Kerr Ding

kerrding at gatech.edu

I am a third-year CS Ph.D. student in the School of Computational Science and Engineering, Georgia Institute of Technology, where I am fortunate to be advised by Prof. Yunan Luo. Before this, I obtained my bachelor’s degree from Peking University.

My research interests lie at the intersection of machine learning (ML) and computational biology. I am broadly interested in developing novel ML tools to accelerate biological research, with a particular focus on tackling protein-related challenges. My primary research focuses include:

  • Machine learning-guided protein engineering
  • Biological network alignment
  • Protein function prediction

News

Feb 05, 2025 Our paper on ML-guided combinatorial library design in enzyme engineering has been selected for presentation in the Highlights Track at RECOMB 2025, occurring April 26-29 in Seoul, South Korea! I will also be presenting the proceeding paper Learning maximally spanning representations improves protein function annotation on behalf of my labmate Jiaqi Luo!
Jul 29, 2024 One paper on ML-guided combinatorial library design in enzyme engineering has been published in Nature Communications.
May 29, 2024 One paper on Conformal prediction for enzyme function has been published in PLOS Computational Biology.
Apr 11, 2023 One paper on Supervised biological network alignment has been accepted for presentation at ISMB 2023, which will be held on July 23-27 in Lyon, France!

Publications (at Georgia Tech)

  1. iScience
    Pareto-optimal sampling for multi-objective protein sequence design
    Jiaqi Luo, Kerr Ding, and Yunan Luo
    iScience, 2025
    HTML Code
  2. Nat Commun
    Machine learning-guided co-optimization of fitness and diversity facilitates combinatorial library design in enzyme engineering
    Kerr Ding, Michael Chin, Yunlong Zhao, and 6 more authors
    Nature Communications, 2024 (Highlights Track at RECOMB 2025)
    HTML Code
  3. Nat Rev Electr Eng
    Opportunities and challenges of graph neural networks in electrical engineering
    Eli Chien, Mufei Li, Anthony Aportela, and 8 more authors
    Nature Reviews Electrical Engineering, 2024
    HTML
  4. PLOS CB
    Leveraging conformal prediction to annotate enzyme function space with limited false positives
    Kerr Ding, Jiaqi Luo, and Yunan Luo
    PLOS Computational Biology, 2024
    HTML Code
  5. ISMB
    Supervised biological network alignment with graph neural networks
    Kerr Ding, Sheng Wang, and Yunan Luo
    ISMB/Bioinformatics, 2023
    HTML Code

Academic Services

Reviewer: RECOMB (2024, 2025), ISMB (2024, 2025), Transactions on Machine Learning Research (TMLR), PLOS One, npj Artificial Intelligence, ICLR 2025 workshops (AI4NA, MLMP, GEM), CVPR 2025 workshop (MM4Mat)

Teaching

  • TA for CSE7850/CX4803 Machine Learning in Computational Biology (Spring 2025)