About me

I’m a UCLA graduate pursuing advanced study in Statistics and Machine Learning, with a focus on developing rigorous methodology for sequential decision-making and reinforcement learning. Having studied Data Theory at UCLA, I’m particularly drawn to problems at the intersection of statistical inference, algorithmic development, and real-world applications.

I’m drawn to questions at the core of reinforcement learning: How can mathematical and statistical principles guide the design of better algorithms? How do we extend RL from discrete to continuous settings—handling continuous time and infinite horizons? And how do we move from simulation to the real world, building agents that are robust to stochastic and adversarial noise? My work draws on tools from information geometry, stochastic analysis, optimal transport, and stochastic control to tackle these questions.

Outside of research, I enjoy rock climbing and running marathons!

I’m always eager to discuss research ideas and collaborate, so feel free to reach out.

Email: 20iyadomit@gmail.com