Ph.D. Students on the Job Market
A number of ORIE Ph.D. students are looking for employment opportunities.
Yilun's research lies in the area of sequential decision-making, where he applies techniques of applied probability and combinatorial optimization to the design and analysis of efficient and provably near optimal solutions, addressing "curse of dimensionality" issues. His work was awarded first place in the 2019 INFORMS Nicholson Student Paper Competition. Yilun is a final-year Ph.D. candidate and is actively seeking a faculty/post-doc position.
Lijun's research lies at the intersection of optimization, statistics, and machine learning, where he works on solving large-scale and high dimensional optimization problems. By exploring ideas and techniques such as Frank-Wolfe, strict complementarity, and the leave-one-out argument in these fields, he is able to design computationally and statistically efficient algorithms for both classical convex optimization problems such as semidefinite programming, and newly arising nonconvex problems. He is mainly looking for a postdoctoral position in the next academic year.
Ben Grimmer's research focuses on establishing theoretical foundations for modern optimization problems (e.g., machine learning) beyond the scope of classical approaches, supported by an NSF graduate fellowship. He is currently interested in either research-oriented postdoc opportunities or staying at Cornell for a sixth year, teaching additional classes.
Amy (B.Z.) Zhang
My research focuses on designing principled yet simple approximation to dynamic programming (DP) problems, specifically by combining low-rank infrastructure for efficiency with CLT-style reduction for accuracy analysis. I am expecting to graduate in December 2021 and am interested in industry roles in developing/applying pattern mining and optimization models. (More generally, I have background in probability, machine learning, graph and network theory, and economics.)
CV: PDF | Text
I am interested in researching at the interface of statistical machine learning and operations research in order to inform reliable data-driven decision-making, motivated by important application areas such as e-commerce, healthcare, and policy. Specifically, I have worked on robust causal-effect-maximizing personalized decision rules in view of unobserved confounding and credible impact evaluation for algorithmic fairness. I am interested in academic opportunities.