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Taehyeon Kim

 DL Research Scientist
 Ph. D. Candidate @ KAIST AI [OSI Lab]
 Advised by Prof. Se-Young Yun
 Located in Seoul, South Korea
Education
 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: potter32@kaist.ac.kr, kimtaehyeon610@gmail.com (permanant)

 About

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

NYC, U.S.A.
Oct. 2023 - Dec. 2023

DynamoFL

(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

OpenMined

England (Remote)
Jun. 2022 - Jun. 2023

Research Scientist

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

Qualcomm

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

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Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection
NeurIPS2023
Navigating Data Heterogeneity in Federated Learning: A Semi-Supervised Approach for Object Detection
NeurIPS2023

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

 Details