Taehyeon Kim

 DL Research Scientist
 Ph. D. Candidate @ KAIST AI [OSI Lab]
 Advised by Prof. Se-Young Yun
 Located in Seoul, South Korea
 M.S. in Data Science (KAIST)
 B.S. in Mathematical Science (KAIST)
 Minor in Intellectual Property (KAIST)
My expected graduation date is Aug. 2024.  Contact:, (permanant)


My goal is to tackle trustworthy and real-world AI/ML challenges. Specifically, my interests include:
Knowledge Distillation & Learning with Noisy Labels
Federated Learning & AutoML & Semi-Supervised Learning
Optimization for training deep neural networks (Efficient Deep Learning)
Language Model & Instruction Tuning
Game-Changing Research

️ News

Oct. 2023
May. 2023
Jan. 2023
 Working with Google Research NYC
 Invited to Liner Dev conference
 Working with DynamoFL Research Team
Dec. 2022
 4th Award in NeurIPS2022 Competition-Weather4Cast [Link]
 Two papers accepted to NeurIPS2022 Workshop
 Invited talk at HyperConnect Research Team
 Invited talk at KSC Conference
 Invited to Twelve Labs Networking
Oct. 2022
 Qualcomm Innovation Fellowship Winner in 2022 [Link]
Jul. 2022
 Two papers accepted to ICML2022 Workshop (including an oral presentation)

Working at/with

Google Research

Oct. 2023 - Dec. 2023


(YC W22)
U.S.A. (Remote)
Jan. 2023 - May. 2023

Research Intern

Research on Language Models.

Research Intern

Research on real-world federated learning problems.
Semi-supervised federated learning


England (Remote)
Jun. 2022 - Jun. 2023

Research Scientist

Social-focused AI research: privacy, fairness, accountability, and transparency.


Seoul, South Korea
Jun. 2021 - Dec. 2021

CV & ML Ph.D. Internship for Autonomous Driving report

Designing a resource-efficient and accurate backbone for ADAS
1 Paper and 1 US Patent
Knowledge Distillation, Neural Architecture Search

Data Mining Lab

Daejeon, South Korea
Mar. 2017 - Feb. 2018

Undergraduate Research Internship

Data Science, KAIST (Advisor: Jae-Gil Lee)
Trajectory prediction based on user card data

 Publications & Technical Reports

Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection

Preprints (Under Review)

 Tackling Heterogeneous Label Noise in Federated Learning with Label-Mixture Regularization
Taehyeon Kim*, Donggyu Kim*, Se-Young Yun
 Overcoming Heterogeneity in Federated Learning with Supernet Training and Parametric Normalization
Taehyeon Kim, Se-Young Yun
 A Survey of Supernet Optimization and its Applications: Spatial and Temporal Optimization for Neural Architecture Search
Stephen Cha, Taeheyeon Kim, Hayeon Lee, Se-Young Yun
 Task Adaptive Distillation in Cross Domain Few Shot Learning
Yongjin Yang, Taehyeon Kim, Se-Young Yun