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HomeUCLA Brain Research Institute (BRI)  > People  > Departments  > Electrical & Computer Engineering
Achuta Kadambi

Achuta Kadambi, Ph.D.

Achuta Kadambi received the PhD degree from MIT and joined UCLA as an Assistant Professor. His publications have been presented as Orals at CVPR, ICCV, ICCP and SIGGRAPH. He is currently co-authoring a textbook (“Computational Imaging”, MIT Press 2021) and is also a co-founder in a robotic imaging startup (http://akasha.im). Achuta’s research has received several recognitions including the NSF CAREER Award, Forbes 30 under 30 (Science), Google Faculty Award, Sony Imaging Young Faculty Award, and Army Young Investigator Award (ARO-YIP).

Jonathan Kao

Jonathan Kao, Ph.D.

Biography

Dr. Jonathan Kao is an Assistant Professor in the Department of Electrical Engineering. He received his Ph.D. in 2016 in Electrical Engineering from Stanford University working with Dr. Krishna Shenoy, where he developed algorithms for brain-machine interfaces. He continued his post-doctoral research in the same lab, using statistical and machine learning techniques to analyze the dynamics of neural populations. His lab is interested in computational techniques to elucidate computational mechanisms in neural populations as well as designing algorithms for neural prostheses.

Publications

A selected list of publications:

O’Shea Daniel J, Trautmann Eric, Chandrasekaran Chandramouli, Stavisky Sergey, Kao Jonathan C, Sahani Maneesh, Ryu Stephen, Deisseroth Karl, Shenoy Krishna V   The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces Experimental neurology, 2016; .
Kao Jonathan C, Nuyujukian Paul, Ryu Stephen I, Shenoy Krishna V   A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models IEEE transactions on bio-medical engineering, 2016; .
Kao Jonathan C, Ryu Stephen I, Shenoy Krishna V   Leveraging historical knowledge of neural dynamics to rescue decoder performance as neural channels are lost: “Decoder hysteresis” Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015; 2015: 1061-6.
Kao Jonathan C, Nuyujukian Paul, Ryu Stephen I, Churchland Mark M, Cunningham John P, Shenoy Krishna V   Single-trial dynamics of motor cortex and their applications to brain-machine interfaces Nature communications, 2015; 6: 7759.
Stavisky Sergey D, Kao Jonathan C, Nuyujukian Paul, Ryu Stephen I, Shenoy Krishna V   A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes Journal of neural engineering, 2015; 12(3): 036009.
Even-Chen Nir, Stavisky Sergey D, Kao Jonathan C, Ryu Stephen I, Shenoy Krishna V   Auto-deleting brain machine interface: Error detection using spiking neural activity in the motor cortex Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2015; 2015(3): 71-5.
Nuyujukian Paul, Fan Joline M, Kao Jonathan C, Ryu Stephen I, Shenoy Krishna V   A high-performance keyboard neural prosthesis enabled by task optimization IEEE transactions on bio-medical engineering, 2015; 62(1): 21-9.
Nuyujukian Paul, Kao Jonathan C, Fan Joline M, Stavisky Sergey D, Ryu Stephen I, Shenoy Krishna V   Performance sustaining intracortical neural prostheses Journal of neural engineering, 2014; 11(6): 066003.
Stavisky Sergey D, Kao Jonathan C, Nuyujukian Paul, Ryu Stephen I, Shenoy Krishna V   Hybrid decoding of both spikes and low-frequency local field potentials for brain-machine interfaces Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2014; 2014(1): 3041-4.
Fan Joline M, Nuyujukian Paul, Kao Jonathan C, Chestek Cynthia A, Ryu Stephen I, Shenoy Krishna V   Intention estimation in brain-machine interfaces Journal of neural engineering, 2014; 11(1): 016004.
Kao Jonathan C, Nuyujukian Paul, Stavisky Sergey, Ryu Stephen I, Ganguli Surya, Shenoy Krishna V   Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness Conference proceedings : … Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2013; 2013(1): 293-8.
Gilja Vikash, Nuyujukian Paul, Chestek Cindy A, Cunningham John P, Yu Byron M, Fan Joline M, Churchland Mark M, Kaufman Matthew T, Kao Jonathan C, Ryu Stephen I, Shenoy Krishna V   A high-performance neural prosthesis enabled by control algorithm design Nature neuroscience, 2012; 15(12): 1752-7.
Sussillo David, Nuyujukian Paul, Fan Joline M, Kao Jonathan C, Stavisky Sergey D, Ryu Stephen, Shenoy Krishna   A recurrent neural network for closed-loop intracortical brain-machine interface decoders Journal of neural engineering, 2012; 9(2): 026027.
Dejan Markovic, Ph.D.

Dejan Markovic, Ph.D.

Biography

Dejan Markovic received the Dipl.Ing. degree from the University of Belgrade, Serbia, in 1998 and the M.S. and Ph.D. (thesis) degrees from the University of California, Berkeley, in 2000 and 2006, respectively, all in Electrical Engineering. In 2006, he joined the faculty of the Electrical Engineering Department at the University of California, Los Angeles as an Assistant Professor. His current research is focused on digital integrated circuits and architectures for parallel data processing in wireless communications and neuroscience, including optimization methods and supporting CAD flows. Dr. Marković was awarded the CalVIEW Fellow Award in 2001 and 2002 for excellence in teaching and mentoring of industry engineers through the UC Berkeley distance learning program. In 2004, he was a co-recipient of the Best Paper Award at the IEEE International Symposium on Quality Electronic Design. He received 2007 David J. Sakrison Memorial Prize from the Department of EECS, UC Berkeley.