Welcome to my website!

I am a proud French-Canadian

My passion lies in reimagining how science is done. From my earliest work modeling the Casimir effect in graduate school, I’ve been fascinated by what happens at the edge of knowledge, where theory must be tested against reality. Later, in the wet lab, I explored electrochemical systems, building cells, cycling them, probing their inner workings with NMR, XRD, and SEM. These experiences taught me that science moves forward when ideas are confronted with experiments, when predictions are pushed beyond the page and into the lab.

Today, artificial intelligence is transforming that process. AI can master existing knowledge, recognizing patterns across centuries of recorded discovery, but most of the world lies beyond curated datasets. I believe the real leap will come when AI doesn’t just analyze what’s written, but begins to experiment, explore, and generate data of its own. That vision drives my current research, where I’m developing a deep learning pipeline to predict pore fractions in porous materials. By generating synthetic pore structures, calculating pore fractions, and training a CNN model, I can then validate results on PFIB-SEM images of fuel cell catalyst layers. Through iterative labeling, the model improves its accuracy, inching closer to the kind of experimental intelligence that can accelerate discovery in the physical sciences.

I obtained my Ph.D. in Physics at Dalhousie University, where I worked alongside Jeff Dahn. During this work, I studied the impact of new electrolyte additives and different state of charge ranges on the cycling performance and properties of lithium cells, while also learning the basics of density functional theory (DFT) and nuclear magnetic resonance (NMR). Before that, I earned my M.Sc. at Université de Moncton on the theoretical modeling of the Casimir effect, which rooted my curiosity in the fundamentals of physics.

After my Ph.D., I joined the Obrovac Research Group as a research scientist, where I studied anode materials for lithium-ion batteries and gained further expertise in XRD and SEM. Later, I worked with the Venkat Viswanathan Electrochemical Energy Group (then at CMU, now at the University of Michigan) and the Jay Whitacre Research Group at CMU as a post-doctoral fellow. There, I developed nuclear magnetic resonance, electrochemical, and computational methods for lithium-ion cell monitoring, mitigation, and prediction.

I am currently a research scientist at the Laboratory for Transport Phenomena in Energy Systems working with Shawn Litster to improve performance of fuel cells.

At the core of my journey—from theoretical physics to electrochemistry to AI-driven pipelines—is a belief that transformative science requires transforming the scientific process itself. I’m motivated by the idea of enabling AI to turn the wheel of science, not just analyzing what is known, but actively generating the knowledge that will define what comes next.

Aside from research, I’m also passionate about science communication and the arts.