Multiple PhD (available in Fall 2022) and Postdoc Positions (available immediately)

Dr. Yingbin Liang 's lab will have multiple PhD positions (supported by research assistantship) starting in Fall 2022. Her lab also has postdoc positions available immediately. All positions are in the areas of Machine Learning, Deep Learning, and Stochastic Large-scale Optimization. Students and postdocs working with her will also be affiliated with the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE) and will have opportunities to collaborate with the faculty in the institute.

Short Biography

Dr. Yingbin Liang is currently a Professor at the Department of Electrical and Computer Engineering at the Ohio State University (OSU). She received the Ph.D. degree in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2005, and served on the faculty of University of Hawaii and Syracuse University before she joined OSU. Dr. Liang’s research interests include information theory, wireless communications, machine learning, optimization, and statistical signal processing. Dr. Liang received the National Science Foundation CAREER Award in 2009, and the State of Hawaii Governor Innovation Award in 2009. Her paper received EURASIP Best Paper Award in 2014. She served as an Associate Editor for the Shannon Theory of the IEEE Transactions on Information Theory during 2013-2015.

Research Interests

My research spans over multiple disciplines including machine learning, large-scale optimization, information theory, and statistical signal processing. My current research projects are briefly summarized as follows.

• Deep learning and generative adversarial networks (GANs): generalization performance, convergence of algorithms, statistical learning theory

• Meta-Learning: convergence analysis of various meta-learning algorithms and learning to optimize algorithms, impact of landscape properties on performance of MAML

• Reinforcement Learning: design of fast convergent temporal difference (TD)-learning, Q-learning, policy gradient, and imitation algorithms, non-asymptotic analysis of reinforcement learning algorithms.

• Large-scale Optimization: convergence of min-max optimization algorithms, zeroth and first-order stochastic algorithms, second-order algorithms that escape saddle points, variance reduction algorithms, accelerated algorithms, generalization error of stochastic algorithms, asynchronous parallel nonconvex optimization

• Information Theory and Communications: network information theory, state-dependent network information theory, information theoretic security, secret key generation, Poisson channels, MIMO wireless communications

Honors and Awards

• 2014 EURASIP Best Paper Award for the EURASIP Journal on Wireless Communications and Networking

• National Science Foundation CAREER Award, 2009

• State of Hawaii Governor Innovation Award, 2009

• M. E. Van Valkenburg Graduate Research Award, University of Illinois, 2005

• Vodafone-US Foundation Fellows Initiative Research Merit Award, 2005

• Vodafone-US Foundation Graduate Fellowship, 2003-2005