Jay H. Park (JaeHyeong)

Email: pjh0530z [at] gmail.com

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Jay H. Park
I am a software engineer in the Advanced Solution Development Team at Samsung Electronics. Before joining Samsung Electronics, I was a postdoctoral researcher in Artificial Intelligence Institute at Seoul National University working with Prof. Byung-Gon Chun (Software Platform Lab.). I received a Ph.D. in Computer Science and Engineering from the Ulsan National Institute of Science and Technology (UNIST) in August 2021, under the supervision of Prof. Sam H. Noh. I received B.S. and M.S. degrees in Computer Engineering from Hongik University.

EMPLOYMENT
- Staff Engineer, Advanced Solution Development Team at Samsung Electronics. (Jan. 2024 - present)
- Staff Engineer, Software Development Team at Samsung Electronics. (Mar. 2023 - Dec. 2023)
- Postdoctoral Researcher, Artificial Intelligence Institute at Seoul National University. (Sept. 2021 - Feb. 2023)
  (Advisor: Prof. Byung-Gon Chun, Software Platform Lab.)

EDUCATION
- Ph.D. in Computer Science and Engineering,
  Ulsan National Institute of Science and Technology  (UNIST),  Aug.  2021.
  (Advisor: Prof. Sam H. Noh, NECSST Lab.)
  Thesis: Efficient Distributed DNN Training through Resource-Aware Hybrid Parallelism.
- B.S., M.S. in Computer Engineering, Hongik University.
  (M.S. Advisor: Prof. Sam H. Noh)

RESEARCH INTERESTS
- AI  Systems
- Machine learning platforms
- Distributed deep learning systems
- Persistent memory

NEWS

• Sept. 2021: Jay H. Park was selected for the Postdoctoral Fellowship Program by the National Research Foundation of Korea (NRF).
  - "A Research on GPU Memory Management for Distributed Training of Large-Scale DNN Models", Date: Sept. 2021 ~ Aug. 2023 (Amount of Funding: KRW 120,000,000)

• Jan. 2021: Jay H. Park won the NAVER Ph.D. Fellowship Award
  - NAVER annually selects Ph.D. students who have shown remarkable research achievements in computer science and awards the NAVER Ph.D. Fellowship. 

• Jul. 2020: A paper is accepted USENIX ACT 2020
  - "HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism",  USENIX ATC 2020 (acceptance rate: 18.6%)
  Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh, and Young-ri Choi