Simon

machine learning, and computing.

I grew up in a small town in Ohio. In 2010, I graduated *Summa Cum Laude* with my Bachelor’s degree in Chemical Engineering and minor in Applied Mathematics from The University of Akron. During my undergraduate education, I tutored mathematics, carried out a mathematical modeling project in the Department of Mathematics, and worked as an engineering co-op at Bridgestone Americas Center for Research and Technology.

In the summer of 2009, I participated in an NSF-funded Research Experience for Undergraduates (REU) program at the Virginia Bioinformatics Institute at Virginia Tech. There, we developed an age-structured mathematical model of the spread of influenza that partitions disease transmission into a social and biological component.

Directed by my love for mathematics, I then moved to the beautiful city of Vancouver, Canada to pursue a Ph.D. in Mathematics at the University of British Columbia (UBC). With my supervisor, Professor Leah Keshet, I developed a mathematical model to understand how a protein signaling network orchestrates single-cell wound healing. We established a collaboration with Professor William Bement at the University of Wisconsin, who conducts single-cell wound healing experiments.

In the summer of 2012, I lived in Okinawa, Japan– a beautiful island– and, with Professor Erik De Schutter at the Okinawa Institute of Science and Technology (OIST), used mathematical models to study how the morphology of dendritic spines in neurons influences the lateral diffusion of receptor proteins.

I then transferred to UC Berkeley for a Ph.D. in Chemical Engineering under the supervision of Professor Berend Smit. At Berkeley, I employed molecular models, Markov-chain Monte Carlo simulations, and machine learning algorithms to computationally screen large databases of nanoporous materials for storing and separating gases. The applications we considered were vehicular natural gas storage and xenon/krypton separations. For several of these projects, we collaborated with experimental groups, including Professor Hong-Cai Zhou and Praveen Thallapally.

During Fall 2014, I worked as a data science intern at Stitch Fix, an e-commerse fashion company, where I wrote recommendation algorithms for clothing purchases.

I spent a sizable portion of 2015 at Lawrence Berkeley National Lab working with Maciej Haranczyk. We wrote a Python package for ideal adsorbed solution theory calculations and used random forests to predict gas selectivities in porous materials from quickly-computed structural descriptors.

In the summer of 2015, I took the opportunity to carry out my research at EPFL in Sion, Switzerland, where my Ph.D. supervisor is now a director of a research center.

During the end of my Ph.D., I worked with Professor Carlo Carraro to construct a statistical mechanical model of gas adsorption in porous crystals with rotating ligands.

Snowboarding, running, hiking, backpacking, snorkeling, playing guitar, traveling, red wine