Ph.D. Students

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.

I am primarily interested in data-driven prediction, inference and decision-making in the context of online retail and consumer returns, sustainable operations, incentive design and worker productivity. In my research, I utilize tools from optimization, game theory, machine learning, high-dimensional statistics and econometrics. During my Ph.D. studies, I have co-authored three journal papers that have been published or accepted for publication in M&SOM and EJOR, and one more journal paper that is currently under 2nd round review at Management Science. Prior to my Ph.D. studies, I worked as a fraud risk manager with American Express, and as a credit risk analyst with HSBC.

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.

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.

Brian Rongqing Han a Ph.D. Candidate at the Department of Data Sciences and Operations, USC Marshall School of Business. His research addresses how e-commerce platforms leverage the vast network of logistics infrastructure as an offline platform for commercial activities such as promotion and advertising of physical products. He uses field experiment combined with machine learning and data-driven optimization to explore innovative business models, generate business value, and transform the industry.

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.

Wilson Lin is a Ph.D. student in the Data Sciences and Operations department at the USC Marshall School of Business. His research interests include empirical and behavioral operations management, with applications in public health 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.