Ph.D. Students on the Job Market

Billy Jin

Billy Jin is a final-year PhD Candidate supervised by David Williamson. His research interests lie in the union of online decision-making, stochastic optimization, and approximation algorithms. A focus of his recent research has been on advice-augmented algorithms, which use some advice or prediction (e.g. from historical data, forecasts, or expert advice), whose quality is unknown. He aims to design algorithms that perform well when the quality is high, yet remain robust in their performance even when the quality is low.

Billy's research has been recognized with several awards, including an NSERC graduate fellowship, the 2023 Student Paper Prize awarded by the INFORMS Decision Analysis Society, and runner-up in the 2023 Student Paper Prize awarded by the INFORMS Optimization Society. Billy graduated from the University of Waterloo in 2018, majoring in Computer Science and Combinatorics & Optimization, where he was the recipient of the Alumni Gold Medal.

Email: bzj3@cornell.edu

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

Miaolan Xie

Miaolan Xie is a fifth-year Ph.D. Candidate advised by Katya Scheinberg at Cornell ORIE. Her research lies at the intersection of data science, machine learning, and stochastic optimization, utilizing tools from statistics and stochastic processes. Specifically, she designs and studies reliable and adaptive optimization algorithms capable of handling messy data in real-world applications while providing strong performance guarantees.

Miaolan has received notable recognition for her research, including Second Place in the 2023 Student Paper Prize awarded by the INFORMS Optimization Society, as well as Second Place in the Flash Talk Competition at the YinzOR Student Conference. Additionally, she was awarded NSF Research Internship Funding in 2022.

Before starting her Ph.D., Miaolan worked as a data scientist in Alibaba Group on the retail supply chain platform team, and prior to that, she worked in Baidu and PwC Consulting. In the summer of 2022, she was a Givens Associate in the Mathematics and Computer Science Division at Argonne National Laboratory, working with Stefan Wild and Matt Menickelly.

Website: https://miaolan.github.io/

Connor Lawless

Connor Lawless is a fifth year Ph.D. student in Operations Research and Information Engineering at Cornell University advised by Oktay Gunluk. His research leverages tools from computational integer programming to design new methodologies for interpretable and fair machine learning. Prior to joining Cornell, he completed his undergraduate at the University of Toronto in Industrial Engineering where he was awarded the Professional Engineers of Ontario Gold Medal in Engineering and the W.S. Wilson Medal in Industrial Engineering. He was selected as Future Leader in Data Science by the University of Michigan Institute for Data Science in 2023.

Connor has worked on a number of industrial projects including interpretable clustering at IBM Research, reinforcement learning based trade execution algorithms at the Royal Bank of Canada, class scheduling for Cornell during COVID-19, and hybrid large language model and constraint programming systems for interactive decision support at Microsoft Research. Outside of his research Connor is passionate about teaching the next generation of data scientists through programs such as iXperience where he has taught introductory data science tools to over 300 undergraduate students around the world.  

Website: https://conlaw.github.io/