Miaolan Xie



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I am a fifth year Ph.D. Candidate in Operations Research and Information Engineering at Cornell University, where Iā€™m fortunate to be advised by Professor Katya Scheinberg. I obtained my Bachelor of Mathematics with majors in Pure Mathematics and Combinatorics and Optimization from the University of Waterloo. I completed my Master's degree with Levent Tunçel in Combinatorics and Optimization at the University of Waterloo.

My research lies at the intersection of data science, machine learning, and stochastic optimization, utilizing tools from statistics and stochastic processes. Specifically, I design and study reliable and adaptive optimization algorithms capable of handling messy data in real-world applications while providing strong performance guarantees.

I have received notable recognition for my 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, I was awarded NSF Research Internship Funding in 2022.

Before starting my Ph.D., I worked as a data scientist in Alibaba on the retail supply chain platform team, and prior to that I worked in Baidu and PwC Consulting. I also had an internship at Scotia-bank in Toronto in 2016. In the summer of 2022, I was a Givens Associate in the Mathematics and Computer Science Division at Argonne National Laboratory, working with Stefan Wild and Matt Menickelly.

I am on the 2023-2024 academic job market!

Publications

* indicates authorship in alphabetical order by last name.

* Sample Complexity Analysis for Adaptive Optimization Algorithms with Stochastic Oracles
Billy Jin, Katya Scheinberg, Miaolan Xie.
• To appear in Mathematical Programming, 2023
• šŸ† Second Place for 2023 Student Paper Prize of INFORMS Optimization Society

* High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles
Billy Jin, Katya Scheinberg, Miaolan Xie.
• To appear in SIAM Journal on Optimization (SIOPT), 2024

* A Sequential Quadratic Programming Method with High Probability Complexity Bounds
  for Nonlinear Equality Constrained Stochastic Optimization
Albert Berahas, Miaolan Xie, Baoyu Zhou.
• Under 2nd Round Review at SIAM Journal on Optimization (SIOPT), 2023

* A Stochastic Quasi-Newton Method in the Absence of Common Random Numbers
Matt Menickelly, Stefan Wild, Miaolan Xie.
• Preprint, 2023

* Stochastic Adaptive Regularization Method with Cubics:
   A High Probability Complexity Bound
Katya Scheinberg, Miaolan Xie.
• Winter Simulation Conference, 2023

* High Probability Complexity Bounds for Line Search Based on Stochastic Oracles
Billy Jin, Katya Scheinberg, Miaolan Xie.
• NeurIPS, 2021

ControlBurn: Feature Selection by Sparse Forests
Brian Liu, Miaolan Xie, Madeleine Udell.
• KDD, 2021

* Stochastic Adaptive Regularization Method with Cubics: A High Probability Complexity Bound
Katya Scheinberg, Miaolan Xie.
• NeurIPS OPT Workshop, 2022

* High Probability Step Size Lower Bound for Adaptive Stochastic Optimization
Billy Jin, Katya Scheinberg, Miaolan Xie.
• NeurIPS OPT Workshop, 2021

Inner Approximation of Convex Cones via Primal-Dual Ellipsoidal norms
Miaolan Xie.
• Master's Thesis

Talks

Reliable Adaptive Stochastic Optimization for Messy Data: with High Probability Guarantees

High Probability Complexity Bounds for Adaptive Optimization Methods with Stochastic Oracles

Stochastic Adaptive Regularization Method with Cubics: A High Probability Complexity Bound

High Probability Iteration and Sample Complexity for Adaptive Step Search via Stochastic Oracles

High Probability Complexity Bounds for Line Search Based on Stochastic Oracles

High Probability Step Size Lower Bound for Adaptive Stochastic Optimization

ControlBurn: Feature Selection by Sparse Forests

Teaching & Service

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