Research
My research develops computational methods for online safety, social sensing, and multilingual large-scale text mining, with emphasis on privacy, security, and societal impact.
My research sits at the intersection of computational social science, cybersecurity, and natural language processing. I build reproducible computational pipelines for behavioral trace data, text corpora, and survey data to study online safety, platform dynamics, and sociotechnical risk.
The three subpages below reflect one integrated agenda: (1) privacy and security in decentralized and anonymous systems, (2) social sensing and behavioral analytics for digital resilience, and (3) multilingual NLP and text mining for cultural and geopolitical analytics. Across these streams, I combine methodological depth (NLP, survival modeling, panel methods, causal inference strategies) with applied relevance in online safety, platform governance, and public-interest computing.
The agenda is structured for externally funded, team-based research. Each stream is modular, allowing PhD students to lead identifiable subproblems (data engineering, modeling, evaluation, and societal interpretation) while contributing to a coherent long-term program.
