Lex Fridman is a research scientist at MIT working on developing deep learning approaches to human sensing, scene understanding, and planning in human-AI interaction paradigms including robotics, crowdsourced human-in-the-loop supervision, and simulated environments. His work focuses on design, development, deployment, and evaluation of learning-based systems that leverage large-scale, real-world data.
Lex received his BS, MS, and PhD from Drexel University where he worked on applications of machine learning in behavioral biometric authentication, activity recognition, scene perception, and multi-robot planning and communication systems. Before joining MIT, Lex was at Google working on deep learning approaches to behavior-based identity recognition. Lex is a recipient of a CHI-17 best paper award and a CHI-18 honorable mention award.