Inference and Information for Data Science Lab
I am an assistant professor at the School of Electrical Engineering at KAIST. I completed Ph.D. in Electrical Engineering and Computer Science (EECS) at MIT in 2014. From 2014 to 2017, I worked at University of Michigan as a research fellow. I received my M.S. from MIT, and my B.S. (with summa cum laude) from KAIST in Korea, all in the Department of EECS.
My research interests include data science, information theory, statistical inference, machine learning, and quantum information. I want to provide theoretical framework to data science by using tools from information theory, statistical inference and machine learning. I also aim to develop efficient algorithmic tools in extracting and exploiting information in statistical inference problems.
(Feb. 2020) A new paper on XOR queries for crowdsourced classification is available at arXiv.
- Daesung Kim and Hye Won Chung, Crowdsourced Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm [arXiv]
(Apr. 2019) Our work on weak detection of signal in the spiked Wigner model is accepted to ICML 2019.
- Hye Won Chung and Ji Oon Lee, Weak Detection of Signal in the Spiked Wigner Model [PMLR]
(Mar. 2019) Our work on structure design of neural network is accepted to ISIT 2019.
- Youngjae Min and Hye Won Chung, Shallow Neural Network can Perfectly Classify an Object following Separable Probability Distribution [arXiv]
- Hye Won Chung, Ji Oon Lee, Doyeon Kim and Alfred Hero, Parity Crowdsourcing for Cooperative Labeling [arXiv]