About me
Welcome to my website! I’m a physicist and machine learning researcher leading the Applications and Algorithms team at Q-CTRL, where I focus on developing quantum computing algorithms and building robust software infrastructure for scientific computing.
Current Work & Research Interests
At Q-CTRL, I lead a team developing practical applications for near-term quantum computers. Our work spans quantum optimization, simulation of quantum systems, quantum chemistry, and quantum machine learning. I’m particularly excited about projects that bridge the gap between theoretical quantum algorithms and real hardware implementations. Current work includes formulating protein folding problems as combinatorial optimization challenges suitable for quantum computers, designing improved quantum kernels for machine learning applications, and building state-of-the-art photonic circuit simulation engines. For a summary of recent publications, see here.
Beyond research, I play a key role in software engineering and team management. One aspect of quantum applications research that I particularly enjoy is ensuring that research software facilitates both agile exploration of new ideas and seamless integration with production systems. I believe that writing good code is fundamentally about thinking clearly—the challenge is making sure our software implementations are as elegant and well-structured as our ideas about how to best harness quantum computers.
Looking forward, the field is moving rapidly from proof-of-concept demonstrations toward genuine utility. I’m excited about the potential for quantum computers to tackle increasingly complex real-world problems as hardware continues to improve.
Background
I previously spent five years as an Information Scientist at the RAND Corporation, where I applied machine learning and computational modeling to diverse policy challenges. My work there focused on AI safety—particularly studying adversarial vulnerabilities in computer vision systems—computational modeling of nuclear weapons effects, and using graph neural networks to solve complex network problems. I also contributed to red-teaming efforts for large language models, including OpenAI’s GPT-4.
Before RAND, I was a postdoctoral researcher at the University of Southampton’s STAG Research Centre, studying black holes and gravitational solutions in string theory. I earned my PhD in Physics at UC Santa Barbara, focusing on the existence and stability of higher-dimensional black holes and using black hole physics to understand strongly coupled gauge theories through the gauge/gravity correspondence.
The thread connecting all my work—from string theory to policy research to quantum computing—is a fascination with using mathematical tools to understand complex systems and solve challenging problems. Whether it’s the information paradox in black holes, adversarial examples in machine learning, or optimization on quantum hardware, I’m drawn to questions where deep theory meets practical application.