Amy Wang
At Lila Sciences, I lead work on evaluations and environments for LLM post-training, with a focus on scientific decision-making in biologics discovery.
Previously, I was a Senior ML Scientist at Prescient Design (Genentech), where I led computational strategy for three antibody programs and built ML models for protein design, affinity prediction, and developability optimization. This work led to methods adopted across the antibody portfolio and shaped my interest in automating drug discovery decision-making.
My work draws on both machine learning and experimental biophysics. I earned my PhD in Chemical Engineering from Stanford in 2023, studying force-dependent cell adhesion proteins, and worked on protein–polymer systems for drug delivery as an undergraduate at MIT.
amywangsci [at] gmail.com
Publications
Concept Bottleneck Language Models For Protein Design
International Conference on Learning Representations (ICLR) 2025
Fine-tuning discrete diffusion models via reward optimization with applications to DNA and protein design
International Conference on Learning Representations (ICLR) 2025
Multi-level force-dependent allosteric enhancement of αE-catenin binding to F-actin by vinculin
Journal of Molecular Biology 2023 | PDF
A retrievable implant for the long-term encapsulation and survival of therapeutic xenogeneic cells
Nature Biomedical Engineering 2020 | PDF
Predicting protein-polymer block copolymer self-assembly from protein properties
Biomacromolecules 2019 | PDF
Design of insulin-loaded nanoparticles enabled by multistep control of nanoprecipitation and zinc chelation
ACS Applied Materials & Interfaces 2017 | PDF
Patent Filed October 7, 2015.
Workshops
A guided design framework for the optimization of therapeutic-like antibodies
Generative and Experimental Perspectives for Biomolecular Design, ICLR 2025 | PDF
SurfProp: A surface-based property prediction framework for antibody developability and screening
GenBio Workshop, ICLR 2025 | PDF
The Effects of Structural Conditioning on Antibody Inverse Folding
Women in ML Workshop, NeurIPS 2023
Learning from physics-based features improves protein property prediction
Machine Learning in Structural Biology Workshop, NeurIPS 2022
PDF | Poster
Awards and Affiliations
- University Nominee for the 2023 Schmidt Science Fellows Program
- Valuation of Public Companies in the Life Sciences Pitch Competition (INDE209) - First Place (2020)
- NSF Graduate Research Fellowship (2018-2023)
- Stanford ChEM-H Chemistry/Biology Interface Predoctoral Training Program (2018-2023)
- Stanford Graduate Fellowship (2018-2023)
- MIT Tau Beta Pi (2017)
- MIT Chemical Engineering Departmental BP Academic Achievement Award (2016)