MolSSI’s 2023-2024 Software Fellows


Anja Conev

Rice University

2023-24 Software Fellow

MolSSI Software Mentor: Dr. Jing Chen

"Developing the DINC-Ensemble toolkit for accelerated ensemble docking of large flexible ligands"

Diego Kleiman

University of Illinois, Urbana-Champaign

2023-24 Software Fellow

MolSSI Software Mentor:

"Design and implementation of a machine learning model for automatic reaction coordinate discovery in proteins"

Jeremy Leung

University of Pittsburgh

2023-24 Software Fellow

MolSSI Software Mentor:

"An Interoperable WESTPA Framework for Machine-Learning-Enhanced Weighted Ensemble Simulations of Rare Events"

Ericka Miller

Case Western Reserve University

2023-24 Software Fellow

MolSSI Software Mentor:

“Implementing an efficient, stable, and open-source version of the state-averaged Resonating Hartree-Fock method for use in photochemical applications”

Augustine Onyema

Graduate Center, City University of New York

2023-24 Software Fellow

MolSSI Software Mentor:

“Developing python modules to compute the lifetimes of hydrogen bonds and characterize the conditional activity of proteins by secondary structure type to deduce protein conformations”

David Poole

Georgia Tech

2023-24 Software Fellow

Advisor: Prof. David Sherrill

MolSSI Software Mentor: Dr. Sina Mostafanejad

"Development of reduced-scaling coupled cluster utilizing graphics processing units to enable high-performance, high-accuracy calculations"

Evan Pretti

University of California, Santa Barbara

2023-24 Software Fellow

MolSSI Software Mentor:

“Development of a flexible and extensible Python framework for high-performance replica exchange molecular dynamics simulations using the OpenMM simulation engine, with applications to coarse-grained modeling of peptide aggregation”

Zhengkai Tu

Massachusetts Institute of Technology

2023-24 Software Fellow

MolSSI Software Mentor:

“Development of a microservice-based synthesis planning tool for accelerating molecular discovery with tree search and machine learning algorithms”

Valeria Rios Vargas

Rutgers University

2023-24 Software Fellow

Advisor: Prof. Michele Pavanello

MolSSI Software Mentor: Dr. Sina Mostafanejad

“Modeling plasmonic nanoparticles and their dynamical interaction with molecules and materials with quantum embedding methods based on subsystem DFT, orbital-free DFT and retardation-including electrodynamics”