Laboratory for Information and Inference Systems – LIONS
We divide our research into two synergistic theory thrusts: information scalable optimization and data acquisition, and learning theory and methods for low-dimensional signal models. These research directions dovetail in order to develop a unified theory and practical toolset for adaptive representations, sampling and computational methods for high-dimensional data that feature structured geometric and combinatorial foundations.
EDEE PhD Thesis Distinction for Ya-Ping Hsieh
The EDEE Program Committee has rewarded Ya-Ping's doctoral dissertation entitled Convergence without Convexity: Sampling, Optimization, and Games with a Thesis Destinction.
PhD Defense of Ya-Ping Hsieh
On 30th September 2020, Ya-Ping Hsieh, a PhD student at LIONS lab, successfully defended his PhD thesis. The thesis, entitled "Convergence without convexity: sampling, optimization, and games" was supervised by Prof. Volkan Cevher. Due to Covid-19 the presentation was held remotely via Zoom.
LIONS @ NeurIPS 2020
Two LIONS papers have been accepted to thirty-fourth conference on Neural Information Processing Systems (NeurIPS). Due to Covid-19, this year NeurIPS edition will be a virtual-only conference held from Sunday, December 6th through Saturday, December 12th, 2020. The accepted LIONS papers are as follows: