The Postdoctoral Scholars Program at the Data Science Institute offers fellowships for Postdoctoral Scholars who wish to deepen their knowledge of cutting-edge data science and computing research while developing additional expertise in a specific, applied problem domain. For more information about how to apply to the program, please visit the Fellowships page.
Patricia ChirilPostdoctoral Scholar, Data Science Institute
Shi FengPostdoctoral Scholar, Data Science Institute, The University of Chicago
Ningzi LiPostdoctoral Scholar, Data Science Institute
Peter LuPostdoctoral Scholar, Data Science Institute
Yuetian LuoPostdoctoral Scholar, Data Science Institute
Tarun ManglaPostdoctoral Scholar, Data Science Institute
Jeffrey NegreaPostdoctoral Scholar, Data Science Institute
Riley TuckerPostdoctoral Scholar, Data Science Institute and Mansueto Institute for Urban Innovation
Anna WoodardPostdoctoral Scholar, Data Science Institute
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.
Bio: Shi Feng is a postdoc fellow at the University of Chicago working on human-in-the-loop and interpretable NLP. Recently, he is focused on investigating the role of interpretability in the alignment of NLP systems. He holds a PhD from University of Maryland, supervised by Jordan Boyd-Graber.
Talk Title: Towards AIs that Help Humans Work Better
Talk Abstract: This talk focuses on developing machine learning models that are maximally useful to humans. Our primary goal is to improve the productivity of human-AI cooperation on important decision making problems by understanding how human and AI interact. In the traditional approach to machine learning, humans are treated as either rivals or teachers. However, machine learning can make up for some of the shortcomings of humans. Treating humans as collaborators opens up several new directions of research.
In the first part of the talk, we use flashcard learning as a testbed and study how human productivity can benefit from consuming information generated by machine learning models. In the second part, we consider humans as decision makers, and investigate how explanations of machine learning predictions can improve the performance of human-AI teams on sequential decision making problems. Finally, we study the limitations of natural language explanations for model predictions, as well as novel methods to improve them.
Ningzi Li received her doctoral degree in Sociology at Cornell University. Her research focuses on organizational theory and sociology of strategy, in particular, how social and institutional factors shape firm strategies. One stream of her work investigates causes and consequences of inter-organizational networks over the course of institutional changes using big data approach. A second stream of her work examines language as an essential component and representation of strategy using natural experiments and NLP methods. She is a recipient of the best paper award from Canadian Sociological Association Economic Sociology Research Cluster, 2019.
Her CV is here.Peter Y. Lu joined the University of Chicago Data Science Institute in 2022 as a postdoctoral scholar and is working at the intersection of physics and machine learning. He received a Ph.D. in physics from MIT in 2022, and an A.B. in physics and mathematics from Harvard in 2016. His research interests include physics-informed machine learning, condensed matter physics, and nonlinear dynamics, and he is more broadly interested in developing new computational methods for modeling and understanding physical systems with an emphasis on incorporating physics-informed priors and developing interpretable representation learning methods.Website: peterparity.github.io
Yuetian Luo joined DSI as a postdoc scholar in July 2022 and has previously completed his doctoral degree in Statistics at the University of Wisconsin-Madison. In his Ph.D., he has been working on high-dimensional statistical inference, tensor data analysis, and non-convex optimization. He is currently interested in distribution-free inference and conformal prediction methods.
Tarun Mangla joined DSI as a postdoctoral scholar in summer 2020, and was previously a PhD student in the School of Computer Science at the Georgia Institute of Technology, co-advised by Mostafa Ammar and Ellen Zegura. His research interests span video streaming, network measurements, and cellular networks. He completed his bachelors in Computer Science and Engineering from Indian Institute of Technology, Delhi (2014) and MS in Computer Science from Georgia Tech (2018). He is a recipient of the Best Paper Award at IFIP TMA, 2018.
Jeffrey’s research focuses on questions of reliability and robustness for statistical and machine learning methods. His work touches on diverse topics including generalization bounds, sequential decision making, learning theory, and Markov chain Monte Carlo. Jeff holds a Ph.D. and an M.Sc. in Statistical Sciences from the University of Toronto, where he was a Vanier Scholar, and a B.Math. from the University of Waterloo. Jeff was a visiting student at the Institute for Advanced Study in 2020, and was recognized as a Rising Star in Data Science by the University of Chicago’s Data Science Institute in 2021.
Riley Tucker is a Post-Doctoral Fellow at the Mansueto Institute for Urban Innovation and the Data Science Institute at the University of Chicago. His work seeks to advance and develop scientific theories about cities and neighborhoods using computational methods to merge traditional data sources with geospatially informed online data sets such as Twitter, Yelp, and Foursquare. His theoretical approach is strongly inspired by the classical Chicago School of sociology, with his dissertation proposing a strategy for measuring social disorganization among ambient populations using geotagged Twitter posts. He is excited to investigate how old ideas formulated at the University of Chicago can help us understand a new and changing world. He holds a PhD in Criminology and Justice Policy from Northeastern University and a BA in Sociology from Temple University.
Former Postdoctoral Scholars
Dylan FitzpatrickPostdoctoral Scholar, Data Science Institute; Research Director, Crime & Education Labs at University of Chicago Urban Labs
Sainyam GalhotraComputing Innovation Postdoctoral Fellow, Data Science Institute, The University of Chicago
Jamie SaxonPostdoctoral Scholar, Data Science Institute
Dylan Fitzpatrick joined the Urban Labs Crime Lab as a Research Director and DSI as a postdoctoral scholar in summer 2020. He is currently a PhD candidate in Machine Learning and Public Policy at Carnegie Mellon University, where he is a member of the Event and Pattern Detection Lab. His research is in development of new ML methods that leverage large spatiotemporal data sets to improve public health, safety, and security. For his dissertation, Dylan has designed novel algorithms for disease outbreak detection and crime forecasting. Most recently, Dylan has focused on patient-level opioid use monitoring, developing a semi-supervised approach for evaluating risk of opioid misuse in settings with few training labels. Dylan was a Researcher at the 2019 NASA Frontier Development Lab, where his research team developed generalizable, multi-basin models of flood susceptibility designed to overcome limitations of physics-based hydraulic and hydrologic models. Dylan earned a BA in Economics from Middlebury College and an MS in Computer Science from Carnegie Mellon University. Dylan’s PhD advisor is Daniel B. Neill, Associate Professor of Computer Science and Public Service and Director of the Machine Learning for Good Laboratory at New York University.
Bio: Sainyam Galhotra is a CI postdoctoral fellow at University of Chicago. He received his Phd from University of Massachusetts Amherst. Previously, he was a researcher at Xerox Research and received his Bachelor’s degree in computer science from Indian Institute of Technology, Delhi. His research is broadly in the area of data management with a specific focus on designing algorithms to not only be efficient but also transparent and equitable in their decision-making capabilities. He is a recipient of the Best Paper Award in FSE 2017 and Most Reproducible Paper Award in SIGMOD 2017 and 2018. He is a DAAD AInet Fellow and the first recipient of the Krithi Ramamritham Award at UMass for contribution to database research.
Talk Title: Designing a Privacy-aware Fair Trade Marketplace for Data
Talk Abstract: A growing number of data-based applications are used for decision-making that have far-reaching consequences and significant societal impact. The increased availability of data has fueled the importance of designing effective techniques for data sharing which are not only scalable to large scale datasets, but also transparent and equitable in their decision-making capabilities. My research focuses on these different facets of data science and are aimed towards designing a fair trade marketplace as a novel data sharing paradigm that can address the unique challenges in the path of meaningful and equitable commoditization of data. In this talk, I will present a multi-pronged approach to deal with different challenges. First, I will present a novel data discovery system that constructs bespoke datasets that satisfy user requirements. Second, I will present the challenges of deploying data marketplaces in practice and ways to mitigate them to maintain robustness of market design against adversarial attacks from different entities (buyers or sellers). Third, I will present a suite of techniques to ensure transparency and inculcate trust of the involved entities.
Jamie Saxon joined DSI as a postdoctoral scholar in summer 2020, and was previously a postdoctoral fellow with the Harris School of Public Policy and the Center for Spatial Data Science of the University of Chicago.
He uses large data sources to measure the availability and use of civic and social resources in American cities. He is particularly interested in mobility among neighborhoods and the consequences of this mobility. He has also studied how gerrymandering affects representation, and developed powerful automated districting software.
He was trained as a particle physicist and was previously an Enrico Fermi Fellow on the ATLAS Experiment on CERN’s Large Hadron Collider at the Enrico Fermi Institute. He worked for many years on electronics and firmware for measuring and reconstructing particle trajectories. As a graduate student at the University of Pennsylvania, he made noteworthy contributions to the discovery and first measurements of the Higgs Boson in the two-photon channel.