In today's constantly evolving world, intelligent systems must be able to make high-quality decisions in real-time in order to safely react to changing conditions, unexpected disruptions, and interact with other decision-makers. Mathematical optimization is a powerful and flexible tool to formulate and solve decision-making problems, but three main challenges have always limited its widespread adoption:
- Computing optimal decisions in real-time;
- Ensuring robustness to uncertain problem parameters;
- Coordinating multi-agent interactions.
My research addresses these challenges using tools from optimization, control theory, and machine learning. More specifically, I use data to develop new methods for decision-making in highly dynamic and uncertain environments, including acceleration schemes for optimization algorithms in fast real-time scenarios, frameworks to make decisions presence of uncertainty, and architectures to model multi-agent rationality and design interventions.
I put strong emphasis on computations, by aiming at building a trade-off between quality of the proposed techniques and computational tractability. Along the way, I seek to develop open-source numerical tools to help practitioners apply my work in the real-world. I work on various applications in autonomous systems, robotics, power systems, healthcare, finance, and engineering.