Vincent D. Zaballa

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I am a Ph.D. candidate at University of California, Irvine. I work on machine learning methods in biology with a strong interest in integrating systems and structural biology with applications in drug discovery. Given the complexity of biological data, I seek to combine biological data into more robust machine learning models to reduce entropy of biological sytems. A key application of this is in Bayesian optimal experimental design, which helps scientists decipher between competing biological hypotheses with maximum efficiency.

So far, the main software from my Ph.D. work is the JAX-based conditional normalizing flow library LFIAX with experimental design loss functions.

selected publications

  1. Stochastic Gradient Bayesian Optimal Experimental Designs for Simulation-based Inference
    Vincent D Zaballa, and Elliot E Hui
    ArXiv, 2023
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  2. Reducing uncertainty through mutual information in structural and systems biology
    Vincent D Zaballa, and Elliot E Hui
    ArXiv, 2024
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  3. Systems-Structure-Based Drug Design
    Vincent D Zaballa, and Elliot E Hui
    arXiv preprint arXiv:2410.10108, 2024
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