Ph.D. Students on the Job Market

Vasilis Charisopoulos

Vasilis Charisopoulos is a final year PhD candidate advised by Damek Davis. He works on optimization and numerical linear algebra methods for machine learning and scientific computing. In particular, he studies what drives the success of heuristically motivated methods, which often find optimal solutions of nonconvex optimization problems despite NP-hardness, as well as the tradeoffs between communication, statistical efficiency, and privacy that arise when these algorithms are deployed in massively distributed computing environments. In support of this research, Vasilis was awarded the 2020-2021 Andreas G. Leventis scholarship and was a Cornell University nominee for the 2021-2022 Google PhD fellowship.

Outside research, Vasilis is passionate about teaching and access to education. He was a recipient of the Cornelia Ye outstanding teaching assistant award in 2021, and served as instructor for Cornell's Prison Education Program in the Fall of 2019.

He is looking for postdoctoral and faculty positions for the 2023-2024 academic year.

Email: vc333@cornell.edu
Website :https://vchariso.com

X.Y. Han

X.Y. Han is a PhD Candidate supervised by Adrian S. Lewis at Cornell ORIE; previously, he earned an MS from Stanford Statistics---where he conducted still-ongoing research under David L. Donoho---and a BSE from Princeton ORFE. He discovered the now-widely-studied Neural Collapse phenomenon in deep neural network training and invented the Survey Descent method for nonsmooth optimization. For these works, he received the ICLR 2022 Outstanding Paper Award and was a finalist for the ICCOPT 2022 Best Paper Prize for Young Researchers. He maintains real-world collaborations with the Frick Art Reference Library in NYC, the USC Keck School of Medicine, and the Veolia North America utilities company. Broadly, his research on nonsmooth optimization and deep learning mathematically analyzes and builds new methods based on realistic phenomena observed in modern computational practices.

Email: xh332@cornell.edu

Website : https://xyhan.me 

Yichun Hu

Yichun’s research centers around fast and reliable data-driven decision-making. In both online/sequential and offline/batch settings, she works to understand (1) under what problem structures we can hope to achieve instance-specific fast regret rates,  (2) how to exploit these problem structures to design algorithms that achieve fast rates, and (3) how to incorporate additional practical considerations into the algorithm design, e.g., balancing competing objectives. On the theoretical side, she leverages techniques from machine learning, stochastic optimization, and statistics to study the statistical limits of data-driven decision-making. On the empirical side, she focuses on applications in important social domains, including healthcare and experimentation platforms, and collaborates with practitioners to discover novel problems and put algorithms into use. 

Yichun’s work has been published in prestigious journals and conferences (Management Science, Operations Research, COLT, etc.), and was recognized as a finalist for the 2020 INFORMS Applied Probability Society Best Student Paper Competition. 

Email: yh767@cornell.edu
Website: https://yichunhu.github.io/ 

Billy Jin

Billy Jin is a fifth-year PhD student in ORIE at Cornell University, advised by David Williamson. His research centers around data-driven decision making; in particular, in incorporating more realistic models of uncertainty, and designing algorithms that are robust to model errors. His recent contributions include work on pareto-optimal prediction-augmented algorithms, equity and fairness in online resource allocation, and better approximation algorithms for structured instances of the traveling salesman problem. Billy is a recipient of the NSERC graduate fellowship, and his work has appeared in venues such as NeurIPS, SODA, and IPCO. He graduated from the University of Waterloo in 2018 with a B.Math in Combinatorics & Optimization and Computer Science, where he was the recipient of the Alumni Gold Medal. 

Email: bzj3@cornell.edu

Website : https://people.orie.cornell.edu/bzj3/ 

Sean Sinclair

Sean Sinclair is a final-year Ph.D. student in the School of Operations Research and Information Engineering at Cornell University, where he is co-advised by Professors Christina Yu and Sid Banerjee.  His research focuses on developing algorithms for data-driven sequential decision making in societal applications.  He bridges algorithmic techniques in reinforcement learning to an operations management perspective with an emphasis on models, data uncertainty, and objectives.  Recent contributions include instance-specific optimal regret guarantees for nonparametric reinforcement learning, Pareto-optimal fair resource allocation, and data-efficient algorithms for cloud compute allocations.  Complementing this, he also designs open-source code instrumentation and methodology to empirically analyze the multi-criteria performance of algorithms on these problems. 

His paper, "Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve" is a finalist for the 2022 INFORMS Diversity, Equity, and Inclusion Student Paper Competition.  Sean was selected for the 2022 Future Leaders Summit at the Michigan Institute for Data Science.  In 2020 and 2022 he was a visitor at the Simons Institute for the programs on the Theory of Reinforcement Learning and Data-Driven Decision Processes.  He graduated from McGill University in 2015, with a B.S. in Honours Mathematics and Computer Science. 

Email: srs429@cornell.edu

Website:  https://people.orie.cornell.edu/srs429/