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Han-Byul Kim I'm a Ph.D candidate at Computer Science & Engineering, Seoul National University under the supervision of professor Sungjoo Yoo. I'm passionate about making machine learning faster and more efficient. I focus on optimizing all aspects of neural networks, from algorithms and architectures to system design and hardware implementation. My research explores techniques like quantization and neural architecture search to achieve this. Additionally, I work on projects for deploying optimized neural networks on FPGAs using RTL design. |
Experience |
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Research Intern, Apple Apr. 2024 - Now, Seattle, WA, United States Full time PhD intern in MIND (Machine Intelligence & Neural Design) team, DMLI |
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Student Researcher, Google Oct. 2022 - Dec. 2023, Seoul, Korea Full time PhD intern in Model optimization team, CoreML |
Research |
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MetaMix: Meta-state Precision Searcher for Mixed-precision Activation Quantization
Han-Byul Kim, Joo Hyung Lee, Sungjoo Yoo, Hong-Seok Kim AAAI Conference on Artificial Intelligence (AAAI), 2024 #Quantization, #Neural_Architecture_Search, #Mixed_Precision |
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JaxPruner: A concise library for sparsity research
Joo Hyung Lee, Wonpyo Park, Nicole Mitchell, Jonathan Pilault, Johan Obando-Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart Bik, Woohyun Han, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci Conference on Parsimony and Learning (CPAL), 2024#Sparsity, #Quantization, #Jax |
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BASQ: Branch-wise Activation-clipping Search
Quantization for Sub-4-bit Neural Networks
Han-Byul Kim, Eunhyeok Park, Sungjoo Yoo European Conference on Computer Vision (ECCV), 2022#Quantization, #Neural_Architecture_Search, #2-bit, #3-bit, #4-bit |
For more details, please visit my LinkedIn |
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Webpage source: https://github.com/jonbarron/jonbarron_website. |