Lex Fridman (pronounced: Freedman)
I'm an AI researcher working on autonomous vehicles, human-robot interaction, and machine learning at MIT and beyond. I'm hiring.
Teaching: deeplearning.mit.edu
Podcast: Lex Fridman Podcast
Sample Conversations: Elon Musk, Jack Dorsey, Richard Dawkins, Leonard Susskind, Noam Chomsky, Eric Weinstein, Roger Penrose, Stephen Wolfram, Sean Carroll, Bjarne Stroustrup, Donald Knuth, and my dad Alexander Fridman.

Connect with me @lexfridman on Twitter, LinkedIn, Instagram, Facebook, YouTube, Medium.

Outside of research and teaching, I enjoy:
- playing guitar & piano
- practicing jiu jitsu & judo
- challenging myself mentally & physically

Research & Publications (Google Scholar)

What Can Be Predicted from 6 Seconds of Driver Glances?
Summary: Winner of the CHI 2017 Best Paper Award. We consider a dataset of real-world, on-road driving to explore the predictive power of driver glances.
Human-Centered Autonomous Vehicle Systems
Summary: We propose a set of shared autonomy principles for designing and building autonomous vehicle systems in a human-centered way, and demonstrate these principles on a full-scale semi-autonomous vehicle.
Summary: Traffic simulation and optimization with deep reinforcement learning. Primary goal is to make the hands-on study of deep RL accessible to thousands of students, educators, and researchers.
Crowdsourced Assessment of External Vehicle-to-Pedestrian Displays
Summary: 30 external vehicle-to-pedestrian display concepts for autonomous vehicles were evaluated. Simple, minimalist displays performed best.
Arguing Machines: Human Supervision of Black Box AI Systems
Summary: Framework for providing human supervision of a black box AI system that makes life-critical decisions. We demonstrate this approach on two applications: (1) image classification and (2) real-world data of AI-assisted steering in Tesla vehicles.
Cognitive Load Estimation in the Wild
Summary: Winner of the CHI 2018 Honorable Mention Award. We propose two novel vision-based methods for cognitive load estimation and evaluate them on a large-scale dataset collected under real-world driving conditions.
Driver Gaze Region Estimation without Use of Eye Movement
Summary: We propose a simplification of the general gaze estimation task by framing it as a gaze region estimation task in the driving context, thereby making it amenable to machine learning approaches. We go on to describe and evaluate one such learning-based approach.
MIT Advanced Vehicle Technology Study
Summary: Large-scale real-world AI-assisted driving data collection study to understand how human-AI interaction in driving can be safe and enjoyable. The emphasis is on computer vision based analysis of driver behavior in the context of automation use.
Active Authentication on Mobile Devices
Summary: An approach for verifying the identity of a smartphone user with with four biometric modalities. We evaluate the approach by collecting real-world behavioral biometrics data from smartphones of 200 subjects over a period of at least 30 days.
Automated Synchronization of Driving Data Using Vibration and Steering Events
Summary: A method for automated synchronization of vehicle sensors using accelerometer, telemetry, audio, and dense optical flow from three video sensors.