Research Group Vision
In the RDI Research Group, we seek to employ modern computational techniques to tackle big problems, from infectious diseases to climate change. We employ cutting-edge molecular simulation, mathematical modeling, and machine learning techniques to describe complex interfacial phenomena, whether that be the interface between a pathogen and antibody (for vaccine design), small molecule and cell membrane (for treating brain-based diseases), polymer and oxide surface ( for plastics upcycling), or electrolyte and cathode material (for Li-ion batteries).
Designing Vaccines Against Highly Mutable Pathogens
Combining sequence and structural information with mathematical modeling and deep reinforcement learning techniques, we model the process by which antibodies evolve with the goal of designing new and better vaccine formulations for highly mutable pathogens like HIV, influenza, and malaria.
Uncovering New Treatments for Neurological and Neuroinfectious Diseases
Leveraging molecular dynamics simulations and machine learning, we seek to understand - and counter - the pathogenic processes underlying neurological diseases like Alzheimer's disease, and neuroinfectious diseases like HIV-associated neurocognitive disorder (HAND).
Controlling the Near-Surface Environment of Solid Materials for Energy Applications
Utilizing reactive force fields and enhanced sampling simulation techniques, we seek to understand the key interactions that take place at the polymer/metal oxide and electrolyte/cathode interfaces. This knowledge will inform the design of novel catalysts and electrolyte/cathode materials for improved plastics upcycling and Li-ion battery design.
Engineering Antibodies with Improved Properties
Using bioinformatics, machine learning, and molecular simulations, we are working to develop a deep understanding of antibody structure, function, and dynamics, to guide the design of antibodies with improved stability, developability, and binding characteristics.