Welcome to my website! I am a rising fifth-year PhD candidate in the Operations Research Center at MIT where I am advised by Professor Rahul Mazumder. My research lies in the intersection of statistical machine learning and discrete optimization, and I focus on developing efficient and explainable algorithms for data science and ML. I am also interested in developing interpretable ML methods for healthcare and public policy.
Prior to graduate school, I was a Data and Applied Scientist at Microsoft where I worked on online advertising and I am originally from the San Francisco Bay Area.
I am on the 2025–2026 academic job market!
Please find my CV here.
Selected Awards
- 2025 MIT Health and Life Sciences (HEALS) Collaborative Graduate Fellow
- 2025 ISyE-MS&E-IOE Rising Star
- 2025 American Statistical Association Statistical Computing Section Best Student Paper Winner
- 2024 INFORMS Data Mining Society Best Student Paper Competition 1st Place
Working Papers
TreePrompt: Distilling Boosted Tree Ensembles for In-Context Learning in Large Language Models, 2025.
- Brian Liu and Rahul Mazumder
- Preliminary version appeared in The First Structured Knowledge for Large Language Models Workshop (KDD 2025)
Under Review
Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives, 2025, arXiv.
- Brian Liu, Rahul Mazumder, and Peter Radchenko
- Submitted to Journal of the American Statistical Association (JASA)
Locally Transparent Rule Sets for Explainable Machine Learning, 2025.
- Brian Liu and Rahul Mazumder
- R&R at Operations Research
Publications
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests, 2024, arXiv.
- Brian Liu and Rahul Mazumder
- To appear in Journal of Machine Learning Research (JMLR)
MOSS: Multi-Objective Optimization for Stable Rule Sets, 2025, arXiv.
- Brian Liu and Rahul Mazumder
- 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
- 📌 2024 INFORMS Data Mining Society Best Student Paper Competition 1st Place.
FASTopt: An Optimization Framework for Fast Additive Segmentation, 2024, arXiv.
- Brian Liu and Rahul Mazumder
- 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
- 📌 2025 American Statistical Association Statistical Computing Student Paper Competition Winner.
FIRE: An Optimization Framework for Fast Interpretable Rule Extraction, 2023, arXiv.
- Brian Liu and Rahul Mazumder
- 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
ForestPrune: Compact Depth-Pruned Tree Ensembles, 2023, arXiv.
- Brian Liu and Rahul Mazumder
- 26th International Conference on Artificial Intelligence and Statistics (AISTATS)
ControlBurn: Feature Selection by Sparse Forests, 2021, arXiv.
- Brian Liu, Miaolan Xie, and Madeleine Udell
- 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
Modeling the Risk of In-Person Instruction During the COVID-19 Pandemic, 2024, arXiv.
- Brian Liu*, Yujia Zhang*, Shane Henderson, David Shmoys, and Peter Frazier
- INFORMS Journal of Applied Analytics
Modeling for COVID-19 College Reopening Decisions: Cornell, A Case Study, 2022, paper.
- Peter Frazier, J. Massey Cashore, Ning Duan, Shane G. Henderson, Alyf Janmohamed, Brian Liu David B. Shmoys, Jiayue Wan, and Yujia Zhang
- Proceedings of the National Academy of Sciences
Talks
- MIT Sloan Health Systems Initiative Annual Workshop, October 2024
- Interpretable Machine Learning Methods for Predicting Telemental Health Outcomes
- INFORMS Annual Meeting, October 2024
- An Optimization Framework for Fast Additive Segmentation in Transparent ML
- ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2024
- An Optimization Framework for Fast Additive Segmentation in Transparent ML
- International Symposium on Mathematical Programming, July 2024
- An Optimization Framework for Fast Additive Segmentation in Transparent ML
- US Census Bureau Center for Statistical Research and Methodology, July 2024
- Making Tree Ensembles Interpretable
- ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2023
- Fast Interpretable Rule Extraction
- INFORMS Annual Meeting, October 2022
- Depth-Pruning Tree Ensembles
- ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2021
- Feature Selection with Sparse Forests