About Jonathan
Jonathan is an Associate Professor in Electrical and Computer Engineering at UCLA, where he directs the Neural Engineering and Computation Lab and teaches Signals & Systems, Neural Signal Processing, and Neural Networks & Deep Learning. He received his BS, MS, and PhD in Electrical Engineering from Stanford University. His PhD and postdoc were under the supervision of Professor Krishna Shenoy. He is the recipient of the NIH Director's New Innovator Award, a NSF CAREER Award, a Brain & Behavior Research Foundation Young Investigator Grant, and a Hellman Fellowship.
Honors & Awards
2024 Northrop Grumman Excellence in Teaching Award | Article
2020 NIH Director's New Innovator Award | Article
2020 NSF CAREER Award
2020 Brain & Behavior Research Foundation Young Investigator (Research Partners Program, P&S Fund Investigator)
2019 Hellman Fellow
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)
Timeline
2023 - present
Associate Professor, University of California, Los Angeles
Electrical and Computer Engineering
Neurosciences Interdepartmental Program
2017 - 2023
Assistant Professor, University of California, Los Angeles
2017 - present
Director, Neural Engineering and Computation Laboratory
2016 - 2017
Postdoctoral Fellow, Stanford University
Neural Prosthetics Systems Laboratory
Advisor: Professor Krishna V. Shenoy
2010 - 2016
PhD, Electrical Engineering, Stanford University
NSF Graduate Research Fellowship Program
Neural Prosthetics Systems Laboratory
Advisor: Professor Krishna V. Shenoy
Dissertation: Decoder algorithm design for high-performance and robust neural prostheses
2009 - 2010
MS, Electrical Engineering, Stanford University
2006 - 2010
BS with distinction, Electrical Engineering, Stanford University
Frederick E. Terman Award for Scholastic Achievement in Engineering
Teaching
At UCLA, Jonathan has taught:
Signals & Systems (ECE 102) Fall quarters: 2018 - present
Elements of differential equations, first- and second-order equations, variation of parameters method and method of undetermined coefficients, existence and uniqueness. Systems: input/output description, linearity, time-invariance, and causality. Impulse response functions, superposition and convolution integrals. Laplace transforms and system functions. Fourier series and transforms. Frequency responses, responses of systems to periodic signals. Sampling theorem.
Undergraduate Advanced Honors Seminar (ECE 189) Fall quarters: 2018 - present
Neural Networks and Deep Learning (ECE C147/C247; formerly ECE 239AS) Winter quarters: 2018 - present
Review of machine learning concepts; maximum likelihood; supervised classification; neural network architectures; backpropagation; regularization for training neural networks; optimization for training neural networks; convolutional neural networks; practical CNN architectures; deep learning libraries in Python; recurrent neural networks, backpropagation through time, long short-term memory and gated recurrent units; variational autoencoders; generative adversarial networks; adversarial examples and training.
Neural Networks and Deep Learning 2 (ECE 239AS) Winter quarters: 2024 - present
Study covers advanced topics in deep learning including deep generative modeling, transformers, and deep reinforcement learning. Designed for students who have completed course C147 or C247.
Neural Signal Processing & Machine Learning (ECE C143A/C243A; formerly ECE 239AS) Spring quarters: 2017 - 2023
Fundamental properties of electrical activity in neurons; technology for measuring neural activity; spiking statistics and Poisson processes; generative models and classification; regression and Kalman filtering; principal components analysis, factor analysis, and expectation maximization.