Inference and Information for Data Science Lab

Hye Won Chung, Assistant Professor at School of Electrical Engineering, KAIST

Curriculum Vitae: [CV]

Email: hwchung@kaist.ac.kr

Office: N1 Building Room 206, KAIST

Phone: +82-42-350-7441

Short Bio:

I am an assistant professor at the School of Electrical Engineering at KAIST. I completed my 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.


News:

(Apr. 2022) Delivered an invited talk at "Post-Shannon Communication" panel session at JCCI 2022.

(Apr. 2022) A paper accepted in ISIT 2022.

  • A Generalized Worker-Task Specialization Model for Crowdsourcing: Optimal Limits and Algorithm [arXiv]

(Mar. 2022) An invited talk at Conference on Information Sciences and Systems (CISS) at Princeton

(Mar. 2022) Daesung has been awarded KAIST EE Best Research Achievement Award. Congrats Daesung!

(Mar. 2022) Two new papers are available at arXiv

  • A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits [arXiv]

  • Asymptotic Normality of Log-Likelihood Ratio and Fundamental Limit of the Weak Detection for Spiked Wigner Matrices [arXiv]

(Mar. 2022) Welcome new members, Youngmin Lee and Seunghun Cha

(Dec. 2021) I received KAIST Technology Innovation Award.

(Sep. 2021) Our work on Self-Diagnosing GAN is accepted to NeurIPS 2021.

  • Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks [arXiv]

(May 2021) Our work on detection of signal in the spiked rectangular models is accepted to ICML 2021.

  • Detection of Signal in the Spiked Rectangular Models [arXiv]

(May 2021) Our work on crowdsourced labelling is accepted to ISIT 2021.

  • Crowdsourced Labelling for Worker-Task Specialization Model [arXiv]

(Apr. 2021) Our work on binary classification with XOR queries is accepted to IEEE Transactions on Information Theory.

  • Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm [arXiv]

(Apr. 2021) A talk at ETRI-AI Academy (2nd) with the title "Data Optimization for AI."

(Mar. 2021) Supported by NRF Excellent Early-Career Research Funding for the next 4 years.

(Feb. 2021) A tutorial talk at KICS winter conference with the title "Data Optimization for AI."