The Data Science and Applied AI Postdoctoral Scholars Program at the Center for Data and Computing 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.
Dylan FitzpatrickPostdoctoral Scholar, Center for Data and Computing; Research Director, Crime & Education Labs at University of Chicago Urban Labs
Shi FengPostdoctoral Scholar, Center for Data and Computing
Sainyam GalhotraPostdoctoral Researcher, Center for Data and Computing
Ningzi LiPostdoctoral Scholar, Center for Data and Computing
Tarun ManglaPostdoctoral Scholar, Center for Data and Computing
Jamie SaxonPostdoctoral Scholar, Center for Data and Computing
Dylan Fitzpatrick will be joining the Urban Labs Crime Lab as a Research Director and CDAC 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.
Sainyam Galhotra is a PhD candidate in the College of Information and Computer Sciences at University of Massachusetts Amherst. Before joining PhD, 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.
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 will join CDAC as a postdoctoral scholar in summer 2020, and is currently 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 will join CDAC 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.