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Jeonghwan 'Jay' Lee

About me

I am a researcher and engineer inspired by my own mobility challenges to develop technologies that help people with neurological injuries regain independence and live fuller lives.

  • Currently a Postdoctoral Researcher in Brain and Cognitive Sciences at MIT, working with Dr. Nidhi Seethapathi.
  • Ph.D. in Mechanical Engineering, University of Texas at Austin, 2022, under the mentorship of Dr. James Sulzer, with expertise in Biomechanics and Rehabilitation Engineering with wearable devices.
  • Over 5 years of human subject research experience, specializing in experimental design, statistical analysis, musculoskeletal simulation, and time-series biomechanical signal processing (kinematics, kinetics, IMUs and EMG).
  • Additional 3 years of industrial experience leading Robot Perception software development, with a focus on multi-modal sensor data acquisition, computer vision, and cloud-based AI pipelines.
  • Demonstrated track record of delivering innovative solutions in fast-paced start-up and scale-up environments.

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At MIT, my ongoing research is bridging neuromechanics principles with modern Generative AI to advance understanding of human embodiments, musculoskeletal simulation for biomechanics, rehabilitation, and robotics research. This project is funded by MIT Generative AI Impact Consortium.

My doctoral research projects cover post-stroke gait rehabilitation, musculoskeletal simulation, and affordable robotics devices.

  • Data-driven characterization of post-stroke Stiff-Knee Gait [Paper]
  • Biomechanical variable selection for quadriceps hyperreflexia prediction to optimize exoskeletal assistance [Paper]
  • Kinematic synthesis to design an affordable 1-DOF motorized gait trainer [Paper]

Before joining MIT, I worked as a Senior Robotic Engineer at Contoro Robotics, where I led the development of robot perception system, enabling warehouse container unloading robots [video1 , video2 , video3] to operate effectively in real-world environments. My work included integrating 2D/3D sensors and designing a comprehensive computer vision and machine learning pipeline, supported by a cloud-based, human-in-the-loop segmentation and online data re-labeling workflow.

Peer-reviewed publications (7 out of 12)

Selected works from my Ph.D. center on data-driven human movement biomechanics, gait rehabilitation, and wearable exoskeleton design, which are integral to developing personalized, effective solutions for post-stroke rehabilitation and assistive robotics.

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