Education

• B.S., University of California Berkeley, 1990
• M.S.E, Stanford University, 1993
• Ph.D., Stanford University, 1998
• postdoc, Université de Paris, V, 1998
• postdoc, Oregon Health and Science University, 1999-2002

Teaching Interests

I am committed to developing and teaching highly interdisciplinary and integrative courses. I have developed and taught several courses aimed at bridging the gap between engineering and biology, such as BMED 8841 Computational Neuromechanics, as well as between engineering and clinical problems such as BMED 8843 Clinical Experience for Engineers and BMED 8813 Computational Neuromechanics. I'm also passionate about teaching about grantwriting and science communication, which has been incorporated into the BME PhD core courses.

Research Interests

Computational and experimental methods for understanding neuromechanical interactions underlying movement and understanding the neural basis of human balance and mobility. We focus on complex, whole body movements such as walking and balance in healthy aging and neurologically impaired individuals (stroke, Parkinson's disease), and skilled movements involved in dance and sport. We use techniques such as biomechanics, neural recordings of brain activity and muscle activity, robotic interactions, and simulations. We also model the biophysical bases of muscle and proprioceptive sensing to understand their role in movement.

Recent Publications

• Cognitive-Motor Interactions in Aging (EEG and Biomechanics)
  Payne, A.M., Palmer, J.A., McKay, J.L., Ting, L.H.* (2021) Lower cognitive set shifting ability is associated with stiffer balance recovery behavior and larger perturbation-evoked cortical responses in older adults. Frontiers in Aging Neuroscience, Dec 6;13:742243. doi: 10.3389/fnagi.2021.742243. PMCID:PMC8685437.
• Physiologically-inspired robotic exoskeletons for balance
  Beck, O.N., Shepherd, M.K., Rastogi, R. Martino, G., Ting, L.H., Sawicki, G.S. (2023) Exoskeletons need to react faster than physiological response to improve standing balance. Science Robotics, Feb 22;8(75):eadf1080. doi: 10.1126/scirobotics.adf1080. PMCID:PMC10169237.
• Biophysical muscle and sensory modeling for movement simulations
  Blum, K.P., Horslen, B., Campbell K.S., Nardelli P., Cope T.C., Ting L.H.* (2020) Diverse and complex muscle spindle firing properties emerge from multiscale muscle mechanics. Elife, Dec 28;9:e55177. doi: 10.7554/eLife.55177. PMCID:PMC7769569.
• Computational methods to identify individual differences in movement
  Winner, T.S., Rosenberg, M.C., Kesar, T.M., Ting, L.H., Berman, G.J. (2023) Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics. PLoS Computational Biology, Oct 27;19(10):e1011556. doi: 10.1371/journal.pcbi.1011556. PMCID:PMC10610102.
• Muscle synergies and modular control of movement
  Ting, L.H.*, Chiel, H.J., Trumbower, R.D., Allen, J.L., McKay, J.L., Hackney, M.E. Kesar, T.M. (2015) Neuromechanical principles underlying movement modularity and their implications for rehabilitation. Neuron 86(1):38-54. doi: 10.1016/j.neuron.2015.02.042. PMCID: PMC4392340