Meghan Huber is a postdoctoral research associate in the Department of Mechanical Engineering at the Massachusetts Institute of Technology and a member of the Newman Laboratory under the direction of Professor Neville Hogan.

Her research focuses on understanding human motor control, including how humans learn to control physical interactions and how humans learn from observing the actions of others. This basic research also serves to inform the development controllers for human-robot interaction and humanoid robots.

Her prior research focused on assessing and enhancing complex motor skill learning using virtual environments. She also developed multiple virtual rehabilitation systems for in-home use and worked on teams developing virtual training simulations for medical and military purposes.

Meghan received her B.S. degree in Biomedical Engineering from Rutgers University in 2009 and her M.S. degree in Biomedical Engineering from The University of Texas at Dallas in 2011. She recently received her Ph.D. in Bioengineering from Northeastern University in 2016 under the advisement of Dr. Dagmar Sternad. During her doctoral training, she was a Visiting Junior Scientist in the Autonomous Motion Department at the Max Planck Institute for Intelligent Systems in Tübingen, Germany from 2014-2015.

Recent News

Paper Accepted to ICRA 2019
Jongwoo Lee, Meghan Huber, Enrico Chiovetto, Martin Giese, Dagmar Sternad, and Neville Hogan will present "Human-inspired balance model to account for foot-beam interaction mechanics" at ICRA 2019 in Montreal, Canada.
ICRA 2019 Workshop Accepted
Pauline Maurice, Meghan Huber, Claudia Latella, Serena Ivaldi, and Neville Hogan are organizing a workshop at the 2019 International Conference of Robotics and Automation (ICRA) titled "Human Movement Science for Physical Human-Robot Collaboration". Find out more at hms2019icra.mit.edu!
Project Funded by Samsung
Neville Hogan, Meghan Huber, and Jongwoo Lee received funding from Samsung for a new research project "Novel Interventions and Assessment Measures for Robot-Aided Rehabilitation".