Massachusetts Institute of Technology
Ph.D. in Information Technology
2022–Present
AI for Behavioral Science
Robin Na
Building and evaluating AI agents that learn from scientific evidence and predict human behavior.
I am a PhD candidate in Information Technology at MIT, advised by Abdullah Almaatouq and Michiel Bakker. My work spans research synthesis, behavioral simulation, and evaluation under distribution shift.
I build systems that turn papers and behavioral data into predictions, and study when those predictions hold up in new settings. I also develop multi-agent environments to evaluate how AI scientists gather evidence and make research decisions.
Education
Seoul National University
B.S. in Physics & B.A. in Knowledge Ecology
Summa cum laude
Korea Presidential Science Scholar
2021
Research
Designing personas for behavioral simulation
Which features of a person matter for simulating their behavior? I build an automated framework that iteratively constructs behavioral profiles for LLM simulations. It selects features by their ability to predict collective human behavior under experimental designs that differ from those used to develop the profiles.
Designing Behavioral Profiles for Language Model Simulation of Out-of-Distribution Human Behavior
Under review at Information Systems Research · Accepted at UserSim at NeurIPS
Undocumented Evidence and Collective Failure in Multi-Agent AI Scientists
How should an AI scientist decide what to study next? I build multi-agent research environments to evaluate how agents allocate data-collection budgets and combine heterogeneous, noisy, and potentially misleading evidence. The goal is to understand how those research decisions shape predictions and policy recommendations in new settings.
Working Paper
Can scientific literature improve AI predictions?
I build research-synthesis agents that extract and integrate evidence from scientific papers to predict behavioral experiments. By evaluating predictions on new experimental designs, this work examines both the capabilities of AI research agents and the predictive value of the literature they draw on.
Auditing Scientific Literatures with Large Language Models
Working Paper · Presented at the AI for Science Workshop at ICML
The Integration of Explanation and Prediction in Behavioral Science
Connecting causal explanation and predictive evaluation to develop behavioral science that generalizes across settings.
Current Directions in Psychological Science, 35(3), 2026
See my CV for the full list of papers.
Experiences
Amazon
Applied Scientist Intern
2023, 2024
Carnegie Mellon University
Predoctoral Research Fellow
2021–2022
Santa Fe Institute
Undergraduate Researcher
ASU–Santa Fe Institute Center for Complex Systems
2020
Connect
I always love to interact with and learn from people with diverse perspectives. If there are any interesting research ideas you would like to discuss, please don't hesitate to email me!