Faez Ahmed
Associate Professor and Doherty Chair in Ocean Utilization, Department of Mechanical Engineering

Who they work with
Faez Ahmed is an associate professor and the Doherty Chair in Ocean Utilization in the Department of Mechanical Engineering at MIT, where he leads the Design Computation and Digital Engineering (DeCoDE) lab. His research focuses on developing new machine learning and optimization methods to study complex engineering design problems. His recent work includes proposing automated design synthesis methods to generate novel high-performance designs, creating the first provably optimal algorithm for the diverse matching problem, and building computationally efficient ways for combining physics with human expert knowledge to design new products.
Before joining MIT, Ahmed was a postdoctoral fellow at Northwestern University and completed his PhD in mechanical engineering at the University of Maryland. He also worked in the railway and mining industry in Australia, where he pioneered data-driven predictive maintenance and renewal planning efforts.
Selected Publications
- Yu, R. T.-Y., Picard, C., & Ahmed, F. (2025). Fast and accurate Bayesian optimization with pre-trained transformers for constrained engineering problems. Structural and Multidisciplinary Optimization, 68, Article 66.
- Heyrani Nobari, A, Regenwetter, L, & Ahmed, F. (2024). Towards Domain-Adaptive, Resolution-Free 3D Topology Optimization With Neural Implicit Fields. Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 3A: 50th Design Automation Conference (DAC).
- Regenwetter, L., Giannone, G., Srivastava, A., Gutfreund, D., & Ahmed, F. (2024). Constraining generative models for engineering design with negative data. Transactions on Machine Learning Research (TMLR).
Media
- May 9, 2025: MIT News, School of Engineering faculty and staff receive awards for winter 2025
- November 12, 2024: MIT News, 3 Questions: Inverting the problem of design
- August 20, 2024: MIT Technology Review, How to fine-tune AI for prosperity