Welcome to my website! I am a final-year PhD candidate in the Operations Research Center at MIT where I am advised by Professor Rahul Mazumder. Prior to graduate school, I was a Data and Applied Scientist at Microsoft and I graduated Cornell with a B.S. in Operations Research.

My research lies at the intersection of computational statistics, operations research, and data science, where I focus on leveraging techniques from discrete and combinatorial optimization to develop efficient, interpretable algorithms for machine learning. I am particularly interested in extracting interpretable models from complex black boxes, with considerations to multiple objectives such as accuracy, compactness, and stability. On the applied side, I use these methods to tackle data science problems in healthcare and digital platforms.

I am on the 2025–26 academic job market!

Please find my CV here.

Selected Awards

• 2025 INFORMS Quality, Statistics and Reliability Section Best Student Paper Competition 1st Place
• 2025 American Statistical Association Statistical Computing Section Best Student Paper
• 2024 INFORMS Data Mining Society Best Student Paper Competition 1st Place
• 2025 MIT Health and Life Sciences (HEALS) Collaborative Graduate Fellowship
• 2025 ISyE-MS&E-IOE Rising Star

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
• Major Revision in the Journal of the American Statistical Association (JASA), revision submitted

Locally Transparent Rule Sets for Explainable Machine Learning, 2025.

• Brian Liu and Rahul Mazumder
• R&R in Operations Research
• 📌 2025 INFORMS Quality, Statistics and Reliability Section Student Paper Competition 1st Place.

Publications

Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests, 2024, arXiv.

• Brian Liu and Rahul Mazumder
• 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

Teaching

Massachusetts Institute of Technology

• Teaching Assistant, 15.081 Analytics Edge (MBA)
  Spring 2025
• Teaching Assistant, 15.072 Advanced Analytics Edge (Graduate MBAn)
  Fall 2024
• Teaching Assistant, 15.075 Statistical Thinking and Data Analysis (Undergraduate)
  Spring 2024
• Teaching Assistant, 15.072 Advanced Analytics Edge (Graduate MBAn)
  Fall 2023
• Teaching Assistant, 15.071 Analytics Edge (MBA)
  Fall 2023
• Teaching Assistant, 15.067 Engineering Statistics and Data Science (Graduate LGO)
  Summer 2023

Cornell University

• Teaching Assistant, ORIE 4740: Introduction to Statistical Learning
  Spring 2020
• Teaching Assistant, ORIE 3300: Optimization I
  Fall 2018

Talks

• Joint Statistical Meeting, August 2025
  • Efficient Algorithms for Transparent Additive Models
• MIT Industrial Liaison Program Webinar: Unlocking the Value of Employee Wellness, July 2025
  • Explainable AI for Digital Mental Health
• 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