Associate Machine Learning Scientist
February 2023 - Present @ Prescient Design / Genentech
Machine learning (especially geometric deep learning) for antibody discovery.
I am a Machine Learning Scientist at Prescient Design / Genentech.
My work is focused on ML approaches to antibody drug discovery, and I'm particularly focused on methods that can model the complex three dimensional structures of proteins.
In my free time, I home-roast coffee, make pizza, and quick-pickle things.
Experience
Summer 2022 @ Sandia National Laboratory
Geometric deep learning (specifically using Euclidean Neural Networks) for quantum chemistry, with applications to biomolecular simulation, with Josh Rackers.
Research Intern
Jan 2022 — Dec 2022 (Excluding Summer 2022) @ NASA Quantum Artificial Intelligence Laboratory (QuAIL)
Research Intern working with Zhihui Wang on quantum algorithms, particularly a QAOA inspired approach to Grover's algorithm.
Research Assistant
June 2018— Jan 2023 @ Institute for Condensed Matter Theory
Transport in topological materials and some exotic classical systems with Barry Bradlyn. Some of our work.
Rotation Student
2017—2018 @ Institute for Genomic Biology
Half year rotation in the lab of Jun Song, studying machine learning approaches to cancer genomics
- Took courses in machine learning and bioinformatics.
- Presented papers in neural network applications for genomics to the group journal club.
Teaching Assistant
Sep 2019— Jan 2023 @ U of I Physics Department
- Led discussion section for introductory physics classes.
- Peer mentor for two different undergraduate students.
Research Assistant
2013—2016 @ Michigan Center for Theoretical Physics
AdS/CFT with a focus on potential condensed matter applications with James Liu.
Study
Doctorate in Physics
2017—2023 @ University of Illinois
Urbana-Champaign, Illinois
Bachelors in Honors Physics and Mathematics
2012—2016 @ University of Michigan
Ann Arbor, Michigan