Samuel C. Hoover
Chemical engineering Ph.D. candidate, PPG Fellow, polymer physicist, and computational modeler.
Projects
CV | Resume
About Me
Programmer by choice, chemical engineer by compulsion (I’m joking, I’m joking!).
I am actively looking for full-time positions with an expected start date of Summer 2024.
I am a PPG Fellow and fourth year Ph.D. candidate in Chemical Engineering at UMass Amherst studying the phase behavior of complex polymer systems in the Muthukumar Group. My work focuses on using theory, simulation, and machine learning techniques to investigate the fundamental physics underlying polymer aggregates and self-assemblies in synthetic and biological systems. Our goal is to leverage our understanding of intermolecular interactions to develop therapeutics for diseases that are onset by aberrant aggregations.
Outside of the lab, my hobbies include cooking and baking (especially when it’s for my girlfriend), learning computer science and molecular biology, reading Cold War and modern era (post-1980) history, and hanging out with my cats Leo (demon) and Lou Lou (angel) whom I both love equally.
You can find more about my ongoing and completed projects by visiting my projects page.
Education
Ph.D., Chemical Engineering
B.S., Chemical Engineering
Minors in Mathematics and International & Cross-Cultural Perspectives
Selected Academic Experience
Prof. M. Muthukumar, University of Massachusetts Amherst
Studying physics underlying aggregation in synthetic and biological systems
Prof. Peng Bai, University of Massachusetts Amherst
Studied use of convolutional neural networks to predict material properties of nanoporous materials
Industrial Experience
Triton Systems, Inc.
Optimized induction heating coil using electromagnetic modeling in COMSOL for DHS SBIR project
SI Group
Implemented PI Asset Framework, conducted root cause analysis on company loss events, and led group intern project
Publications
Liu, Y.; Perez, G.; Cheng, Z.; Sun, A.; Hoover, S. C.; Fan, W.; Maji, S.; Bai, P. ZeoNet: 3D Convolutional Neural Networks for Predicting Adsorption in Nanoporous Zeolites. Journal of Materials Chemistry A 2023. DOI: https://doi.org/10.1039/D3TA01911J.
Ongoing work
Hoover, S. C.; Margossian, K. O.; M. Muthukumar. Theory and Quantitative Assessment of pH-responsive Polyzwitterion-Polyelectrolyte Complexation. In preparation.
Hoover, S. C.; Li, S.-F.; M. Muthukumar. Using Machine Learning to Predict the Microphase Separation Transition of Sequence-Defined Charged Heteropolymers in Concentrated Solutions. In preparation.