Hi, I'm Rojin!
Some people know me as Nazdim, and either name is great.
I'm a Biostatistician II in social epidemiology at the Stanford Center for Population Health Sciences (PHS), where I work with Dr. David Rehkopf. My work is a collaboration between PHS and the Stanford Center on Poverty and Inequality (CPI), with Dr. David Grusky. I'm also pursuing an MS in Computer Science at Georgetown University.
My work focuses on predicting population health behaviour, with the goal of improving health outcomes. I combine LLM-powered social simulations with classical NLP and machine learning, working with electronic health records (EHR) and long-form narrative data. It builds on my work on the limits of LLM-simulated societies, which showed that alignment can erase real cross-cultural differences.
More broadly, my research sits between computer science and health. I study how large language models behave in medical settings, from cognitive bias to how they respond when patients arrive with an incorrect self-diagnosis, and I've helped build AgentClinic, a benchmark for evaluating AI in simulated clinical environments. I'm also interested in causal inference, speech and language technology for Persian and other low-resource languages, and what LLMs can and can't tell us about human societies.
Before this, I was a software engineer on Capital One's AI Foundations team, and a research assistant at Stanford with Susan Athey, at Johns Hopkins with Anjalie Field, and at the University of Maryland, where I earned my BS in Information Science and was the department's 2023 commencement speaker.
Things I think about
- Social epidemiology
- Social simulation
- Health behaviour prediction
- Population health
- Medical AI and bias
- Large language models
- Causal inference
- Low-resource languages
- Computational social science
β¨ News: My paper The Limits of Simulated Societies: How LLM Alignment Erases Cross-Cultural Variance was accepted at AACL! π
Curriculum vitae
The short version is below. For the full thing, grab the PDF.
Experience
- CurrentBiostatistician IIStanford Center for Population Health Sciences (PHS), with Dr. David Rehkopf
In collaboration with the Stanford Center on Poverty and Inequality (CPI) and Dr. David Grusky - Aug 2024 β Sep 2025Software Engineer, AI FoundationsCapital One
- Apr 2024 β Jul 2025Research AssistantStanford University, with Susan Athey
- Aug 2023 β Apr 2024Research AssistantJohns Hopkins University, with Anjalie Field
- Nov 2022 β Aug 2023Research AssistantUniversity of Maryland, with Giovanni Ciampaglia and Ernesto Calvo
- May 2022 β Sep 2022Data Science InternEMD Electronics
Education
- Aug 2025 β presentMS, Computer ScienceGeorgetown University
- Sep 2020 β May 2023BS, Information ScienceUniversity of Maryland, College Park
Honors and awards
- Commencement Speaker, UMD Information Science, 2023
- Invited Panel Speaker, UMD Privacy of Information Workshop, 2023
- NET+ Fellowship, 2022β2023
- Presidential Fellowship, 2017β2021
Skills
- Python
- R
- SQL
- Java
- C / C++
- Scala
- Spark and PySpark
- TensorFlow
- scikit-learn
- AWS
- React and Svelte
- Tableau
Languages
- English (native)
- Farsi (native)
Publications
My name is in purple, and * marks equal contribution. The most up-to-date list is on Google Scholar.
-
2026
The Limits of Simulated Societies: How LLM Alignment Erases Cross-Cultural Variance AACL
-
2026
Segmentation Strategy Matters: Benchmarking Whisper on Persian YouTube Content EACL 2026, Silk Road Workshop
-
2026
AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments npj Digital Medicine
-
2026
The Electoral Business Cycle of Wikipedia: What Elections Reveal About Partisanship, the Publicβs Attention, and the Production of Political Knowledge Under review at EPJ Data Science
-
2025
Addressing cognitive bias in medical language models npj Digital Medicine
-
2024
Traineesβ perspectives and recommendations for catalyzing the next generation of NeuroAI researchers Nature Communications
-
2024
Brain-inspired learning in artificial neural networks APL Machine Learning
-
2023
Medical language models are susceptible to incorrect patient self-diagnosis NeurIPS 2023, Deep Generative Models for Health Workshop
-
2023
Meta-Nanomaterials Aspects for Photocatalysis as Potential for the Inactivation of COVID-19 Virus Catalysts
Research
A few threads that run through my work.
Social simulation for public health
Predicting population health behaviour with LLM-powered social simulations, alongside NLP and machine learning on EHR and long-form narrative data.
AI in medicine
Evaluating medical language models: cognitive and implicit bias, susceptibility to patient self-diagnosis, and agent benchmarks like AgentClinic.
Language and culture
How LLM alignment flattens cross-cultural differences, plus speech recognition and NLP for Persian and other low-resource languages.
Causal inference and fairness
Using generative models and causal methods to study bias in hiring and in micro-loan decisions.
Computational social science
Modeling how elections and political events shape public attention and knowledge production on Wikipedia.
Say hello
I'm always happy to chat about research, collaborations, or anything at the crossroads of AI and health.