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JUNGTAEK KIM Postdoctoral Associate jungtaek.kim [at] pitt [dot] edu Google Scholar · GitHub · Twitter · LinkedIn · Curriculum Vitae |
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NEWS
- [May 2023] My new paper "Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning" is available at arXiv now.
- [March 2023] I will serve as a reviewer for NeurIPS-2023.
- [March 2023] I will give a talk at the minisymposium "Sequential Decision Making for Optimization, Learning and Search", which will be held as part of the 10th International Congress on Industrial and Applied Mathematics (ICIAM-2023).
- [January 2023] I will serve as a reviewer for ICML-2023.
- [January 2023] Happy new year!
- [December 2022] I will serve as a program committee member for UAI-2023.
- [November 2022] I will serve as a reviewer for CVPR-2023.
- [October 2022] My new paper "Sequential Brick Assembly with Efficient Constraint Satisfaction" (with Seokjun Ahn, Prof. Minsu Cho, and Prof. Jaesik Park) is available at arXiv now.
- [August 2022] I will serve as a reviewer for AISTATS-2023.
- [July 2022] I will serve as a reviewer for ICLR-2023.
- [July 2022] I will serve as a program committee member for AAAI-2023.
- [July 2022] I joined the University of Pittsburgh as a postdoctoral associate, and I will work with Prof. Paul W. Leu, Prof. Satish Iyengar, Prof. Lucas Mentch, and Prof. Oliver Hinder.
- [June 2022] The up-to-date version of my work "Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytopes" is available at arXiv now.
- [June 2022] My recent work "Learning to Assemble Geometric Shapes" is available at arXiv now.
- [May 2022] My work "Combinatorial Bayesian Optimization with Random Mapping Functions to Convex Polytopes" (with Seungjin Choi and Minsu Cho) has been accepted at UAI-2022.
ABOUT ME
Jungtaek Kim is a postdoctoral associate at the University of Pittsburgh, working as a member of the Center for Materials Data Science for Reliability and Degradation. At the University of Pittsburgh, he works with Prof. Paul W. Leu, Prof. Satish Iyengar, Prof. Lucas Mentch, and Prof. Oliver Hinder. Before that, he was a postdoctoral researcher at POSTECH, in the group of Prof. Minsu Cho. He received B.S. in Mechanical Engineering and Computer Science and Engineering from POSTECH in 2015, and Ph.D. in Computer Science and Engineering from POSTECH in 2022, under the supervision of Prof. Seungjin Choi and Prof. Minsu Cho. He interned at Vector Institute and SigOpt (acquired by Intel), during his Ph.D. program. His internship at Vector Institute was carried out under the supervision of Prof. Graham W. Taylor and he worked with Dr. Michael McCourt at SigOpt. He presented his work as the first author or a co-author, at the top-tier machine learning conferences such as NeurIPS, AISTATS, UAI, ICML, and ICLR, and moreover he served as a program committee member or a reviewer for diverse machine learning conferences such as NeurIPS, ICML, AISTATS, UAI, and ICLR. His main research interests are statistical machine learning, Bayesian optimization, and sequential assembly.
CURRENT RESEARCH INTERESTS
- Statistical Machine Learning
- Bayesian Optimization
- Hyperparameter Optimization
- Automation of Machine Learning
- Sequential Assembly
WORK-IN-PROGRESS PAPERS
- Jungtaek Kim. Density Ratio Estimation-based Bayesian Optimization with Semi-Supervised Learning.
- Seokjun Ahn, Jungtaek Kim, Minsu Cho, and Jaesik Park. Sequential Brick Assembly with Efficient Constraint Satisfaction.
ELSEWHERE
- Academic Profile: Google Scholar, DBLP, Semantic Scholar, ORCID
- Social Network: LinkedIn, Twitter
- Development: GitHub
Last updated: May 26, 2023