Dr. Bahman Moraffah joined the Department of Computer Science at Worcester Polytechnic Institute (WPI) in 2025. His expertise centers on generative models, Bayesian inference, Bayesian nonparametrics, and scalable approximation methods. He focuses on the theoretical foundations of probabilistic machine learning, with recent contributions to diffusion models, variational inference, and Markov Chain Monte Carlo (MCMC) for high-dimensional and structured data. His work addresses the main challenges in uncertainty quantification, representation learning, and latent-variable modeling, while also advancing methodologies for analyzing complex real-world data. Applications of his research include multi-object tracking, biological signal processing, and high-throughput experimental systems.

Before joining WPI, Dr. Moraffah was a faculty member at Arizona State University in the School of Electrical, Computer, and Energy Engineering. His academic background spans engineering, statistics, and machine learning, with an emphasis on scalable and interpretable learning algorithms. He has authored more than 30 peer-reviewed publications and received Best Paper Awards in 2018 and 2021. In addition to scholarly articles, he has written tutorials and technical manuscripts aimed at making advanced probabilistic methods more accessible. His forthcoming book, Diffusion Models: From Theory to Practice, will be published by Springer. Dr. Moraffah has also worked in industry as a Research Scientist, where he led a machine learning team focused on biological data analysis, pattern discovery, and biomedical signal processing. His research has been supported by competitive grants and interdisciplinary collaborations.