Camille Bilodeau

Camille Bilodeau is an Assistant Professor in the Department of Chemical Engineering at the University of Virginia, School of Engineering and Applied Science. She holds a B.S. and M.S. from Northwestern University, a Ph.D. from Rensselaer Polytechnic Institute, and completed postdoctoral research at MIT. Her research focuses on combining molecular simulations, statistical physics, and artificial intelligence to design new molecules, surfaces, and proteins for applications in biomanufacturing, protein separations, and biotherapeutics.

Education:

• B.S. in Chemical and Biological Engineering, Northwestern University (2015)
• M.S. in Chemical and Biological Engineering, Northwestern University (2015)
• Ph.D. in Chemical and Biological Engineering, Rensselaer Polytechnic Institute (2020)
• Postdoctoral Researcher, MIT Chemical Engineering (2020-2022)

Research Themes: The Bilodeau Lab explores interdisciplinary approaches to molecular design, leveraging GPU-accelerated simulations and deep learning. Key areas include peptide functionalization for tunable surfaces, machine learning-driven property prediction, and statistical physics-based modeling of complex systems. Recent work emphasizes applications in antimicrobial peptide design and automated molecular discovery.

Recent Trends in Publications: Her recent articles highlight advancements in hybrid deep learning models (e.g., PepMNet), autonomous molecular discovery frameworks, and multi-scale modeling of ion effects in chromatographic systems. The work bridges theory and application, with contributions to both fundamental understanding and industrial biomanufacturing challenges.

Awards:

• NSF CAREER Award (2025)
• RPI William N. Gill Dissertation Prize (2020)
• Lawrence Livermore Advanced Simulations Fellowship (2016-2020)
• Multiple top poster awards at international conferences (2017-2018)

Advising & Labs: As a new faculty member (since 2023), she leads the Bilodeau Lab, focusing on AI-driven molecular engineering. Her work integrates computational tools with experimental validation, emphasizing collaboration with industry partners for translational impact. Active projects include NSF CAREER-supported peptide surface design and NIH-funded studies on protein separation mechanisms.