I'm a third-year PhD candidate in Computer Science at NYU Abu Dhabi, where I study how socio-technical systems shape attention, persuasion, and inequality. My work sits at the intersection of artificial intelligence, computational social science, and social/traditional media studies: I build large-scale measurements of platform behavior, probe where algorithms and institutions introduce bias, and design empirical tests that separate noisy anecdotes from durable, causal patterns. Prior to my PhD, I earned a Master's in Computer Science from the University of Toronto, and a Bachelor's in Computer Engineering from New York University Abu Dhabi.

My research agenda focuses on three broad themes: (1) auditing recommendation and ranking systems to understand how political information and viewpoints are amplified or suppressed; (2) mapping representation and visibility in media—who appears, how they're framed, and with what downstream consequences; and (3) examining gatekeeping and access in knowledge ecosystems, from scholarly publishing to data availability. I also study ideological behavior in modern large language models, connecting model outputs to real-world persuasion and stance-taking.

Methodologically, I have utilized large-scale data collection and engineering, network analysis, mixed-effects and causal inference, survey and behavioral experiments, and contemporary NLP/LLM evaluation techniques to investigate these research themes. I aim for reproducible pipelines and policy-relevant findings, pairing observational evidence with experiments whenever possible, releasing tools and code, and collaborating across computer science, information science, and the social sciences. Ultimately, my goal is to develop general, testable frameworks for auditing algorithmic systems and for measuring representation and influence across digital media—work that advances science while informing governance and public debate.