Miaolan Xie is a Ph.D. candidate in operations research and information engineering at Cornell University, where she is fortunate to be advised by Professor Katya Scheinberg. She obtained her Bachelor of Mathematics with majors in pure mathematics and combinatorics and optimization from the University of Waterloo. She completed her Master's degree with Levent Tunçel in combinatorics and optimization at the University of Waterloo.
Before starting her Ph.D., Miaolan worked as a data scientist in Alibaba on the retail supply chain platform team, and prior to that she worked with Baidu and PwC Consulting. She also had an internship at Scotia-bank in Toronto in 2016. 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.
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.
Reliable and adaptive stochastic optimization algorithms are designed to handle messy real-world data while ensuring strong performance guarantees. These aspects are crucial in real-world applications, especially in domains involving data science and machine learning: the algorithm needs to (1) be reliable when dealing with unreliable or messy data (i.e., data with biases, noise, errors, and adversarial corruptions) which is often the case in practical scenarios; (2) retain strong performance guarantees, efficiently utilize available data, and achieve near-optimal results fast; and (3) be user-friendly and adaptive to specific problem instances and landscape by design.
I am in general very interested in foraging, gardening, hiking and cooking. Recently, I became especially interested in foraging and have already found wild strawberry, broadleaf and narrow leaf plantain, ground ivy, clover and dandelion in our backyard!
Cornell ORIE has an impressively strong and diverse faculty group. During my visit to the department at the open house, I was impressed by the friendly and cheerful atmosphere here.
After completing a one-year postdoc following my graduation, I will be joining the Purdue University Industrial Engineering department as a tenure-track faculty member next summer.
Cornell provides an amazing selection of resources, featuring a wide range of workshops and initiatives. Participating in these activities is a fantastic opportunity to make new friends across various departments. This is an area I wish I had explored more during my time at Cornell.
In his TEDxCornellUniversity talk “Fairness Through Algorithmic Thinking: Designing Better Congressional Districts and Elections,” David Shmoys discusses optimization tools to create fairer... Read more about Using algorithmic thinking to fight gerrymandering
Raul Astudillo wants to help machines and people make better decisions. Toward that end, he studies Bayesian methods for efficient sequential decision-making for problems where there is a nested... Read more about Making Bayesian optimization algorithms more efficient
Sean Sinclair, a Ph.D. student in Cornell Engineering’s School of Operations Research and Information Engineering (ORIE), sees the entire world as a mix of probability and optimization problems.... Read more about Math with a purpose—using probability and statistics for the greater good
