Sanketh Vedula

PhD Candidate, Technion • ML Research Intern, Pfizer

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I’m a PhD student at the Computer Science Department, Technion – Israel Institute of Technology advised by Alex Bronstein. I’m also a ML Research Scientist Intern at Pfizer, Berlin.

I am interested in developing machine learning methods that assist in making scientific discoveries. In particular, I research and develop tools involving generative modeling, optimal transport, geometric deep learning, and distribution-free uncertainty estimation, to tackle problems in computational and structural biology, drug discovery, and physics.

More generally, I aspire to build solutions for challenging problems that have real-world impact. I love science, I enjoy doing research, learning, and discussing ideas with people.

In parallel to my graduate studies, I was a ML Researcher at Sibylla, a Technion spin-off start-up, building mid-frequency quant-trading and portfolio management strategies.

I received an M.Sc. (cum laude) in Computer Science from Technion in 2020, where I was advised by Alex Bronstein and Michael Zibulevsky. Earlier, I received a B.Eng. (Hons.) in Computer Science from BITS Pilani in 2017. I also held visiting positions at Institute of Science & Technology Austria, Imperial College London, and Chennai Mathematical Institute.

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selected publications

  1. ICML Workshop
    Scalable unsupervised alignment of general metric and non-metric structures
    S. Vedula, V. Maiorca, L. Basile, and 2 more authors
    arXiv preprint arXiv:2406.13507 (AI for Science Workshop, ICML), 2024
  2. bioRxiv
    Seeing Double: Molecular dynamics simulations reveal the stability of certain alternate protein conformations in crystal structures
    A. RosenbergS. VedulaA. Bronstein, and 1 more author
    bioRxiv, 2024
  3. ICML Workshop
    Continuous Vector Quantile Regression
    S. VedulaI. TalliniA. Rosenberg, and 4 more authors
    In ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023
  4. AISTATS
    Vector Quantile Regression on Manifolds
    M. PegoraroS. VedulaA. Rosenberg, and 3 more authors
    In Proc. International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
  5. ICLR
    Fast Nonlinear Vector Quantile Regression
    A. RosenbergS. VedulaY. Romano, and 1 more author
    In Proc. International Conference on Learning Representations (ICLR), 2023
  6. MSML
    Spectral Geometric Matrix Completion
    A. Boyarski, S. Vedula, and A. Bronstein
    In Proceedings of the Second Mathematical and Scientific Machine Learning Conference (MSML), 2021