Taehyun Yoon

Machine Learning Ph.D. student at Georgia Institute of Technology

[cv] [linkedin] [github] [email]

Research Interests

My current research interest spans deep learning, combinatorial optimization, and autonomous systems. I am particularly interested in building safe, efficient, and robust intelligent systems.

Honors and Awards

• [Dec. 2023] I was nominated as an alternate candidate for a 2024 Fulbright U.S.-Korea Presidential STEM Initiative Award for graduate studies.
• [Mar. 2024] I have been upgraded to a principal candidate.


• [Dec. 2021] NeurIPS 2021 Machine Learning for Combinatorial Optimization (ML4CO) competition:
  I ranked first place on the student leaderboard, second place on the global leaderboard at the primal task track.
• Outperformed teams from Huawei, Alibaba, SAS, Megvii, Caltech, ETH Zurich, Peking, and Tsinghua University. [link]
• Reported by the mainstream media in Korea: [The Hankyoreh] [Segye] [Dongascience]

Education

• Ulsan National Institute of Science and Technology
• M.S. in Artificial Intelligence
  
• Thesis: Threshold-aware Learning to Generate Feasible Solutions of Mixed Integer Programs
• Department Valedictorian
• Ulsan National Institute of Science and Technology
• B.S. in Electrical Engineering and Computer Science (interdisciplinary double major)
  
• Magna Cum Laude

Selected Publications

• Design of high-performance entangling logic in silicon quantum dot systems with Bayesian optimization
  Ji-Hoon Kang^*, Taehyun Yoon^*, Chanhui Lee, Sungbin Lim, Hoon Ryu
  Scientific Reports (2024)
• A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone
  Aigerim Bogyrbayeva, Taehyun Yoon, Hanbum Ko, Sungbin Lim, Hyokun Yun, Changhyun Kwon
  Transportation Research Part C: Emerging Technologies (2023) [code]
• Confidence Threshold Neural Diving
  Taehyun Yoon
  NeurIPS 2021 Competition Workshop on Machine Learning for Combinatorial Optimization (2021) [poster]

Preprints

• Threshold-aware Learning to Generate Feasible Solutions for Mixed Integer Programs
  Taehyun Yoon, Jinwon Choi, Hyokun Yun, Sungbin Lim
  Submitted to Transactions on Machine Learning Research, under revision

Teaching

• Teaching Assistant, AI50201 Principles of Deep Learning, UNIST. Fall 2022.
  Instructor(s): Sungbin Lim
• Teaching Assistant, IE47001 Special Topics in IE I (Deep Generative Models and Multi-Armed Bandits), KAIST and UNIST. Fall 2021.
  Instructor(s): Ilchul Moon, Sungbin Lim
• Mentor, AI Tech Boot Camp, Naver Connect and Upstage AI. Winter 2020 and Fall 2021.