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.

Publications

• Heyrani Nobari, A., Chen, W., Ahmed, F. (2021). PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design. ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
• Regenwetter, L., Curry, B., Ahmed, F. (2021). BIKED: A Dataset and Machine Learning Benchmarks for Data-Driven Bicycle Design. Proceedings of the ASME IDETC/CIE.
• Heyrani Nobari, A., Chen, W., Ahmed, F. (2021). Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis. Proceedings of the ASME IDETC/CIE.
• Chen, W., Ahmed, F. (2020). PaDGAN: Learning to Generate High-Quality Novel Designs. Journal of Mechanical Design.
• Wang, L., Chan, Y., Ahmed, F., Liu, Z., Zhu, P., Chen, W. (2020). Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems. Computer Methods in Applied Mechanics and Engineering.
• Ahmed, F., Dickerson, J., Fuge, M. (2020). Forming Diverse Teams from Sequentially Arriving People. Journal of Mechanical Design.