Youngeun Kim
Assistant Professor · School of Electrical Engineering · Korea University
Building efficient AI systems that are compact, adaptive, and practical to deploy—from multimodal and agentic AI to model compression and brain-inspired computing.
- Lab
- Efficient Machine Intelligence Lab
- Office
- Science Library, Room 426
Biography
Youngeun Kim is an Assistant Professor in the School of Electrical Engineering at Korea University and the Principal Investigator of the Efficient Machine Intelligence Lab. Before joining Korea University, Youngeun Kim was an Applied Scientist at Amazon AWS AI Labs, working on efficient multimodal LLM serving, and a Machine Learning Research Scientist at Meta Reality Labs, working on time-series neural networks for neuromotor-interface AR/VR applications.
Youngeun Kim received a Ph.D. in Electrical and Computer Engineering from Yale University in 2024, an M.S. from KAIST in 2020, and a B.S. from Sogang University in 2018. The lab's research spans efficient language and multimodal models, scalable agentic AI, model compression, continual adaptation, neuromorphic computing, and algorithm–hardware co-design.
Experience
Professional Experience
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Sep 2026 — Present
Assistant ProfessorSchool of Electrical Engineering, Korea University · Seoul, South Korea
Leading the Efficient Machine Intelligence Lab, with a focus on energy-efficient AI.
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Jul 2025 — Aug 2026
Applied ScientistAmazon AWS AI Labs · Bellevue, WA
Researched efficient scaling and operational optimization for multimodal large language models.
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Jun 2024 — Jul 2025
Machine Learning Research ScientistMeta Reality Labs · New York, NY
Developed time-series foundation models and neural interfaces for next-generation AR/VR.
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Jun — Aug 2023
Applied Scientist InternAmazon AWS AI Labs · Bellevue, WA
Developed continual-learning methods for large-scale foundation vision models.
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Jun — Aug 2021
Research InternSamsung Advanced Institute of Technology (SAIT) · Suwon, South Korea
Developed hardware-aware neural-network training algorithms for neuromorphic devices.
Education
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Sep 2020 — May 2024
Ph.D. in Electrical and Computer EngineeringYale University
Advisor: Prof. Priyadarshini Panda
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Mar 2018 — Feb 2020
M.S. in Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)
Advisor: Prof. Changick Kim
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Mar 2012 — Feb 2018
B.S. in Electrical EngineeringSogang University
Teaching
- SPECIAL TOPICS IN PROCESSOR ARCHITECTURE차세대컴퓨팅특론
- DATA STRUCTURE AND ALGORITHM데이터구조및알고리즘
Talks
- Neuromorphic Computing and AI ConsciousnessSentient AI and National Security Forum (SAIF)
- Designing Efficient yet Strong Video Understanding ModelsETRI
- AI Safety Needs On-Device AI: Efficient Deployment Under Real-World ConstraintsKAIST
- Towards Efficient AI ComputingPOSTECH
- Towards Efficient Deep Learning: Brain-Inspired Algorithm, Fine-Tuning, and CompressionYonsei University
- Efficient Machine Learning: From Algorithm to Hardware PerspectiveSungkyunkwan University
- Searching for Feedback Connection Architectures Using NAS in Spiking Neural NetworksCenter for Brain-Inspired Computing (C-BRIC, SRC)
- Towards Deep, Interpretable, and Robust Spiking Neural Networks: Algorithmic ApproachesCenter for Brain-Inspired Computing (C-BRIC, SRC)
Academic Service
- International Conference on Learning Representations (ICLR)Area Chair
- AAAI Conference on Artificial Intelligence (AAAI)Program Committee Member
- International Conference on Learning Representations (ICLR)
- International Conference on Machine Learning (ICML)
- Conference on Neural Information Processing Systems (NeurIPS)
- ACL Rolling Review (ARR)
- European Conference on Computer Vision (ECCV)
- International Conference on Computer Vision (ICCV)
- IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- British Machine Vision Conference (BMVC)
- Frontiers in Neuroscience
- IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
- IEEE Transactions on Artificial Intelligence (T-AI)
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS)