Haofeng Zhang

Since 2024, I have been a researcher with the Machine Learning Research Team at Morgan Stanley. My work lies at the intersection of machine learning, optimization, and statistics, bridging foundational research with the development of practical, scalable ML systems. My research spans language and vision models, time-series forecasting, recommendation systems, and other data-driven applications.

My academic research advances the foundations of trustworthy prediction and decision-making under uncertainty. My primary research interests include:

• Trustworthy machine learning with applications in large language & vision models with a focus on uncertainty, robustness, and reliability.
• Data-driven decision-making, e.g., optimization under uncertainty, decision-focused learning, sequential decision-making, recommendation systems.
• Uncertainty quantification, e.g., Monte Carlo methods, probabilistic and generative modeling, model uncertainty and calibration, distributional robustness and shift, Bayesian inference and optimization.
• Machine learning for finance.

In parallel, in my applied research, I contribute across the full machine learning lifecycle, from algorithm development and experimental design to scalable deployment, evaluation, and ongoing model monitoring.

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.