TL;DR
I am a PhD student in Organizational Behavior at the Stanford Graduate School of Business, broadly studying personality, culture, values, and cognition within the space of AI for Social Science. I am also a member of UChicago Knowledge Lab and the Stanford–Berkeley Computational Culture Lab.
👤 About Me
I am intrigued by big questions about humans, society, and AI:
- Instead of having AI mimic or replace humans, how can we use it to facilitate research and augment human capabilities?
- How do AI systems fundamentally differ from human intelligence, and what new theories or frameworks can we develop to study their unique characteristics?
- Given that large language models represent a compressed form of collective human knowledge, what new insights about human behavior and society can we uncover by studying their internal representations?
At Stanford, I am (1) developing a new theoretical framework to understand and conceptualize AI’s values and (2) investigating AI’s cognition and representations through a social science lens.
Previously, I completed my M.A. in Computational Social Science at The University of Chicago, advised by Dr. James Evans, where we explored ways to use Generative Agent-Based Modelling to simulate psychological and social phenomena. Together, we explored how Generative Agent-Based Modelling can be used to simulate psychological and social phenomena. Dr. Evans has profoundly influenced me in many dimensions: he taught me to pursue bold ideas with confidence, support others generously, and carry a strong, decisive energy into everything I do. I also learned the “gives a week, takes a week; gives a minute, takes a minute” philosophy from him, which continues to guide how I approach research and life.
Before that, I earned my B.A. in Computer Science and Psychology (Honours) from The University of British Columbia, where I was fortunate to be mentored by Dr. Victoria Savalei, Dr. Kristin Laurin, and Dr. Christina Conati. Their patience, rigor, and unwavering support helped me find my footing in research and build a strong foundation for interdisciplinary work. At UBC, I explored topics from psychometric modeling of personality to socioeconomic cognition and explainable AI—experiences that continue to shape my curiosity and approach to scientific inquiry.
My research has since expanded through collaborations at Microsoft Research Asia with Dr. Xiaoyuan Yi and Dr. Xing Xie, where we examine AI alignment problems.
Across all my recent projects, I aim to build a computational psychology of AI: a framework that treats intelligent systems as evolving and active social participants that reflect and reshape human society.
📝 Publications
Journal and Conference Proceedings
- Huang, M., & Evans, J. (In Press). Institutions as cached computation for resource-rational negotiation. Behavioral and Brain Sciences. https://doi.org/10.1017/S0140525X2510174X
- Huang, M., Zhang, X., Soto, C., & Evans, J. (Under Review). Designing AI-Agents with Personalities: A Psychometric Approach. Personality Science. [Preprint]
- Bai, Y., Duan, S., Huang, M., Yao, J., Liu, Z., Zhang, P., … & Xie, X. (Under Review). IROTE: Human-like Traits Elicitation of Large Language Model via In-Context Self-Reflective Optimization. [Preprint]
- Zhang, H., Huang, M., & Wang, J. (Under Review). Evolving Collective Cognition in Human-Agent Hybrid Societies: How Agents Form Stances and Boundaries. [Preprint]
- Zhang, X., Huang, M., Sun, J., & Savalei, V. (2025). Improving the Measurement of the Big Five via Alternative Formats for the BFI-2. Journal of Personality Assessment. https://doi.org/10.1080/00223891.2025.2531187
- Yao, J., Yi, X., Duan, S., Wang, J., Bai, Y., Huang, M., … & Xie, X. (2025). Value compass benchmarks: A comprehensive, generative and self-evolving platform for llms’ value evaluation. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.acl-demo.64
- Kim, H., Yi, X., Bak, J., Yao, J., Lian, J., Huang, M., Duan, S., & Xie, X. (2025). The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment. SuperIntelligence - Robotics - Safety & Alignment, 2(1). https://doi.org/10.70777/si.v2i1.13963
- Savalei, V., & Huang, M. (2025). Fit Indices Are Insensitive to Multiple Minor Violations of Perfect Simple Structure in Confirmatory Factor Analysis. Psychological Methods. https://dx.doi.org/10.1037/met0000718
- Laurin, K., Engstrom, H., & Huang, M. (2024). What will my life be like when I’m 25? How social class predicts kids’ answers to this question, and how their answers predict their futures. Journal of Social Issues. https://doi.org/10.1111/josi.12650
Conference Presentations
- Huang, M., Biedma, P., Yi, X., Huang, L., Sun, M., Evans, J., Xie, X. (2025, July). Beyond Anthropomorphism: Unveiling Unique Value Structure of Large Language Models (LLMs) via a Psychometric Approach. 11th International Conference on Computational Social Science (IC2S2), Norrköping, Sweden.
- Huang, M. & Huang, N. (2025, August). Re-Discovering the Big Five: Using LLM Embeddings to Understand Personality Structure. American Psychological Association (APA) Convention, Denver, USA.
- Huang, M. (2024, August). Designing LLM-Agents with Personalities: A Psychometric Approach [Poster]. American Psychological Association (APA) Convention, Seattle, USA.
- Zhang, X., & Huang, M. (2025, June). Designing AI-agents with personalities using BFI-2 in different formats.
- Zhang, X., Huang, M., Sun, J., & Savalei, V. (2025, July). Designing AI-agents with personalities using BFI-2 in different formats.
- Huang, M., Zhang, X., & Savalei, V. (2023, August). How to measure Big Five personality traits better? Exploring alternative scale formats for BFI-2 [Poster]. American Psychological Association (APA) Convention, Washington, DC.
- Savalei, V., & Huang, M. (2023, October). Can SEM Fit Indices Distinguish Between CFA and EFA Data Structures? [Paper]. Annual Meeting of the Society for Multivariate Experimental Psychology (SMEP), Iowa City, Iowa.
- Engstrom, H., Huang, M., & Laurin, K. (2022, July). Social class differences in children’s hopes and expectations for the future: A machine learning approach [Talk]. Society in the classroom: Integrating perspectives on how socioeconomic disparities unfold in educational settings, London, England.
- Conati, C., Murali, R., & Huang, M. (2022, June). Gauging Student Engagement with an XAI interface via Eye-tracking [Paper]. IJCAI 2022 Workshop on Explainable Artificial Intelligence, 168.
- Engstrom, H., Huang, M., & Laurin, K. (2022, February). Different social class backgrounds, different imagined futures [Talk]. 2022 Society for Personality and Social Psychology (SPSP) Annual Convention, San Francisco, CA.
- Huang, M., Engstrom, H., & Laurin, K. (2021, May). Exploring the Association between Socioeconomic Status and Children’s Imagined Future: A Study Using Machine Learning [Poster]. Association for Psychological Science (ASP) Annual Convention.