Contact Information:

UIC Department of Mechanical & Industrial Engineering (M/C 251), 2039 ERF, 842 W. Taylor Street, Chicago, Illinois 60607

Office: 2023 ERF

Lab Location: 2060 ERF

Lab Website: The Motor Behavior Laboratory

Ph.D. Mechanical Engineering
Massachusetts Institute of Technology, 2005

M.S. Mechanical Engineering
Massachusetts Institute of Technology, 2000

B.S. Mechanical Engineering
University California San Diego, 1998

Biological motor control and learning

Optimal control

Stochastic optimal control

Computational neuroscience


Machine learning

Bayesian statistics

Statistical inference

Society for Neuroscience, Member

The Neural Control of Movement Society, Member

National Science Foundation, CMMI, Co-PI: “Risk, Variability and Decision-Making in Whole-Body Movements,” 2012-2015

NIH T32 postdoctoral fellowship, 2007-2009

  1. A Probabilistic Analysis of Muscle Force Uncertainty for Control,” Berniker, M., Jarc, A., Kording, K. and Tresch, M., IEEE Transactions on Biomedical Engineering.
  2. Deep networks for motor control functions,” M. Berniker, and K. P. Kording.  Frontiers in Computational Neuroscience 9 (2015): 32.
  3. Using psychophysics to ask if the brain samples or maximizes,” D.E. Acuna, M. Berniker, H.L. Fernandes, & K.P. Kording, Journal of vision 15.3 (2015): 7.
  4. The effects of training breadth on motor generalization,” M. Berniker, H. Mizraei Buini, & K. Kording, J Neurophysiol 00615.2013.
  5. Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of mixed coordinate representations and local learning,” M. Berniker, D. Franklin, R. Flanagan, D. Wolpert & K. Kording, J Neurophysiol 111:1165-1182.
  6. An examination of the generalizability of motor costs,” M. Berniker, M. O’Brien, K. Kording & A. Ahmed, PLoS ONE; 8(1): e53759.
  7. FES control of isometric forces in the rat hindlimb using many muscles,” Jarc, A., Berniker, M., and Tresch, M., IEEE Transactions on Biomedical Engineering.
  8. Estimating the relevance of world disturbances to explain savings, interference and long-term motor adaptation effects,” Berniker, M. and Kording, K., PLoS Comput Biol; 7(10): e1002210.
  9. Discrete-time local dynamic programming,” Berniker, M. and Kording, K., in American Control Conference, 618-25.
  10. Learning priors for Bayesian computations in the nervous system,” Berniker, M., Voss, M. and Kording, K., PLoS ONE; 5(9): e12686.
  11. Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics,” Berniker, M., Jarc, A., Bizzi, E., and Tresch, M., Proceedings of the National Academy of Sciences; 106(18): 7601-06.
  12. Estimating the sources of motor errors for adaptation and generalization,” Berniker, M. and Kording, K., Nature Neuroscience; 11(12): 1454-61.
  13. Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits,” Au, S., Berniker, M., and Herr, H., Neural Networks; 21(4): 654-66.

Book Chapters:

  1. Bayesian sensorimotor control,” in Scholarpedia, sensorimotor control, under revision.
  2. Bayesian Approaches to Sensory Integration for Motor Control,” in Wiley Interdisciplinary Reviews: Cognitive Science, 2: 419-28.
  3. Bayesian approaches to modeling action selection,” in Modeling natural action selection, A. K. Seth, Ed. Cambridge: Cambridge University Press.
  4. Bayesian Models of Motor Control,” In: Encyclopedia of Neuroscience, L.R. Squire Ed. vol. 2, Oxford: Academic Press.