Haofeng Zhang

Since 2024, I have been a researcher within the Machine Learning Research Team at Morgan Stanley. My work bridges research and applied innovation, spanning modern machine learning, optimization, and statistics.

On the applied side, I serve as a full-stack machine learning researcher and engineer, contributing across the entire ML lifecycle, from core algorithmic research and experimental design to scalable deployment and ongoing model monitoring. I collaborate closely with multiple divisions to design and implement state-of-the-art forecasting and decision-support models.

On the academic side, I conduct research that advances the foundations of data-driven prediction and decision-making. My primary research interests include:

• Machine learning methodologies, with an emphasis on uncertainty and decision-making.
• Data-driven decision-making, including optimization under uncertainty and sequential decision-making.
• Simulation and uncertainty quantification, including model uncertainty, Monte Carlo methods, and generative models.

My Google Scholar can be found here. Selected research can be found here. Please feel free to connect with me on LinkedIn here.

I obtained my Ph.D. degree in 2024 from Department of Industrial Engineering and Operations Research at Columbia University, advised by Professors Henry Lam and Adam Elmachtoub.