Computational Biology Thesis Proposal

  • Remote Access - Zoom
  • Virtual Presentation
  • Ph.D. Student
  • Joint CMU-Pitt Ph.D. Program in Computational Biology
  • Computational Biology Department, Carnegie Mellon University
Thesis Proposals

Sequential strategies for automated science and engineering

This thesis will address biological problems that can be resolved through sequential model-based optimization.  First, we propose protein fold family-regularization as a form of ML-assisted directed evolution. Next, we use sequential Bayesian optimization to automatically configure laboratory-based experimental designs.  This includes developing synchronous and asynchronous parallel algorithms, as well using a cloud-based platform for experimental automation.  Finally, we develop algorithms for automating vaccine design that can be used in-line with laboratory-based experimentation, and show how our optimization framework can augment a deep-learning approach in order to automatically generate candidate epitopes for vaccine inclusion.  

Thesis Committee:
Chris Langmead (CMU, Chair)
David Koes (PITT)
Russell Schwartz (CMU)
Jennifer Listgarten (UC Berkeley)

Zoom Participation. See announcement.

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