt Ocean Institute

    Mentor: Molly Offer-Westort

    Project: Facebook Messenger Chatbot for Surveys and Intervention Testing

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer (Social Impact Track)

    Project: WoMin Development Dashboard

    Mentors: Marshini Chetty, Chenhao Tan, Nick Feamster

    Project: Content Moderation

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer, Amanda Kube (Social Impact Track)

    Project: Remora Fishing Traceability – Smart Scale Enhancements

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer (Social Impact Track)

    Project: WoMin Development Dashboard

    Mentor: Sarah Sebo

    Project: Investigating the Influence of a Robot’s Eyes and Gaze in People’s Willingness to Help the Robot

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer, Amanda Kube (Social Impact Track)

    Project: Remora Fishing Traceability – Smart Scale Enhancements

    Mentor: Brian Nord, Aleksandra Ciprijanovic

    Project: Deep Learning Analysis of Sunyaev-Zel’dovich Galaxy Clusters

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer, Amanda Kube (Social Impact Track)

    Project: Remora Fishing Traceability – Smart Scale Enhancements

    Mentors: David Uminsky, Daniel Grzenda, Tim Hannifan, Amanda Kube (Social Impact Track)

    Project: BPI Traffic Stops in Chicago

    Mentors: Haryadi Gunawi, Ruidan Li

    Project: Fleet Anomaly Detection

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer, Trevor Spreadbury (Social Impact Track)

    Project: BankTrack Commercial Loan Pipeline

    Mentors: Anjali Adukia, Patricia Chiril, Teodora Szasz

    Project: Messages, Identity, and Inclusion in Education

    Mentors: David Uminsky, Daniel Grzenda, Trevor Spreadbury (Social Impact Track)

    Project: mBio – Natural Language Processing Analysis of African Biotechnology Media Coverage

    Mentor: Rana Hanocka

    Project: Diffusion Radiance Fields

    Mentor: Jai Yu

    Mentors: Kyle Chard, Matt Baughman

    Project: Enhanced Federated Learning with funcX

    Mentors: David Uminsky, Daniel Grzenda, Tim Hannifan, Amanda Kube (Social Impact Track)

    Project: BPI Traffic Stops in Chicago

    Mentors: Julia Koschinsky, Dylan Halpern, Stuart Lynn

    Project: Bringing Spatial Analytics to the Matico Platform Using SQL and WASM

    Mentors: David Uminsky, Daniel Grzenda, Trevor Spreadbury (Social Impact Track)

    Project: mBio – Natural Language Processing Analysis of African Biotechnology Media Coverage

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer, Trevor Spreadbury (Social Impact Track)

    Project: BankTrack Commercial Loan Pipeline

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer (Social Impact Track)

    Project: WoMin Development Dashboard

    Mentor: Haryadi Gunawi, Martin Putra

    Project: Job Execution Time Prediction in GDC’s Genomic Pipeline System

    Mentor: Nick Feamster

    Mentor: Molly Offer-Westort

    Project: Facebook Messenger Chatbot to Test Interventions to Address Vaccine Hesitancy in East and West Africa

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer (Social Impact Track)

    Project: WoMin Development Dashboard

    Mentors: David Uminsky, Daniel Grzenda, Trevor Spreadbury (Social Impact Track)

    Project: Underwater Computer Vision – Schmidt Ocean Institute

    Mentors: Yuan Chang (YC) Leong, Daniel Grzenda

    Project: Understanding Divergent Interpretations of Political Information

    Mentor: Zhao Wang

    Project: Affordable Autism Spectrum Disorder Diagnosis Using Text Data

    Mentors: David Uminsky, Daniel Grzenda, Trevor Spreadbury (Social Impact Track)

    Project: Underwater Computer Vision – Schmidt Ocean Institute

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer, Trevor Spreadbury (Social Impact Track)

    Project: BankTrack Commercial Loan Pipeline

    Mentors: David Uminsky, Daniel Grzenda, Trevor Spreadbury (Social Impact Track)

    Project: Underwater Computer Vision – Schmidt Ocean Institute

    Mentors: David Uminsky, Daniel Grzenda, Launa Greer, Amanda Kube (Social Impact Track)

    Project: Remora Fishing Traceability – Smart Scale Enhancements

    Mentor: Sarah Sebo

    Project: Investigating the Influence of a Robot’s Eyes and Gaze in People’s Willingness to Help the Robot

    Mentors: David Uminsky, Daniel Grzenda, Trevor Spreadbury (Social Impact Track)

    Project: mBio – Natural Language Processing Analysis of African Biotechnology Media Coverage

    Mentor: Eamon Duede

    Project: AI in Science

    Mentors: Giuseppe Cerati, Daniel Grzenda, V Hewes

    Project: Inferring Neutrino Type with Graphical Neural Network

    Mentors: Giuseppe Cerati, Daniel Grzenda, V Hewes

    Project: Inferring Neutrino Type with Graphical Neural Network

    Mentor: Rana Hanocka

    Project: Deep Motion

  • Mentors

    If you are interested in mentoring in future iterations of the DSI Summer Lab program, please see the mentoring opportunity here and feel free to contact Katie Rosengarten with any questions (krosengarten@uchicago.edu). See previous Summer Lab mentors here.

    2022 Mentors

    Anjali Adukia is an assistant professor at the University of Chicago Harris School of Public Policy and the College. In her work, she is interested in understanding how to reduce inequalities such that children from historically disadvantaged backgrounds have equal opportunities to fully develop their potential.  Her research is focused on understanding factors that motivate and shape behavior, preferences, attitudes, and educational decision-making, with a particular focus on early-life influences.  She examines how the provision of basic needs—such as safety, health, justice, and representation—can increase school participation and improve child outcomes in developing contexts.

    Adukia completed her doctoral degree at the Harvard University Graduate School of Education, with an academic focus on the economics of education. Her work has been funded from organizations such as the William T. Grant Foundation, the National Academy of Education, and the Spencer Foundation.  Her dissertation won awards from the Association for Public Policy Analysis and Management (APPAM), Association for Education Finance and Policy (AEFP), and the Comparative and International Education Society (CIES). Adukia received recognition for her teaching from the University of Chicago Feminist Forum.  She completed her masters of education degrees in international education policy and higher education (administration, planning, and social policy) from Harvard University and her bachelor of science degree in molecular and integrative physiology from the University of Illinois at Urbana-Champaign.  She is a faculty research fellow of the National Bureau of Economic Research and a faculty affiliate of the University of Chicago Education Lab and Crime Lab.  She is on the editorial board of Education Finance and Policy.  She was formerly a board member of the Young Nonprofit Professionals Network – San Francisco Bay Area. She continues to work with non-governmental organizations internationally, such as UNICEF and Manav Sadhna in Gujarat, India.

    Since joining Fermilab in 2016, my scientific work has focused on the search of new physics in neutrino oscillation experiments.

    As a member of the MicroBooNE Collaboration (since 2016), I am mainly interested in searching for sterile neutrinos, as well as in the development of techniques for the reconstruction and analysis of Liquid Argon Time Project Chambers data. I also joined the DUNE Collaboration, where my main interest is CP violation in the neutrino sector.

    Previously I worked for about a decade on the CMS experiments, with major contributions to the Higgs boson discovery, SUSY searches and Standard Model measurements.

    I served as CMS Track Reconstruction convener in 2013-2014.

    Kyle Chard is a Research Assistant Professor in the Department of Computer Science at the University of Chicago and Argonne National Laboratory. He has been Program Director of the Data & Computing Summer Lab since its first iteration under CDAC in 2019, and previously oversaw the Summer Internship Program ran by the former Computation Institute.

    He received his Ph.D. in Computer Science from Victoria University of Wellington in 2011. He co-leads the Globus Labs research group which focuses on a broad range of research problems in data-intensive computing and research data management. He currently leads projects related to parallel programming in Python, scientific reproducibility, and elastic and cost-aware use of cloud infrastructure.

    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.

    Homepage.

    Patricia Chiril joined DSI as a postdoctoral scholar in winter 2022, and has previously completed her doctoral degree at the University of Toulouse, France.

    She is committed to developing robust hate speech detection systems capable of adapting their predictions in the presence of novel or different topics and targets, as well as endowing these systems with affective intelligence.

    Her recent research focuses on analyzing how different characters are represented in children’s books to better understand how the messages children encounter can impact their behavior, motivational dispositions and attitudes.

    Eamon Duede is a joint PhD Candidate in the departments of Philosophy and the Committee on Conceptual and Historical Studies of Science, and was formerly the Executive Director of the Knowledge Lab. His work is broadly at the intersection of the philosophy of science and computational science of science. In the philosophy of science, he focuses on models, simulations, and artificial intelligence / machine learning in science. In computational science of science, he uses large scale, computational analysis alongside targeted intelligent surveying and field experiments to understand how institutions and communities produce knowledge.

    Nick Feamster is Neubauer Professor in the Department of Computer Science and the College. 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.

    Homepage

    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.

    Daniel Grzenda is a Staff Data Scientist supporting the partnership with the 11th Hour Project at the University. He engages with social impact organizations across the domains of energy, food and agriculture, human rights, and marine technology, to support applications of data and computer science. Daniel’s work is focused on increasing the data capacity of these organizations, lowering the barriers to mission driven data science, and empowering these organizations through the development of sustainable technical solutions.

    Haryadi S. Gunawi is an Associate Professor in the Department of Computer Science at the University of Chicago where he leads the UCARE research group (UChicago systems research on Availability, Reliability, and Efficiency). He received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 2009. He was a postdoctoral fellow at the University of California, Berkeley from 2010 to 2012. His current research focuses on cloud computing reliability and new storage technology. He has won numerous awards including NSF CAREER award, NSF Computing Innovation Fellowship, Google Faculty Research Award, NetApp Faculty Fellowships, and Honorable Mention for the 2009 ACM Doctoral Dissertation Award. Website: https://people.cs.uchicago.edu/~haryadi.

    Dylan Halpern is the Principal Software Engineer for the US Covid Atlas at the Center. Utilizing methods of geospatial data analytics, visualization, and web development, he works in domains of public health, urban experience and activity, and transit. He holds a Master in City Planning from MIT, and previous positions include research roles with MIT Senseable City Lab, Civic Data Design Lab, and City Form Lab, and a Fulbright research fellowship in Brazil.

    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.

    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 reconstructiongeometric 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”.

    Jeremy Hewes is a postdoctoral researcher at Fermi National Accelerator Laboratory working on the DUNE project.

    Julia Koschinsky is the Executive Director of the Center for Spatial Data Science at the University of Chicago and has been part of the GeoDa team for over 16 years. She has been conducting and managing research funded through federal awards of over $8 million to gain insights from the spatial dimensions of urban challenges in housing, health, and the built environment.

    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.

    My research examines the different ways in which goals, desires and needs affect how people perceive and respond to our environment. My work draws from the traditions of cognitive neuroscience, social psychology and affective science. I use a broad range of methodological tools, including behavioral experiments, computational modeling, fMRI, pupillometry, naturalistic paradigms and network analyses. By combining different tools and perspectives, I seek to characterize motivational influences on human cognition at the psychological, computational and neural levels. One ultimate goal of this work is to identify behavioral and neural targets of intervention to improve socio-cognitive functioning.

    I direct the Motivation and Cognition Neuroscience Laboratory at the University of Chicago. You can learn more about my lab here: https://mcnlab.uchicago.edu/.

    Yanjing Li is an Assistant Professor in the Department of Computer Science (Systems Group) at the University of Chicago. Prior to joining University of Chicago, she was a senior research scientist at Intel Labs. Professor Li received her Ph.D. in Electrical Engineering from Stanford University, a M.S. in Mathematical Sciences (with honors) and a B.S. in Electrical and Computer Engineering (with a double major in Computer Science) from Carnegie Mellon University.

    Professor Li has received various awards, including Google research scholar award, NSF/SRC energy-efficient computing: from devices to architectures (E2CDA) program award, Intel Labs Gordy academy award (highest honor in Intel Labs) and several other Intel recognition awards, outstanding dissertation award (European Design and Automation Association), and multiple best paper awards (ACM Great Lakes Symposium on VLSI, and IEEE VLSI Test Symposium, and IEEE International Test Conference).

    Website: https://people.cs.uchicago.edu/~yanjingl.

    Michael Maire is an assistant professor in the Department of Computer Science at the University of Chicago.  He was previously a research assistant professor at the Toyota Technological Institute at Chicago (TTIC), where he maintains a courtesy appointment.  Prior to TTIC, he was a postdoctoral scholar at the California Institute of Technology.  He received a PhD in computer science from the University of California, Berkeley in 2009. Website: https://cs.uchicago.edu/people/michael-maire/.

    Nicole Marwell is Associate Professor in the University of Chicago Crown Family School of Social Work, Policy, and Practice. She is also a faculty affiliate of the Department of Sociology, a faculty fellow at the Center for Spatial Data Science, and a member of the Faculty Advisory Council of the Mansueto Institute for Urban Innovation. Her research examines urban governance, with a focus on the diverse intersections between nonprofit organizations, government bureaucracies, and politics.

    Ken Nakagaki is an interaction designer and HCI (Human-Computer Interaction) researcher from Japan. He is an assistant professor at the University of Chicago, Computer Science Department. He directs ‘Actuated Experience Lab’ [AxLab] there.

    His research has been focuses on inventing novel user interface technologies that seamlessly combine dynamic digital information or computational aids into daily physical tools and materials. He is passionate about creating novel physical embodied experiences using such interfaces through curiosity-driven tangible prototyping processes. During his PhD study at MIT Media Lab, he pursued research in Actuated Tangible User Interfaces, under supervision of Prof. Hiroshi Ishii.

    Before joining the Media Lab, he received Master’s and Bachelor’s degrees in interaction design from Keio University. His research has been presented in top HCI conferences (ACM CHI, UIST, TEI). His works were also demonstrated in international exhibitions and museums such as the Ars Electronica Festival and Laval Virtual. He has received numerous awards, including the MIT Technology Review’s Innovators Under 35 Japan & Asia Pacific, the Japan Media Arts Festival, and the James Dyson Award.

    Website: https://www.ken-nakagaki.com/about.

    Brian Nord uses artificial intelligence to search for clues on the origins and development of the universe. He actively works on statistical modeling of strong gravitational lenses, the cosmic microwave background, and galaxy clusters. As leader of the Deep Skies Lab, he brings together experts in computer science and technology to study questions of cosmology, including dark energy, dark matter, and the early universe, through large-scale data analysis.

    Nord has authored or co-authored nearly 50 papers. He trains scientists in public communication, advocates for science education and funding, and works to develop equitable and just research environments. As co-leader of education and public engagement at the Kavli Institute for Cosmological Physics at UChicago, he organizes Space Explorers, a program to help underrepresented minorities in high school engage in hands-on physics experiences outside the classroom. He is an associate scientist at Fermi National Accelerator Laboratory, where he is a member of the Machine Intelligence Group.

    Homepage.

    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.

    I am an Assistant Professor of Computer Science at the University of Chicago. I received my PhD in Computer Science from Yale University in 2020. My current research explores social dynamics in human-robot interactions, where a robot’s social behaviors lead to positive outcomes for people (e.g., improved team dynamics and performance in a human-robot team, educational learning outcomes for children). During my PhD, I focused on developing robots that improve the performance of human-robot teams by shaping team dynamics to promote inclusion, trust, and cohesion.

    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.

    Teodora engages the community of researchers involved in computational image analysis at The University of Chicago across multi-disciplinary areas including: Biology, Physics, Chemistry, Economics, Public Policy, and Cancer Research.

    She serves as a catalyst for solving challenging questions in the research teams that she is supporting, such as: predicting oxygen support for COVID-19 patients, detecting prostate cancer, and analysis of messages related to identity in official educational setting. Teodora brings to the field practical expertise in state-of-the-art advanced technology: Supercomputers, Cloud Computing, Machine Learning, Image Analysis Techniques, and Big Data.

    Prior to joining RCC, Teodora earned her doctorate degree in Computer Science at Toulouse University in France. She won international challenges (IUS PICMUS 2016) in beamforming for ultrasound medical imaging.

    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.

    Homepage.

    Dr. Wang is an Assistant Instructional Professor in the Masters in Computational Social Science program at The University of Chicago since Fall 2021. Zhao’s research is at the intersection of Machine Learning, Natural language Processing, and Social Media Analysis. She has worked on improving statistical model robustness, exploring computational linguistic models to analyze data from social media platforms and provide insights into social issues, and understanding causal inference in natural language. Examples include detecting deceptive marketing such as greenwashing, measuring the authenticity of public messaging, investigating language treatment effects on human perception, suggesting impression management strategies in online communication, identifying and correcting text classification model bias, modeling the relationship between personality and behavior in online communication. Website: https://zhaowang-iit.github.io/.

    Anna Woodard is a postdoctoral scholar in the Department of Computer Science at the University of Chicago, where she is part of Globus Labs.

    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.

    Dr Jai Yu’s research focuses on understanding the neurophysiological mechanisms that support the formation of knowledge. His lab records and analyzes neural data. Prior to joining the University of Chicago, he worked as a data scientist at a Silicon Valley neuromodulation startup where he used advanced data analytics to guide the development of devices for treating neurological conditions.

  • Staff

    Interested in joining the 2022 program as a Lab Coordinator? Check out the job posting here, we are currently accepting applications.

    Summer Lab Leadership

    Kyle Chard is a Research Assistant Professor in the Department of Computer Science at the University of Chicago and Argonne National Laboratory. He has been Program Director of the Data & Computing Summer Lab since its first iteration under CDAC in 2019, and previously oversaw the Summer Internship Program ran by the former Computation Institute.

    He received his Ph.D. in Computer Science from Victoria University of Wellington in 2011. He co-leads the Globus Labs research group which focuses on a broad range of research problems in data-intensive computing and research data management. He currently leads projects related to parallel programming in Python, scientific reproducibility, and elastic and cost-aware use of cloud infrastructure.

    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.

    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.

    Social Impact Track

    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.

    Daniel Grzenda is a Staff Data Scientist supporting the partnership with the 11th Hour Project at the University. He engages with social impact organizations across the domains of energy, food and agriculture, human rights, and marine technology, to support applications of data and computer science. Daniel’s work is focused on increasing the data capacity of these organizations, lowering the barriers to mission driven data science, and empowering these organizations through the development of sustainable technical solutions.

    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.

    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.

    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.

    2022 Lab Coordinators

    I am a third-year PhD student in the Department of Statistics at the University of Chicago. I am broadly interested in causal inference, random graphs, representation learning, and differential privacy. I am very fortunate to be advised by Professor Victor Veitch. Previously, I completed my undergraduate studies at Duke University, where I double majored in mathematics and statistical science, and pursued research opportunities in probability theory, statistical learning theory, and mathematical biology. Prior to Duke, I was born and raised in Bucharest, Romania, where I nurtured my passion for finding rigorous proofs and explanations, by participating in numerous math contests and olympiads.

    I am first year Ph.D. student in the Computer Sciences Department where I am working to develop technologies that facilitate human-computer interactions such as novel coding environments, video games, and teacher facilitation guides. I am particularly passionate about tools that make coding more accessible to young students.

    Weijia He is a fifth-year Ph.D. candidate at the University of Chicago, advised by Professor Blase Ur. Before that, she received her bachelor’s degree in Information Security from Shanghai Jiao Tong University. Her research interests mainly cover security and privacy issues in ubiquitous computing systems, aiming to bridge the gap between existing security systems and real-world users’ challenges. She has published papers at conferences such as USENIX Security, IMWUT (UbiComp), ICSE, EuroS&P, and CHI.

    Anna Harbluk Lorimer is a first year Computer Science PhD student working on internet censorship circumvention and privacy. She once won 60 Montréal-style bagels at a horse whispering contest.

    Derrick is a master’s student in computer science at the University of Chicago working in an RNA epigenetics lab. Previously, he studied neuroscience and philosophy at the same school and worked in a systems neuro lab using brain computer interfaces.

  • FAQ

    For more questions, contact Katie Rosengarten at krosengarten@uchicago.edu.

    • When is the application due?

      The 2022 application is due Sunday February 20th by 11:59pm CT. Late applications will not be considered for review.

      Please subscribe to the DSI Mailing List to receive notifications about the 2022 program and application.

    • Where can I apply?

      The application for the 2022 DSI Summer Lab program can be found here. If you have any issues accessing or submitting the form, please email Katie Rosengarten (krosengarten@uchicago.edu).

    • When will I be notified of my application decision?

      The 2022 application is due Sunday February 20th. Decision notifications will be sent out in early April 2021, no later than April 15th.

      Please subscribe to the DSI Mailing List to receive notifications about the 2022 program and application.

    • Are letters of recommendation required?

      We do not require letters of recommendation for the application.

    • Are there any program prerequisites?

      We do not require any previous research experience to participate in the program. Familiarity in at least one programming language (Python, Java, C++, etc.) is preferred, as well as relevant coursework in areas such as computer science, statistics, and math.

    • What grade or age of students are eligible to apply?

      The following students are eligible to apply to the DSI Summer Lab:

      • High School: current freshmen, sophomores, juniors, and seniors;
      • Undergraduate: current freshmen, sophomores, and juniors;
      • Masters: current 1st year UChicago Masters students.
    • Are international students eligible to apply?

      Yes, international students are eligible apply. However, all students must be authorized to work in the United States and provide all necessary documentation in support of their stipend. To see the documentation required to process stipends, please consult this page. We recommend that all international students check with their home institution’s international affairs office to ensure that they qualify.

    • What are the stipend rates for the program?

      The stipend rates for the 2022 program are below:

      • High School stipend rate: $5,625
      • Undergraduate stipend rate: $6,000
      • Masters stipend rate: $6,375
    • How many hours a week is the program?

      While students are not required to log their hours, we expect each student to work roughly a full-time schedule each week (>37.5 hrs/wk) — i.e. 8am-4pm; 9am-5pm; or 10am-6pm. Schedules are to be consulted with and confirmed by program mentors.

    • Is housing provided?

      Unfortunately, we do not provide housing as part of the program.

    • Will the 2022 program be in person?

      We anticipate that the 2022 program will take place in person. If so, participants will be provided work space on the UChicago campus, and will meet often in the John Crerar Library, home to the Data Science Institute and the Computer Science Department. Unless otherwise agreed upon by their mentor and program leadership, all students are expected to work in the open research space provided — the goal of which is to foster problem-solving and engagement across projects, domains, ages, and skill sets.

      The program administration will consult the recommendations of the University of Chicago and UChicago Medicine to determine the safest format for the Summer Lab 2022 program. We successfully ran the 2021 program remotely, and are prepared to do so for 2022 if necessary.

      We will confirm the format of the program as soon as possible, no later than when decision notifications are sent out in April 2022.

    • What are the 2022 program dates?

      The 2022 program dates are June 13th – August 19th, 2022. Chicago Public Schools (CPS) students may request a late start on June 15th.

    • Where can I see past projects in the program?

      You can view profiles of past projects in the program on our Project Profiles page. Each profile includes details about the student’s mentor, a description of the project, and their final poster. Final videos from the 2021 cohort will be available shortly.

    • If admitted, how will I be paired with a project?

      On the application, we ask for your research areas of interest, as well as self-reported experience and expertise in relevant data science and computational skills and tools. During the application review process, in combination with your research goals and resume, we will use those self-assessments to determine an applicant’s aptitude and eligibility for available research projects.

    • What is the Social Impact Track?

      The Social Impact Track is an opportunity for students to work as a part of a team on a data science project, with topics ranging from energy, food and agriculture, human rights, to marine technology. The projects are scoped and run in coordination with organizations who have been awarded grants by the 11th Hour Project, a grant making foundation serving the nonprofit community. Teams in the social impact track serve as a centralized hub for software and data science for the organizations – providing both open-source and custom data-driven solutions.

    • Who is eligible to participate in the Social Impact Track?

      All student types – high school, undergraduate, and Masters students – are eligible to participate in the Social Impact Track. 1st year UChicago Masters students are only eligible for projects through the Social Impact Track.

    • How can I indicate interest in the Social Impact Track?

      On the application, you will be able to answer “yes” or “no” to the question, “Are you interested in participating in the Social Impact Track?” Selecting “yes” does not limit you to projects within the Social Impact Track, but will flag your interest in potential eligibility for projects within that Track.