Joon Kim

I am a sophomore at UC Berkeley majoring in EECS. My interest is broadly in the union of CS theory, mathematics, and AI. Currently, I am independently studying various topics in mathematics and TCS to start getting involved in related research. Last semester, I worked as an undergraduate researcher in the C.H.E.N. Lab, part of BAIR and CPH, led by Professor Irene Chen. There, I investigated the effect of zero-shot LLM inference on self-supervised learning alongside Ph.D. candidate Jichan Chung. Previously, I worked as a research intern at JLK Inc., researching federated learning models applied to MRI image segmentation. During my military service, I was fortunate to be advised by Professor Sejin Park to research defensive techniques against Deep Leakage in federated learning. In my free time, I usually practice playing the acoustic guitar, watch Netflix or Youtube, or pick up a random CS/Math textbook and hope I understand more than one sentence.

news

Dec 05, 2024	Participating in Advent of Code 2024. Repository can be found here.
Nov 25, 2024	Launching this personal homepage! Hooray!

latest posts

Dec 15, 2024	Advent of Code 2024 Week 2 Reflection
Dec 06, 2024	Advent of Code 2024 Week 1 Reflection
Nov 25, 2024	Hello!

selected publications

medRxiv

In-Silo Federated Learning vs. Centralized Learning for Segmenting Acute and Chronic Ischemic Brain Lesions

Joon Kim, Hoyeon Lee, Jonghyeok Park, and 6 more authors

Preprint, 2024

DOI Bib

@article{Kim2024.05.24.24307154,
  author = {Kim, Joon and Lee, Hoyeon and Park, Jonghyeok and Park, Sang Hyun and Lee, Myungjae and Sunwoo, Leonard and Kim, Chi Kyung and Kim, Beom Joon and Ryu, Wi-Sun},
  title = {In-Silo Federated Learning vs. Centralized Learning for Segmenting Acute and Chronic Ischemic Brain Lesions},
  elocation-id = {2024.05.24.24307154},
  year = {2024},
  doi = {10.1101/2024.05.24.24307154},
  url = {https://www.medrxiv.org/content/early/2024/05/26/2024.05.24.24307154},
  eprint = {https://www.medrxiv.org/content/early/2024/05/26/2024.05.24.24307154.full.pdf},
  journal = {Preprint},
}

arxiv

Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning

Joon Kim, and Sejin Park

Preprint, 2024

DOI Bib

@article{kim2024randomgradientmaskingdefensive,
  title = {Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning},
  author = {Kim, Joon and Park, Sejin},
  year = {2024},
  eprint = {2408.08430},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},
  url = {https://arxiv.org/abs/2408.08430},
  doi = {10.48550/arXiv.2408.08430},
  journal = {Preprint},
}