Investment Fellows
Tucker Burgin
University of Michigan
2019 Seed, 2020 Investment
Adviser: Prof. Heather B. Mayes
MolSSI Software Mentor: Sam Ellis
“Building software tools for understanding and evolving enzymes in silico guided by unbiased, all-atom simulations”
Jennifer Clark
North Carolina State University
2019 Seed, 2020 Investment
Adviser: Prof. Erik Santiso
MolSSI Software Mentor: Andrew Abi-Mansour
“Developing the first open-source application for thermodynamic calculations for the Statistical Associating Fluid Theory equation of state and associated coarse-graining method”
Sebastian Dick
Stony Brook University
2019 Seed, 2020 Investment
Adviser: Prof. Marivi Fernandez-Serra
MolSSI Software Mentor: Sam Ellis
“Improving electronic structure calculations with the help of machine learning”,”Development of an open source library for machine learned density functionals”
View on GitHub
Heta A. Gandhi
University of Rochester
2020 Investment
Adviser: Prof. Andrew D. White
MolSSI Software Mentor: Jessica A. Nash
“Extending machine learning software to coarse-grained molecular dynamics”
Samuel Greene
University of Chicago
2019 Seed, 2020 Investment
Adviser: Prof. Timothy Berkelbach and Prof. Jonathan Weare
MolSSI Software Mentor: Ben Pritchard
“Developing randomized linear algebra libraries for efficient quantum chemistry calculations”
Dr. Pascal T. Merz
Unviersity of Colorado, Boulder
2020 Investment
Adviser: Prof. Michael R. Shirts
MolSSI Software Mentor: Eliseo Marin-Rimoldi
“Enhancement of GROMACS to provide an extensible high-performance hybrid MC/MD framework”
Sina Mostafanejad
Florida State University
2019 Seed, 2020 Investment
Adviser: Prof. Albert Eugene DePrince III
MolSSI Software Mentor: Sam Ellis, Matthew Welborn
“Accurate Reduced-Density Matrix-Based Methods for Strongly-Correlated Systems”
View documentation
Nicholas Stair
Emory University
2019 Seed, 2020 Investment
Adviser: Prof. Francesco Evangelista
MolSSI Software Mentor: Jonathan Moussa
“Development of a self-contained quantum computer simulator with reference implementations of quantum algorithms for strongly correlated electronic structure problems”
