My Research

My research lies at the intersection of probability theory, computer science, and statistics, focusing on the development and analysis of stochastic models that elucidate complex systems. I am particularly interested in how probabilistic methods can inform computational processes and statistical inference.​

I investigate the behavior of random structures, such as graphs and trees, to understand phenomena like connectivity, phase transitions, and component sizes. This work has applications in network theory and algorithm design, where understanding the probabilistic properties of structures can lead to more efficient algorithms.​

My work includes the study of stochastic processes, such as the Tree Builder Random Walk, to analyze their long-term behavior and scaling limits. These analyses often involve deriving laws of large numbers, central limit theorems, and concentration inequalities, which are fundamental in both theoretical and applied probability.​

The probabilistic models I study have direct implications for computer science, particularly in areas like randomized algorithms, data structures, and complexity theory. For instance, understanding the probabilistic behavior of certain algorithms can lead to improvements in their average-case performance and reliability.​

I apply probabilistic models to statistical inference problems, exploring how randomness can be harnessed to make predictions and decisions under uncertainty. This includes work on probabilistic models that underpin statistical learning methods and Bayesian inference, contributing to more robust and interpretable models.​

In addition to theoretical work, I develop computational tools to simulate and visualize probabilistic models, facilitating experimentation and education. I am committed to open science, sharing code and resources to promote transparency and collaboration in research.