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Taehyeon Kim

 ML 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)
I'm looking for a research internship / visiting scholar position for 2023 ~. I can relocate for an on-site internship/visit for up to six months. If you are interested, feel free to contact me. My expected graduation is Feb. 2024.  Contact: potter32@kaist.ac.kr, kimtaehyeon610@gmail.com (permanant)

 About

Curriculum Vitae (CV)
My research has investigated trustworthy and real-world AI/ML challenges. My research has been presented at several conferences and journals. Specifically, my interests include:
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Federated learning
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Automated neural architecture search
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Learning with noisy labels
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Precipitation nowcasting
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Optimization for training deep neural networks
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Automated hyperparameter search
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Efficient deep learning
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Visual representation learning (e.g., Vision-Language Model, Video Representation Learning)

️ News

Jan. 2023
 Working with DynamoFL Research Team as Research Scientist
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)
May. 2022
 One paper accepted to IEEE Access Journal
Mar. 2022
 General Manager for the National Institute of Meteorological Sciences
Jan. 2022
 21/22 KAIST AI Workshop: Two Best Paper Awards
~ Dec. 2021
Previous News

Working at/with

DynamoFL

(YC W22)
U.S.A. (Remote)
Feb. 2023 - Apr. 2023

Research Scientist

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Research on real-world federated learning problems.

OpenMined

England (Remote)
Jun. 2022 - Present

Research Scientist

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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

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

Data Mining Lab

Daejeon, South Korea
Mar. 2017 - Feb. 2018

Undergraduate Research Internship

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

 Publications & Technical Reports

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Region-Conditioned Orthogonal 3D U-Net for Weather4Cast Competition
Competition
NeurIPS2022
Revisiting the Activation Function for Federated Image Classification
Workshop
NeurIPS2022
Layover Intermediate Layer for Multi-Label Classification in Efficient Transfer Learning
Workshop
NeurIPS2022
Revisiting Architecture-aware Knowledge Distillation: Smaller Models and Faster Search
Workshop
ICML2022
Supernet Training for Federated Image Classification under System Heterogeneity
Workshop
ICML2022
Revisiting Kernel Orthogonality Regularization: A Study for Convolutional Neural Networks in Image Classification
IEEE Access
FINE Samples for Learning with Noisy Labels
NeurIPS2021
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
IJCAI2021
Adaptive Local Bayesian Optimization Over Multiple Discrete Variables
Competition
NeurIPS2020
Efficient Model for Image Classification With Regularization Tricks
PMLR
Competition
NeurIPS2019
Orthogonal feature regularization : a novel approach for training robust models, Korea Advanced Institute of Science and Technology
Master Thesis
KAIST
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits.
Arxiv
Technical Report
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction
Arxiv
Mold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
Arxiv

Preprints (Under Review)

 Efficient Framework for Knowledge Distillation with Trust Region Aware Architecture Search
Taehyeon Kim, Heesoo Myeong, Se-Young Yun
 Label Noise-Resistant Federated Learning with Adaptive Regularization
Taehyeon Kim*, Donggyu Kim*, Se-Young Yun
 Federated Supernet Training: A Framework for Addressing Heterogeneity in Federated Learning
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

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