Russ Tedrake
Electrical Engineering and Computer Science
Massachusetts Institute of Technology
About Professor Russ Tedrake
The Massachusetts Institute of Technology remains at the global forefront of technological innovation, particularly within the Department of Electrical Engineering and Computer Science (EECS). As a premier hub for interdisciplinary collaboration, MIT provides an unparalleled academic environment where rigorous theoretical frameworks meet groundbreaking practical application. Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering, exemplifies this tradition of excellence. As the Director of the Center for Robotics at the Computer Science and Artificial Intelligence Lab (CSAIL), he operates within an ecosystem defined by high-impact discovery and world-class facilities, cementing the department’s reputation as a top-tier destination for advanced engineering.
🧬Research Focus
Professor Tedrake’s research focuses on developing elegant control solutions for complex, underactuated, and stochastic dynamical systems. By bridging the gap between non-smooth mechanics and optimization theory, his work enables robust control design for sophisticated mechanical systems. His current efforts prioritize merging systems theory with machine learning to advance robotic manipulation and motion planning. These innovations are critical for creating robots capable of navigating unpredictable environments or performing delicate tasks. Through high-fidelity simulation and optimization-based control, Tedrake’s research addresses the fundamental challenges of nonlinear systems, driving breakthroughs that transition autonomous robotics from theoretical models to reliable, real-world applications.
🎓Student Fit & Career
Graduate research in Professor Tedrake’s group is ideal for PhD students with a strong foundation in mathematics, classical mechanics, and control theory. Successful candidates typically possess a drive for finding theoretical elegance in practical engineering challenges and an interest in the intersection of machine learning and robotics. Under his academic mentorship, students are encouraged to experiment with physical hardware, testing their algorithms on real-world platforms. This rigorous training prepares researchers for influential career paths in both academia and industry-leading laboratories. Graduates emerge as experts in optimization and dynamics, ready to lead the next generation of innovators in the robotics field.
Research Areas
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Interview Experiences (1)
Prepare a short whiteboard demo of a dynamics/control idea and walk through assumptions. He values clarity in modeling and an ability to reason about edge cases — show your math + intuition.
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