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Assistant Professor
Biosketch
I am an assistant professor of Industrial Engineering at Pitt, where I lead the Sociotechnical Systems Research Lab. I am also affiliated with Pitt’s Intelligent Systems Program (ISP) and Institute for Cyber Law, Policy, and Security (Pitt Cyber), as well as the MIT Initiative on the Digital Economy (IDE). Before joining Pitt in the fall of 2020, I was a postdoc with joint appointments at the MIT Institute for Data, Systems, and Society (IDSS) and Sloan (co-advised by Elchanan Mossel and Dean Eckles). I earned my Ph.D. in Electrical and Systems Engineering from Penn (advised by Ali Jadbabaie), along with a master’s in Statistics from Wharton.
My research lies at the intersection of networks, data, and decision sciences, drawing on applied probability, statistics, algorithms, and decision and game theory to study information, inference, and intervention design in large-scale sociotechnical systems. I am especially interested in the critical role that information plays in the operation of these systems and its societal implications, particularly for privacy, fairness and information integrity (e.g., social learning and the spread of online harms). I pursue these questions in settings such as online platforms and markets, public health, production networks, and public institutions, with a growing emphasis on how AI reshapes knowledge work and collective decision making. I have published in the Proceedings of the National Academy of Sciences, Nature Human Behaviour, Nature Communications, the journals Operations Research and Automatica, and several IEEE transactions and control theory journals (see my publications).
My teaching at Pitt spans statistics, probability, algorithms, and data analytics, including AI for Social Good, which grew out of a course I developed for the Pitt Year of Data and Society. Alongside research and teaching, my service to Pitt and the wider research community includes advising researchers on ethical questions involving data and AI through Pitt’s Research Ethics Consultation Service and contributing to scientific peer review and conference organization, for example as an area chair for AAAI-27’s AI for Social Impact (AISI) track. My work has been supported by the National Science Foundation, the Centers for Disease Control and Prevention, the U.S. Army, Meta, and internal university funding. See my CV for further details on grants, awards, and service activities.
Interests
Theory and Methods: applied probability (random processes on graphs and networks, network contagion and percolation); applied statistics (estimation, causal inference, forecasting, and calibration); statistical decision theory; randomized algorithms; differential privacy; statistical and computational learning theory; distributed inference, estimation, and online learning; network science and statistical network analysis; agent-based modeling and simulation; social learning, group decision making, and collective intelligence; mathematical theories of social behavior, judgment, and decision making; distributed and multi-agent systems, dynamics, and control.
Application Areas: ethical and responsible algorithm design; privacy, fairness, and information integrity in sociotechnical systems (healthcare systems, social and economic networks); social platforms, e-commerce, and online marketplaces; optimal and distributed interventions in social and economic networks; public health and biomedicine (disease surveillance, biomarker discovery, phylogeny, public health emergencies, and diseases of despair); policy evaluation and decision support (risk-benefit analysis, cost-effectiveness, value of information, disease screening and management, emergency response, and command and control); production networks and sustainability operations; AI-mediated knowledge work and collective decision making (human–AI collaboration and multi-agent coordination).