Samuel C. Hoover
Chemical engineering Ph.D., former 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 Fall 2024.
I am a recent PPG Fellow and Ph.D. graduate in Chemical Engineering at UMass Amherst. In the Muthukumar Group, I studied the phase behavior of complex polymer systems in using theory, simulation, and machine learning to investigate the fundamental physics driving aberrant aggregations like cataracts and those implicated in neurodegenerative diseases.
Outside of the lab, my hobbies include cooking and baking (especially when it is for my girlfriend), studying computer science and molecular and structural biology, reading Cold War and modern era (post-1980) history, and hanging out with my cats Leo, Lou Lou, and Ariel.
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 using
a suite of computational methods
Prof. Peng Bai, University of Massachusetts Amherst
Applied 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
Hoover, S. C.; Margossian, K. O.; M. Muthukumar. Theory and quantitative assessment of pH-responsive polyzwitterion-polyelectrolyte complexation. Soft Matter 20, 7199-7213 (2024). doi:10.1039/D4SM00575A.
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 11, 17570–17580 (2023). doi: 10.1039/D3TA01911J.
Ongoing work
Hoover, S. C.; Li, S.-F.; M. Muthukumar. Learning the sequence effects on the microphase separation transition of charged heteropolymers. In preparation.