Justin Kang

Welcome to my website. You can find my Publications, Projects and CV above. Below, you can find some information about me.

  • I am a Graduate Student at the University of California, Berkeley, supervised by Prof. Kannan Ramchandran.
  • Previously, I completed my Master’s of Applied Science at The University of Toronto supervised by Prof. Wei Yu.
  • My research interests include machine learning, privacy in learning, and data valuation. Recently, I’ve been applying some ideas from valuation/importance scoring to explainable AI for LLMs (see paper 1 below). I have a strong background in signal processing and information theory, and like to view these modern learning problems through that lens, which often leads to unique and exciting solutions.
  • In 2019 I received my B.A.Sc. from the University of British Columbia in Engineering Physics.

Recent News

Recent Papers

  1. Learning to Understand: Identifying Interactions via the Mobius Transform. Kang, J., Erginbas, Y.E., Butler, L., Pedarsani, R., Ramchandran, K. (2024), Submitted. (paper, video)

  2. Learning a 1-Layer Conditional Generative Model in Total Variation. Ajil Jalal, Justin Singh Kang, Ananya Uppal, Kannan Ramchandran, Eric Price. NeurIPS 2023. (paper, video, poster, code)

  3. The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning. Justin Singh Kang, Ramtin Pedarsani and Kannan Ramchandran. arxiv.2301.13336, NeurIPS Workshops - FL@FM Dec. 2023, TMLR (to appear). (paper, video)

  4. Efficiently Computing Sparse Fourier Transforms of q-ary Functions. Justin Singh Kang, Y. E. Erginbas, A. Aghazadeh and K. Ramchandran. IEEE ISIT 2023 (paper, video, code)