I joined Apple after graduation and can be reached via personal email or LinkedIn message.

Education

  • University of California, San Diego
    Sept. 2019 - June 2023
    • Ph.D. in Electrical and Computer Engineering
    • Dissertation: Efficient Computing in Hyperdimensional Space
  • Korea University
    Mar. 2013 - Feb. 2019
    • Bachelor of Engineering in Electrical Engineering

Work/Research Experience

  • Machine Learning Acceleration Engineer at Apple Neural Engine Compiler Team (Video Engineering Group), Apple June 2023 - Current
  • Graduate Student Researcher at System Energy Effciency Lab, UC San Diego (Advisor: Prof. Tajana Simunic Rosing)
    Jan. 2020 - June 2023
    • GPU acceleration for hyperdimenisonal computing and bioinformatics
    • Fast and energy-efficient machine learning for low-power devices
  • Machine Learning Research Intern at Apple Neural Engine Compiler Team (Video Engineering Group), Apple
    June 2022 - Sept. 2022
    • Hardware-aware model compression
  • Co-op/Intern at Radeon Technologies Group, AMD
    June 2021 - Sept. 2021
    • ML framework; Efficient automated mixed precision training on GPU
  • Research Intern at System Architecture Research Group, SK Hynix
    June 2020 - Sept. 2020
    • Development & PyTorch integration of near-data processing architecture (CMS) for personalized recommendation system
  • Research Intern at Lab for Informatics, Communications, and Systems, Korea University (Advisor: Prof. Sang Hyun Lee)
    Jan. 2018 - Aug. 2019
    • Message Passing: Led project on lifetime maximization of wireless sensor networks using message-passing algorithm and graphical model approach to distributed optimization and decision
  • Data Engineer at Korea University
    May 2019 - Aug. 2019
    • Data Warehousing: Designed ETL pipeline and RESTful API in data warehouse for integrating and managing campus data

Publications

International Journal
  • [J9] J. Kang, W. Xu, W. Bittremieux, N. Moshiri, and T. Rosing, DRAM-Based Acceleration of Open Modification Search in Hyperdimensional Space, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2024.
  • [J8] J. Kang, W. Xu, W. Bittremieux, N. Moshiri, and T. Rosing, Accelerating Open Modification Spectral Library Searching on Tensor Core in High-dimensional Space, Bioinformatics, 2023.
  • [J7] W. Xu, J. Kang, W. Bittremieux, N. Moshiri, and T. Rosing, HyperSpec: Ultra-fast Mass Spectra Clustering in Hyperdimensional Space, Journal of Proteome Research, 2023.
  • [J6] R. K. Fielding-Miller, [and 43 others, including J. Kang], Wastewater and surface monitoring to detect COVID-19 in elementary school settings: The Safer at School Early Alert project, The Lancet Regional Health – Americas, 2023.
  • [J5] J. Kang, B. Khaleghi, T. Rosing, and Y. Kim, OpenHD: A GPU-Powered Framework for Hyperdimensional Computing, IEEE Transactions on Computers (TC), 2022.
  • [J4] G. Armstrong, C. Martino, J. Morris, B. Khaleghi, J. Kang, J. Dereus, Q. Zhu, D. Roush, D. McDonald, A. Gonzalez, J. P Shaffer, C. Carpenter, M. Estaki, S. Wandro, S. Eilert, A. Akel, J. Eno, K. Curewitz, A. D Swafford, N. Moshiri, T. Rosing, R. Knight, Swapping metagenomics preprocessing pipeline components offers speed and sensitivity increases, in American Society for Microbiology mSystems Journal, 2022.
  • [J3] S. Gupta, B. Khaleghi, S. Salamat, J. Morris, R. Ramkumar, J. Yu, A. Tiwari, J. Kang, M. Imani, B. Akansali and T. Rosing, Store-n-Learn: Classification and Clustering with Hyperdimensional Computing across Flash Hierarchy, ACM Transactions on Embedded Computing Systems (TECS), 2022.
  • [J2] S. Gupta, M. Imani, J. Sim, A. Huang, F. Wu, J. Kang, Y. Kim, and T. Rosing, COSMO: Computing with Stochastic Numbers in Memory, ACM Journal on Emerging Technologies in Computing Systems (JETC), 2022.
  • [J1] J. Kang, I. Sohn, and S. H. Lee, Enhanced Message-Passing Based LEACH Protocol for Wireless Sensor Networks, Sensors, 2018. [LINK]
International Conference
  • [C14] J. Kang, Y. H. Lee, M. Zhou, W. Xu, and T. Rosing, HygHD: Hyperdimensional Hypergraph Learning, In Design, Automation, and Test in Europe (DATE), 2024.
  • [C13] W. Xu, J. Kang, and T. Rosing, AttBind: Memory-efficient Acceleration for Long-range Attention using Vector-derived Symbolic Binding, In Design, Automation, and Test in Europe (DATE), 2024.
  • [C12] S. Pinge, W. Xu, J. Kang, T. Zhang, N. Moshiri, W. Bittremieux, and T. Rosing, SpecHD: Hyperdimensional Computing Framework for FPGA-based Mass Spectrometry Clustering, In Design, Automation, and Test in Europe (DATE), 2024.
  • [C11] W. Xu, J. Kang and T. Rosing, FSL-HD: Accelerating Few-Shot Learning on ReRAM using Hyperdimensional Computing, In Design, Automation and Test in Europe (DATE), 2023.
  • [C10] J. Kang, M. Zhou, A. Bhansali, W. Xu, A. Thomas and T. Rosing, RelHD: A Graph-based Learning on FeFET with Hyperdimensional Computing, In IEEE International Conference on Computer Design (ICCD), 2022.
  • [C9] J. Kang, W. Xu, W. Bittremieux, and T. Rosing, Massively Parallel Open Modification Spectral Library Searching with Hyperdimensional Computing, In International Conference on Parallel Architectures and Compilation Techniques (PACT), 2022.
  • [C8] R. Chandrasekaran, K. Ergun, J. Lee, D. Nanjunda, J. Kang, and T. Rosing, FHDnn: Communication Efficient and Robust Federated Learning for AIoT Networks, In Design Automation Conference (DAC), 2022.
  • [C7] W. Xu, J. Kang and T. Rosing, A Near-Storage Framework for Boosted Data Preprocessing of Mass Spectrum Clustering, In Design Automation Conference (DAC), 2022.
  • [C6] B. Khaleghi, U. Mallappa, D. Yaldiz, H. Yang, M. Shah, J. Kang and T. Rosing, PatterNet: Explore and Exploit Filter Patterns for Efficient Deep Neural Networks, In Design Automation Conference (DAC), 2022.
  • [C5] B. Khaleghi, J. Kang, H. Xu, J. Morris, and T. Rosing, GENERIC: Highly Efficient Learning Engine on Edge using Hyperdimensional Computing, In Design Automation Conference (DAC), 2022.
  • [C4] M. Zhou, W. Xu, J. Kang, and T. Rosing, TransPIM: A Memory-based Acceleration via Software-Hardware Co-Design for Transformer, In HPCA, 2022.
  • [C3] J. Kang, B. Khaleghi, Y. Kim, and T. Rosing, XCelHD: An Efficient GPU-Powered Hyperdimensional Computing with Parallelized Training, In ASP-DAC, 2022.
  • [C2] S. Salamat, J. Kang, Y. Kim, M. Imani, N. Moshiri, and T. Rosing, FPGA Acceleration of Protein Back-Translation and Alignment, In DATE, 2021.
  • [C1] Y. Guo, M. Imani, J. Kang, Y. Kim, S. Salamat, J. Morris, B. Aksanli and T. Rosing, HyperRec: Efficient Recommender Systems with Hyperdimensional Computing, In ASP-DAC, 2021.
Domestic Conference
  • [KC3] J. Kang, I. Sohn, and S. H. Lee, Data Compression-considered LEACH Protocol for Wireless Sensor Networks, JCCI, 2019.
  • [KC2] J. Kang, M. Kim, and S. H. Lee, Maximizing Wireless Sensor Network lifetime by message passing between nodes, Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, 2018. [LINK]
  • [KC1] J. Kang, S. Yoo, and H. Kim, LOS and NLOS classification of UWB signals using CapsNet, Proceedings of Symposium of the Korean Institute of Communications and Information Sciences, 2018. [LINK]
Technical Reports & Workshops
  • [W6] B. Khaleghi, J. Kang, H. Xu, J. Morris and T. Rosing, GENERIC: Highly Efficient Learning Engine on Edge using Hyperdimensional Computing, SRC TECHCON, Sept 2022.
  • [W5] J. Kang, C. Young, J. Morris, A. Akel, S. Eilert, J. Eno, K. Curewitz, N. Moshiri and T. Rosing, A GPU-Powered Phylogenetic Analysis for Large-scale Genomic Sequences, Society for Molecular Biology & Evolution, July 2021.
  • [W4] J. Kang, C. Young, J. Morris, A. Akel, S. Eilert, J. Eno, K. Curewitz, N. Moshiri and T. Rosing, A GPU-Powered Phylogenetic Analysis for Large-scale Genomic Sequences, 28th International Dynamics & Evolution of Human Viruses, May 2021.
  • [W3] S. Salamat, J. Kang, Y. Kim, M. Imani, N. Moshiri and T. Rosing, FPGA Acceleration of Protein Back-Translation and Alignment, ASHG Annual Meeting, Oct 2020.
  • [W2] J. Kang, Y. Kim, N. Moshiri and T. Rosing, Acceleration of protein-nucleotide sequence pairwise identification using GPGPU, ASHG Annual Meeting, Oct 2020.
  • [W1] J. Kang, Y. Kim and T. Rosing, HDCUDA: Framework for Brain-Inspired High-Dimensional Computing Accelerated with GPU, SRC TECHCON, Sept 2020.

Last Updated: 20 Jan 2024