Thesis (Ph. D.)--University of Rochester. Department of Chemical Engineering, 2020.
Machine learning (ML) is a broad, flexible suite of applied statistics tools combined
with optimization algorithms, made possible by the advent of modern computing
power. The strength of ML methods is limited by data availability and complexity
of the task to learn, but nonetheless, ML has seen extensive use in the fields of
medicine, technology, and engineering. It also has applications in chemical engineering
and computational chemistry. In this thesis, I present various such applications.
Using ML for computer vision, I helped make a mixed-reality educational tool for
chemical engineering undergraduates, which led to further work on a virtual-reality
app for high school chemistry students. I have also used ML methods for functional
design of antimicrobial peptides, and investigated ways to address the problem of
dataset sizes in this setting, to increase the chances of its use in experimental laboratory
settings, where data is scarce. I also helped develop a framework for ML in
molecular dynamics simulations to reduce the barrier of entry for non-experts in ML.
Finally, my most recent work has been toward applying biasing methods from statistical
mechanics to COVID-19 disease modeling to improve model prediction without
parameter fitting. The broad range of applications presented here only scratches the
surface of what can be done with the powerful combination of traditional numerical
models augmented by machine learning methods, serving to illustrate how much
can be done with its wide adoption in the field of chemical engineering, and the
sciences as a whole.