My background spans physics, machine learning, and quantum computing. As Senior Lead Scientist at Q‑CTRL, I lead the Applications & Algorithms team of 4–6 scientists while remaining deeply involved in research, algorithm development, and scientific software.

Current Work & Research Interests

Our work spans quantum simulation, quantum optimization, and quantum chemistry, with additional forays into quantum machine learning and other emerging areas. A central focus is pushing quantum hardware to its physical noise limit—combining improved algorithm design with the tight integration of error suppression and compilation to extract maximum performance from today's devices. I’m increasingly interested in fault-tolerant quantum algorithms and the systems questions that arise as quantum computing approaches fault tolerance.

My work also spans scientific software engineering. 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. I’m also developing and applying AI-enabled workflows to accelerate scientific reasoning, software development, and research iteration.

Recent Publications

• Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor — preprint, arXiv:2605.04025
• Simulating the dynamics of an SU(2) matrix model on a trapped-ion quantum computer — preprint, arXiv:2604.14094
• Automated discovery of heralded ballistic graph state generators for fusion-based photonic quantum computation — Phys. Rev. A 113, 042608 (2026)
• Integrated error-suppressed pipeline for quantum optimization of nontrivial binary combinatorial optimization problems on gate-model hardware at the 156-qubit scale — New J. Phys. 28, 064506 (2026)

Background

I previously spent five years as an Information Scientist at the RAND Corporation, where I led interdisciplinary AI/ML research on diverse policy challenges. My work there focused on AI safety—particularly adversarial vulnerabilities in computer vision systems—generative modeling and network science, and computational modeling across defense and public-policy problems. Separately, I conducted red-team evaluations for OpenAI and Meta, including GPT-4 prior to its public release.

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.

AI Safety & Red-Teaming

Red-team evaluator for OpenAI and Meta language models, including pre-release GPT-4. At RAND, I helped found and grow the corporation's internal AI research community, organizing seminars and fostering cross-disciplinary collaboration on AI policy questions.

Teaching

• 2018–2022 Core Faculty Member, Pardee RAND Graduate School: designed and taught Introduction to Modern AI and Introduction to Blockchain Technology. Course materials for the AI course are publicly available on GitHub.
• 2015–2016 Lecturer, University of Southampton: Differential Equations, Mathematical Methods, and Light and Waves.
• 2010–2012 Head Teaching Assistant, UC Santa Barbara: managed a team of 40+ teaching assistants across the Physics Department.