Samet Oymak
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Samet Oymak Assistant Professor Electrical and Computer Engineering University of California, Riverside Office: Multidisciplinary Research Building (MRB1) 169 Aberdeen Dr, Riverside, CA 92507 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 in 2015.
My research lies at the confluence of machine learning, optimization, and statistics. I am broadly interested in finding principled solutions to contemporary
ML problems by using tools from optimization and statistics.
Key question: How to learn with provable guarantees by using minimal compute power, minimal human expertise, & imperfect data?
Current topics: learning with imperfect data, reinforcement learning, autoML & model compression, deep learning theory.
Prospective PhD students: Decent background on optimization and statistics is a major plus. Please send a CV and transcript.
Undergraduate juniors: Basic Python knowledge is necessary. Experience with PyTorch & TensorFlow is a plus.
POSTDOC OPENING: We are looking for a postdoc at the intersection of reinforcement learning, control and networks. Please drop an email to me or Fabio.
News
09/2021 3 papers are accepted to NeurIPS! Congrats to my students Mingchen, Xuechen, Ibrahim and all collaborators!
09/2021 Our new work extends confidence calibration to general performance metrics and new application settings.
08/2021 Our paper on Augmenting Geographically Weighted Regression is accepted to SIGSPATIAL as a full paper.
08/2021 Seminar on our work on architecture search at Stanford Statistics.
08/2021 Our note on super-convergence with large cyclical learning rates is accepted to IEEE Signal Processing Letters!
07/2021 Our MURI proposal on Understanding neuro-glial dynamics for robust non-Markovian learning is funded!
06/2021 Three papers will be presented at RL Theory and Overparameterization Workshops at ICML.
05/2021 I am giving seminars on our recent work at EPFL, Uppsala University, and University of Iowa.
05/2021 New preprint on Certainty Equivalent Quadratic Control for Markov Jump Systems.
04/2021 Our paper on Generalization Guarantees for Neural Architecture Search will appear at ICML 2021.
04/2021 Three papers are presented in TOPML workshop.
04/2021 Our paper on Unsupervised Domain Adaptation is now available (CVPR 2021 oral).
02/2021 New preprint on learning from imbalanced data with Chris’ group.
01/2021 Our paper on RL chatbots with Hristidis group received the Best Student Paper Award at ICSC 2021.
01/2021 Received the NSF CAREER award!
01/2021 Our paper on semisupervised learning theory is accepted to AISTATS 2021.
01/2021 Two paper accepted to ICASSP 2021.
12/2020 Our paper on Provable Benefits of Overparameterization in Model Compression is accepted to AAAI 2021! Congrats to Xiangyu, Yingcong and Chris!
11/2020 Our paper on Matrix Profile is accepted to Machine Learning for Systems Workshop at NeurIPS 2020.
09/2020 Our paper on multiclass learning (with Chris and Mahdi) is accepted to NeurIPS 2020!
08/2020 Received UCR's Regents Faculty Fellowship.
06/2020 New preprint on pruning neural networks with Mingchen, Yahya, Chris.
05/2020 Our system identification work with Yue and Maryam is accepted to L4DC as an oral presentation!
04/2020 Our paper on neural net optimization theory is accepted to IEEE Journal on Selected Areas in Info Theory.
03/2020 Paper accepted to ICDCS in collaboration with Jiasi and Srikanth.
02/2020 New paper on calibrating heterogenous datasets.
02/2020 New paper on nonlinear system identification.
01/2020 Our paper is accepted to Transactions on Signal Processing.
01/2020 NSF funded our proposal on Cyber-Physical System Safety!