News and events

I presented an overview of my research on physics-informed machine learning for turbulence, hurricanes and wind-energy cybersecurity. The talk covered two threads: machine-learning-enhanced sub-grid-scale turbulence modeling for hurricane simulation in WRF, and physics-informed anomaly detection for wind energy cybersecurity.

Thanks to the faculty and colleagues for the questions afterwards — they were a good reminder of how interdisciplinary this work has become.

I gave a guest lecture for the ENME 432L Fluid and Energy Lab, a course I had previously supported as a teaching assistant for several semesters. I talked through my PhD work on hurricane simulation and on using machine learning to build more accurate, physically consistent turbulence models for atmospheric flow.

Thanks to Dr. Alok Ghanekar for the invitation, and to the students for their questions.

I defended my dissertation, “Invariance-embedded machine learning sub-grid-scale stress models for meso-scale hurricane boundary layer simulations: model development, a priori assessment, and a posteriori tests with WRF.”

Thanks to my advisor and committee chair Dr. Meilin Yu, and to my committee — Dr. Charles Eggleton, Dr. Tim Oates, Dr. Sayantan Bhattacharya and Dr. Alok Ghanekar — for their guidance throughout.

Recognized at the UMBC Doctoral Candidates Recognition Ceremony — a milestone in my PhD under the guidance of Dr. Meilin Yu, and a good point from which to keep pushing on machine-learning-enhanced turbulence modeling for hurricane simulation.

I received the Urmila and Karman Ghia Graduate Student Scholar Award at ASME FEDSM-SHTC-ES 2026 in Bellevue, Washington. The award supports international graduate students in the United States working in computational fluid dynamics, and comes with an assignment to the Computational Fluid Dynamics Technical Committee for 2026–2027.

My poster on machine learning-enhanced turbulence modeling for hurricane boundary layer simulations received the Doctoral Student Award at COEIT Research Day. An undergraduate poster I mentored, on dynamic stall in vertical-axis wind turbines, took the Undergraduate Student Award the same day.

I attended the summer school and hackathon at the University of Washington, Seattle, which brought together 40 graduate students from across the United States and Canada with support from the NSF and PIMS.

During the hackathon I worked on Project D, Neural ODEs — exploring time integration methods and training strategies, which is directly relevant to weather forecasting and other nonlinear dynamical systems.

Event pages: PIMS and the organisers' site.

UMBC's College of Engineering and Information Technology covered the ASME Ghia Scholar award.

UMBC covered the machine-learning approach behind my hurricane simulation work.

UMBC's GESTAR II and the Department of Physics both covered our Journal of Advances in Modeling Earth Systems paper with Dr. Stephen Guimond, on how numerical dissipation in WRF damps the convective-burst kinetic energy that drives hurricane rapid intensification — a possible contributor to the low bias in operational rapid-intensification forecasts.

UMBC News on the Strategic Award for Research Transitions that seeded Dr. Guimond's hurricane modeling work. The preliminary research was carried out by Dr. Guimond with Devin Protzko '20, physics and mathematics, and me. It established that the numerical modeling approach was the more promising path, and led to a $682,000 NSF grant with the New Jersey Institute of Technology.