Ph.D. Students on the Job Market
A number of ORIE Ph.D. students are looking for employment opportunities.
Raul's research interests lie at the intersection between operations research and machine learning, with an emphasis on Bayesian machine learning methods for sequential decision making. His dissertation work focused on the design and analysis of Bayesian optimization algorithms for problems exhibiting a nested structure. These algorithms have found application in a wide range of domains, such as risk-averse simulation optimization of COVID-19 testing policies, multi-objective portfolio optimization with user preferences, and reinforcement learning-based robot control. Raul's long-term research goal is to develop principled methods for efficient data collection, emphasizing their application to human-in-the-loop optimization problems that arise in materials design, drug discovery, and personalized medicine. He is currently looking for postdoctoral and faculty positions for the next academic year.
Mark's research focuses on optimizing decisions in transportation and service systems via artificial intelligence methods. He developed and implemented deep reinforcement learning algorithms for control optimization in large-scale operating models of ride-hailing platforms, fabrication lines, hospitals, and supply chains. His research contributions were recognized by the INFORMS Applied Probability Society selecting him as one of the finalists for Best Student Paper Prize in 2021. Mark is seeking an industry position related to control optimization in large-scale service systems.
Chamsi Hssaine is a final-year Ph.D. student in the School of Operations Research and Information Engineering at Cornell University, where she is advised by Professor Sid Banerjee. Her research centers around algorithm and incentive design for smart societal systems. Combining rigorous theory, data-driven approaches, and large-scale optimization, her work develops tools for the people-centric design and operations of these systems. In particular, a unique and unifying thread across all of her research is a focus on incorporating more realistic models of behavior under incentives, and better understanding the effect of policy decisions on stakeholders.
Chamsi was selected for the 2020 Rising Stars in EECS workshop at UC Berkeley, as well as the 2020 Rising Scholars conference at the Stanford Graduate School of Business. In 2019, she was a visitor at the Simons Institute for the program on Online and Matching-Based Market Design. Her paper "Real-Time Approximate Routing for Smart Transit Systems" (joint with Sid Banerjee, Noémie Périvier, and Samitha Samaranayake) is a finalist for the 2021 INFORMS Minority Issues Forum Paper Competition. She graduated magna cum laude from Princeton University in 2016, with a B.S. in Operations Research and Financial Engineering.
Yichun's research focuses on various data-driven decision-making problems, including (but not limited to) contextual bandits, reinforcement learning, stochastic optimization and causal inference. She is also interested in application areas like recommendation systems, personalized experiments, policy making and urban planning. She is actively looking for research positions in industry labs, starting Summer/Fall 2022.
Amy B.Z. Zhang
Amy’s research focuses on developing and analyzing approximation algorithms to solve dynamic programming problems, specifically by combining the low-rank infrastructure of state aggregation with PDE analysis, to obtain efficiency and guarantee simultaneously. She also has strong interest in recommender systems, in terms of both the technical and societal aspects, and working to produce solutions that could advance both. She is looking for opportunities to research and build technical solutions in impact-driven teams, especially in machine learning, optimization, and reinforcement learning, starting Fall 2022.
CV: PDF | Text
Xiangyu Zhang's research focuses on developing algorithms to solve large-scale decision-making problems in areas such as dynamic pricing, revenue management, and active learning. He is especially interested in developing computational feasible strategies for real-world problems with extraordinary performance guarantees. He is interested in research roles in the IT or trading industries, starting June 2022.
CV: PDF | Text