Samet Oymak
 |
Samet Oymak Assistant Professor Electrical and Computer Engineering University of California, Riverside Office: Winston Chung Hall 322 Email: lastname (at) ece (dot) ucr (dot) edu |
I am an Assistant Professor of ECE at UC Riverside. I received my PhD degree in Electrical Engineering from Caltech.
Research: My research lies at the confluence of machine learning, optimization, and statistics. I am broadly interested in finding principled solutions to contemporary machine learning and data science problems by using tools from mathematical optimization and statistics. I also explore the fundamental algorithmic tradeoffs between speed, accuracy, scalability, and the data-efficiency.
Active research topics: (non)convex optimization, reinforcement learning, deep networks, and high-dimensional problems.
I am actively looking for prospective students who are interested in machine learning and data science.
A good background in applied math and statistics is a huge plus. Please reach out with a CV and transcript!
News
12/2018, new preprint with Mahdi: “Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path?”.
10/2018, two papers submitted to ICASSP 2019.
09/2018, new preprint “Stochastic gradient descent learns state equations with nonlinear activations”.
09/2018, gave an invited talk at University of Michigan.
07/2018, presented a paper in ICML 2018.
06/2018, gave an invited talk at Bilkent University.
06/2018, new preprint with Necmiye, “Non-asymptotic Identification of LTI Systems from a Single Trajectory”.
05/2018, new preprint with Mahdi: “End-to-end Learning of a Convolutional Neural Network via Deep Tensor Decomposition”.
05/2018, new preprint with Amir, Arindam, Kevin: “High Dimensional Data Enrichment: Interpretable, Fast, and Data-Efficient”.
05/2018, gave an invited talk at UCR Data Science Seminar.
02/2018, started UC Riverside!
02/2018, new preprint “Learning Compact Neural Networks with Regularization”.