Bio

I'm a Stanford Data Science Fellow and Postdoc at the Department of Biochemistry, working with Prof. Rhiju Das. I'm building lab-in-the-loop Generative AI for programmable biology, bridging deep learning and high-throughput RNA biochemistry at scale.

Previously, I completed my PhD in Computer Science at the University of Cambridge with Prof. Pietro Liò. My doctoral work focused on Geometric Deep Learning for molecular modelling and design. As a highlight, I developed gRNAde, an inverse design framework that we used to design and experimentally validate new functional RNA enzymes in collaboration with Dr. Phil Holliger's group at MRC LMB. My research has been recognized by the Qualcomm Innovation Fellowship and the A*STAR National Science Scholarship. I've also been a research scientist intern at Prescient Design (Genentech) and FAIR Chemistry (Meta AI) during my PhD.

Honors & Awards

  • Stanford Data Science Fellowship, Stanford Data Science (2025)
  • Qualcomm Innovation Fellowship, Qualcomm Inc. (2024)
  • National Science Scholarship, A*STAR, Singapore (2021)

Professional Education

  • Ph.D., University of Cambridge, UK, Computer Science (2026)
  • B.Eng., Nanyang Technological University, Singapore, Computer Science, Valedictorian (2019)

Stanford Advisors

Contact

  • Academic
    chaitjo@stanford.edu
    University - Scholar Department: Biochemistry Position: Postdoctoral Scholar

Additional Info

Links

All Publications

  • Template-based RNA structure prediction advanced through a blind code competition. bioRxiv : the preprint server for biology Lee, Y., He, S., Oda, T., Rao, G. J., Kim, Y., Kim, R., Kim, H., Heng, C. K., Kowerko, D., Li, H., Nguyen, H., Sampathkumar, A., Gómez, R. E., Chen, M., Yoshizawa, A., Kuraishi, S., Ogawa, K., Zou, S., Paullier, A., Zhao, B., Chen, H. L., Hsu, T. A., Hirano, T., Chiu, W., Gezelle, J. G., Haack, D., Hong, Y., Jadhav, S., Koirala, D., Kretsch, R. C., Lewicka, A., Li, S., Marcia, M., Piccirilli, J., Rudolfs, B., Srivastava, Y., Steckelberg, A. L., Su, Z., Toor, N., Wang, L., Yang, Z., Zhang, K., Zou, J., Baker, D., Chen, S. J., Demkin, M., Favor, A., Hummer, A. M., Joshi, C. K., Kryshtafovych, A., Küçükbenli, E., Miao, Z., Moult, J., Munley, C., Reade, W., Viel, T., Westhof, E., Zhang, S., Das, R. 2025

    Abstract

    Automatically predicting RNA 3D structure from sequence remains an unsolved challenge in biology and biotechnology. Here, we describe a Kaggle code competition engaging over 1700 teams and 43 previously unreleased structures to tackle this challenge. The top three submitted algorithms achieved scores within statistical error of the winners of the recent CASP16 competition. Unexpectedly, the top Kaggle strategy involved a pipeline for discovering 3D templates, without the use of deep learning. We integrated this template-modeling pipeline and other Kaggle strategies to develop a single model RNAPro that retrospectively outperformed individual Kaggle models on the same test set. These results suggest a growing importance of template-based modeling in RNA structure prediction.

    View details for DOI 10.64898/2025.12.30.696949

    View details for PubMedID 41509375

    View details for PubMedCentralID PMC12776560

https://orcid.org/0000-0003-4722-1815