Bio

I'm a Stanford Data Science Fellow and postdoc with Rhiju Das at the Department of Biochemistry. I build lab-in-the-loop AI for RNA biology, pairing deep learning with wet-lab experiments at scale.

I did my PhD in Computer Science at the University of Cambridge with Pietro Liò, on geometric deep learning for molecular design. I built gRNAde, the first 3D generative model for RNA, and validated it in the wet lab as a visiting researcher in Phil Holliger's group at the MRC LMB. I've also interned at Prescient Design (Genentech) and FAIR Chemistry (Meta AI), and my work has been recognized by the Qualcomm Innovation Fellowship and the A*STAR National Science Scholarship.

Honors & Awards

  • Stanford Data Science Fellowship, Stanford Data Science (2026)
  • 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