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The Data Science and Applied AI 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.

Postdoctoral Scholars

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: 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.

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.

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.

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.

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 is committed to developing resources for computational social science research, and has taught programming and statistics for masters’ students in public policy.

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.

His CV is here; you can also find him on LinkedIn or GitHub.