This research develops a physics-based method for measuring lung elasticity from medical imaging to predict which emphysema patients will benefit from lung valve treatment. By creating detailed elasticity maps, the work aims to improve treatment selection, enhance patient outcomes, and provide new quantitative tools for assessing lung health.

This research applies fluid mechanics, numerical simulations, and machine learning to model the brain’s waste-clearance system during sleep. By investigating how fluid moves through brain tissue and how aging or injury affect this process, the work aims to identify strategies for preventing or slowing neurodegenerative diseases such as Alzheimer's.

This research improves climate prediction models by developing advanced computational methods for simulating cloud microphysics. By tracking more detailed information about cloud droplets and aerosol interactions, the work enhances understanding of how clouds influence Earth’s energy balance, rainfall, and climate change, helping reduce uncertainty in long-term global climate projections.

Victor's research investigates dynamic weakening, a process that can allow small earthquakes to grow into devastating megaquakes. Using supercomputer simulations of the San Andreas Fault, the study explores how stress, fluids, friction, and neighboring fault activity may trigger unexpectedly large earthquakes, improving seismic hazard prediction and understanding of earthquake behavior.

This research investigates earthquake risks associated with underground carbon dioxide storage. By studying seismic activity at the Decatur CO2 storage project, the work improves predictive geological models that account for hidden subsurface structures. The findings aim to make large-scale carbon storage safer, protecting both the climate and nearby communities.

This research models blood flow in narrowed arteries and during catheterization using the Herschel–Bulkley fluid model. By simulating flow and drug dispersion, it identifies factors affecting unpredictability. These insights enable optimized treatments, improved medical device design, and better visualization for clinicians, ultimately enhancing safety and outcomes in cardiovascular care.