I am an Assistant Professor in the Edwardson School of Industrial Engineering at Purdue University. I am actively seeking motivated PhD students to join my research group—please reach out if you are interested in developing reliable and adaptive optimization algorithms with applications in machine learning, healthcare, or other data-driven domains.
I completed my Ph.D. in Operations Research and Information Engineering at Cornell University, with Professor Katya Scheinberg. Prior to joining Purdue, I was a postdoctoral Principal Researcher at the University of Chicago Booth School of Business, affiliated with the Healthcare Initiative, working with Professor Dan Adelman. My research focuses on designing theoretically-grounded optimization algorithms that can handle messy data, utilizing tools from statistics and stochastic processes for applications in machine learning and healthcare.
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. 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. 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.
* indicates authorship in alphabetical order by last name.
| * A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization |
| Albert Berahas, Miaolan Xie, Baoyu Zhou. |
| • SIAM Journal on Optimization, 2025 |
| * Sample Complexity Analysis for Adaptive Optimization Algorithms with Stochastic Oracles |
| Billy Jin, Katya Scheinberg, Miaolan Xie. |
| • Mathematical Programming, 2025 |
| • 🏆 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. |
| • SIAM Journal on Optimization, 2024 |
| * A Stochastic Quasi-Newton Method in the Absence of Common Random Numbers |
| Matt Menickelly, Stefan Wild, Miaolan Xie. |
| • Submitted, 2024 |
| * 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 |
Reliable and Adaptive Stochastic Optimization in the Face of Messy Data (with Highly Corrupted Inputs and Heavy-Tailed noises)
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
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