I am an Assistant Professor in the Daniel J. Epstein Department of Industrial and Systems Engineering (ISE) at the University of Southern California. I received my Ph.D. from the Department of Management Science and Engineering (MS&E) at Stanford University, where I was fortunate to be co-advised by Prof. Peter Glynn and Prof. Jose Blanchet. Prior to my doctoral studies at Stanford, I completed my B.S. degree at Cornell Engineering, majoring in Operations Research and Information Engineering (ORIE).

Research Interests

I am interested in a wide range of research areas within applied probability, machine learning, and simulation. My work focuses on the design and analysis of algorithms for learning and controlling dynamic engineering systems, with applications in management science and operations research. In particular, I address the reliability and scalability challenges that arise in contemporary problems. Key areas of my research include:

• Designing, analyzing, and implementing sample-efficient estimators capable of learning key system characteristics.
• Achieving reinforcement learning (RL) and control of stable stochastic systems.
• Developing statistically tractable data-driven models and algorithms for robust dynamic policy learning and RL.
• Establishing theoretical foundations for robust Markov decision processes.
• Advancing deep learning techniques for policy learning by leveraging simulation methodologies and applied probabilistic tools.