Lyle Regenwetter is a PhD candidate developing generative AI algorithms for design.

Lyle grew up in central Illinois and earned bachelor’s degrees in mechanical engineering and electrical engineering from the University of Illinois. He spent a considerable amount of his high-school and undergraduate years designing robotic systems. While living the highs and lows of countless design projects, Lyle became intimately familiar with the toils of hardware design.

After graduating and coming to MIT, Lyle welcomed an opportunity to reflect on his experience as a designer. Realizing that many of his most frustrating moments were caused by tedious and repetitive tasks, he reasoned that automated tools could conceivably alleviate key frustrations for many designers. As such, Lyle excitedly embarked on a new journey to develop generalizable AI-driven tools to accelerate design and empower designers.

Lyle is now a fourth-year graduate student in the Design Computation and Digital Engineering (DeCoDE) Lab and received his M.S. in mechanical engineering from MIT in 2022. Lyle develops methods to incorporate design requirements (constraints, performance objectives, etc.) into the training process of generative AI models. Lyle also studies effective methods to develop datasets on which to train generative AI models.

Lyle is equally passionate about disseminating the AI-driven design tools that he and others develop. His tools and algorithms have been incorporated into professional design software. He has also co-developed MIT’s 2.155/6 class (AI & ML for Engineering Design), where he has built dozens of instructional demos to teach MIT’s scholars to leverage AI tools for design applications.