Picture a world where a self-driving shuttle navigates rush-hour traffic with your children in the back seat. Where AI helps doctors make split-second decisions in the emergency room. Where algorithms determine how to transition to clean energy without harming communities. In each scenario, the stakes couldn't be higher. Trust becomes everything.
Research Themes
Safety & Verification
AI Safety & Verification
Developing mathematical frameworks and validation methods to ensure AI systems are safe, reliable, and aligned with human values in high-stakes applications.
Simulation & Rare-Event Analysis
Accelerating safety evaluation through importance sampling and simulation techniques that find potential failures 1000x faster than conventional methods.
Autonomous Systems & Robotics
Building trustworthy autonomous decision-making systems for transportation, robotics, and human-robot collaboration with rigorous safety guarantees.
Optimization & Decision Making
Optimization & Decision Making
Designing algorithms for sequential decision-making under uncertainty, integrating reinforcement learning, Bayesian methods, and mathematical optimization.
Investment & Risk Management
Quantitative methods for risk assessment and investment decision-making in uncertain environments, from financial markets to infrastructure planning.
Applications & Sustainability
Supply Chain & Logistics
Modeling resilient supply chains using AI-driven optimization, particularly for critical minerals and energy systems during the global energy transition.
Critical Minerals & Energy Resilience
AI-powered exploration, extraction optimization, and supply chain modeling for critical minerals essential to clean energy technologies.
Climate Change & Sustainability
Applying AI to accelerate equitable energy transitions, optimize renewable resources, and ensure sustainable development that serves all communities.
Key Contributions
Deep Importance Sampling. A technique that finds potential failures in autonomous systems 1000x faster than conventional methods, enabling safety validation in weeks instead of decades.
Adaptive Meta-Learning for Safety. A framework that lets machine learning systems recognize situations beyond their training, creating a sixth sense for uncertainty. Recognized with a CPS Rising Star 2024 award.
Mineral-X Supply Chain Models. AI systems developed with Stanford Mineral-X that optimize for resilience and equity in critical mineral supply chains, ensuring the green revolution serves all communities.