
Ph.D. Students
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Trambak Banerjee
Trambak is a PhD student in Statistics at USC Marshall School of Business. His research interests include high-dimensional statistical inference, large scale optimization techniques and computational statistics. He has previously served as a quantitative risk modeler in the financial services sector where he developed statistical models for estimating banking regulatory capital for Credit and Operational risk events.
Hailong Cui
Hailong Cui works at the interface of operations management and data analytics. His research focuses on data-driven prediction, inference, and decision-making with applications to service, retail and sustainable operations. He draws methods from statistical machine learning, econometrics, game theory, and optimization in his research. He is a recipient of the USC Provost’s Ph.D. Fellowship, and prior to his Ph.D. study, he served as a fraud risk manager with American Express, and a credit risk analyst with HSBC.
Joshua Derenski
Josh is a statistics Ph.D. student in the Marshall School of Business. He obtained his bachelor’s degree in statistics at UCLA, where he analyzed energy consumption data for the UCLA Institute for the Environment and Sustainability. His current research interests are time series analysis and functional data analysis.
Greg Faletto
Gregory Faletto is a PhD Student in the statistics group of the Department of Data Sciences and Operations. He is interested in feature selection, causal inference, & modeling with applications in social science and business.
Rong Qing Han
Brian Rongqing Han is a Ph.D. candidate in Operations Management with a strong interest and demonstrated skills in empirical analysis and data-driven modeling based on real-world applications.
Michael Huang
Michael Huang is a PhD student in the Data Science and Operations department in the Marshall School of Business. He previously studied Operations Research at Columbia University where he worked on research in healthcare operations. His research interests are in developing better optimization methods and algorithms to improve decision making under uncertainty.
Shobhit Jain
Wilson Lin
Wilson Lin is a Ph.D. student in the Data Sciences and Operations department at the USC Marshall School of Business. His current research interests include applied probability and decision making under uncertainty, with applications in policy and healthcare. He earned his M.S. in Operations Research Engineering and B.S. in Industrial and Systems Engineering at the University of Southern California.
