I am Carey Okal, a master's student in Information Science at Cornell University (B.S. May 2026, concentrations in Data Science and Behavioral Science). My work focuses on computational social science and applying data science to build resilience and improve humanitarian response. This site is a window into my work.
Classic social psychology experiments, the Stanford Prison Experiment chief among them, are too ethically fraught to rerun on people. But what happens when the participants are LLM agents? Do role assignment, power asymmetry, and group identity produce the same dynamics in LLM agents that Zimbardo observed in people? And can we trust what the agents report about themselves?
The lab's NSF-funded project studies persuasive AI and the spread of misinformation. One thread of it is a classifier that scores text across four rhetorical strategies, causal, empirical, emotional, and moral, fine-tuned on LLM-labeled synthetic debates and validated against human annotators. The work has been published at NAACL and EMNLP 2025.
The data needed to warn us about disasters already exists in population movement through call detail records, environmental stress visible in satellite imagery, and vulnerability documented in ground surveys. The Data Science to Build Resilience and Improve Humanitarian Response Thought Summit, held in May 2026, brought together academic and industry researchers and humanitarian leaders to explore how data science and AI can help predict, prevent, respond to, and accelerate recovery from crises in some of the world's most vulnerable regions.
Community conservancies in East Africa do conservation-critical work, then compete for visibility against international booking platforms that take big cuts and couldn't care less about sustainability. I was selected to join a team of seven as part of the SMART Fellowship at Cornell, in partnership with USIU Chandaria Business School, to contribute to solutions to this problem.
Research aside, I build for the web — client work and my own products, all currently live.
