Faez Ahmed

Faez Ahmed is an assistant professor 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

• Giannone, G., Regenwetter, L., Srivastava, A., Gutfreund, D., & Ahmed, F. (2023). Learning from Invalid Data: On Constraint Satisfaction in Generative Models. Proceedings of the Conference on Neural Information Processing Systems (NeurIPS) workshop.
• Giannone, G., Srivastava, A., Winther, O., & Ahmed, F. (2023). Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation. Proceedings of the Conference on Neural Information Processing Systems (NeurIPS).
• Giannone, G., Srivastava, A., & Ahmed, F. (2023). Diffusion Optimization Models with Trajectory Alignment for Constrained Design Generation. Proceedings of the Conference on Neural Information Processing Systems (NeurIPS).

Media

• • August 20, 2024: MIT Technology Review, How to fine-tune AI for prosperity
  • May 3, 2024: MIT News, Exploring frontiers of mechanical engineering
  • October 19, 2023: MIT News, To excel at engineering design, generative AI must learn to innovate, study finds