Jonathan Kao

Jonathan Kao

Jonathan Kao
Assistant Professor
Primary Area: Signals and Systems

Office: 56-147H Engr. IV
Phone: (310) 983-3068
Personal Web:


Research and Teaching Interests:

Neural prostheses, brain-machine interfaces, computational and systems neuroscience, neural signal processing, machine learning, dynamical systems, recurrent neural networks, deep learning

Awards and Recognitions

2020 NIH Director’s New Innovator Award
2020 Brain & Behavior Research Foundation Young Investigator Award
2020 NSF Career Award
2019 UCLA Hellman Fellows Award
2016 Sammy Kuo Award in Neuroscience, Finalist, Stanford University
2014 BRAIN Best Paper Award, IEEE EMBS, Chicago (Stavisky SD, Kao JC, Nuyujukian P, Ryu SI, Shenoy KV)
2006 Hewlett Packard Best Paper Award, ASME IMECE, Chicago (Kao JC, Warren J, Xu J, Attinger D)
Selected Publications
  • Kao JC*, Nuyujukian P*, Ryu SI, Shenoy KV (2017) A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Transactions on Biomedical Engineering. 64(4):935–945.  (Link:
  • Stavisky SD, Kao JC, Ryu SI, Shenoy KV (2017) Trial-by-trial motor cortical correlates of a rapidly adapting visuomotor internal model. The Journal of Neuroscience. 37(7):1721–1732. (Link:
  • Nuyujukian P, Kao JC, Ryu SI, Shenoy KV (2017) A nonhuman primate brain-computer typing interface. Proceedings of the IEEE. 105(1):66–72. (Link:
  • O Shea DJ, Trautmann E, Chandrasekaran C, Stavisky SD, Kao JC, Sahani M, Ryu SI, Deisseroth K, Shenoy KV (2017) The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces. Experimental Neurology. 287:437–451. (Link:
  • Sussillo D*, Stavisky SD*, Kao JC*, Ryu SI, Shenoy KV (2016) Making brain-machine interfaces robust to future neural variability. Nature Communications. 7:13749.  (Link:
  • Kao JC, Nuyujukian P, Ryu SI, Churchland MM, Cunningham JP, Shenoy KV (2015) Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nature Communications. 6(May):1–12. (Link:
  • Nuyujukian P, Kao JC., Fan JM., Stavisky SD., Ryu SI., Shenoy KV. (2014) Performance sustaining intracortical neural prostheses. Journal of Neural Engineering. 11(6):066003. (Link:
  • Kao JC, Stavisky SD, Sussillo David, Nuyujukian P, Shenoy KV (2014) Information systems opportunities in brain-machine interface decoders. Proceedings of the IEEE. 102(5):666–682. (Link:
  • Fan JM, Nuyujukian P, Kao JC, Chestek CA, Ryu SI, Shenoy KV (2014) Intention estimation in brain machine interfaces. Journal of Neuroengineering. 11(1):016004. (Link:
  • Gilja V*, Nuyujukian P*, Chestek CA, Cunningham JP, Yu BM, Fan JM, Churchland MM, Kaufman MT, Kao JC, Ryu SI, Shenoy KV (2012) A high-performance neural prosthesis enabled by control algorithm design. Nature Neuroscience. 15(12):1752–7.  (Link:
  • Sussillo D, Nuyujukian P, Fan JM, Kao JC, Stavisky SD, Ryu SI, Shenoy KV (2012) A recurrent neural network for closed-loop intracortical brain-machine interface decoders. Journal of Neural Engineering. 9(2):026027. (Link: