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My research lies at the intersection of computational social science, human-centered AI, and digital mental health. I develop computational and machine learning methods to understand how online social systems shape mental health, well-being, and risk, with a focus on youth and other under-studied populations. A core emphasis of my work is leveraging large-scale digital trace data from social media, search, and emerging online technologies to identify early markers of mental health challenges, model trajectories of risk and recovery, and inform timely, ethical interventions. Increasingly, my research examines the opportunities and harms of generative AI and large language models in mental health contexts, including issues of reliability, alignment with clinical and lived experience expertise, and sociotechnical failure modes. Across projects, I integrate causal inference, participatory design, and safety frameworks to ensure that AI systems for mental health are grounded in real-world care delivery, policy, and governance needs.
My teaching interests center on social computing, human-centered artificial intelligence, and the societal impacts of data-driven systems, with particular emphasis on mental health, well-being, and online harm. I aim to equip students with both conceptual frameworks and methodological rigor to critically analyze and design sociotechnical systems that shape human behavior at scale. In the classroom, I emphasize theory-informed, data-driven reasoning; ethical reflection; and engagement with real-world cases drawn from social media, health, and policy contexts. My teaching is closely integrated with mentorship, where I prioritize scaffolded research training, adaptive advising, and intellectual independence across undergraduate, master’s, and doctoral students. I actively mentor students in interdisciplinary research, helping them develop skills in computational methods, interpretive and critical inquiry, and responsible AI practice, while supporting their professional development, leadership, and sense of belonging in the field.
De Choudhury, M., Gamon, M., Counts, S., and Horvitz, E. (2013). Predicting Depression via Social Media. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (Boston, MA, Jul 8-Jul 10, 2013). ICWSM 2013.
De Choudhury, M., and De, S. (2014). Mental Health Discourse on Reddit: Self-disclosure, Social Support, and Anonymity. In Proceedings of the 8th International AAAI Conference on Weblogs and Social Media (Ann Arbor, MI, Jun 2-Jun 4, 2014). ICWSM 2014.
De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., and Kumar, M. (2016). Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (San Jose, CA, May 7-12, 2016). CHI 2016.
Chancellor, S., and De Choudhury, M. (2020). Methods in Predictive Techniques for Mental Health Status on Social Media: A Critical Review. In Nature Partner Journal - Digital Medicine. npj Digital Medicine.
Chancellor, S., Pater, J., Clear, T., Gilbert, E., and De Choudhury, M. (2016). #thyghgapp: Instagram Content Moderation and Lexical Variation in Pro-Eating Disorder Communities. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (San Francisco, CA, Feb 27–Mar 2, 2016). CSCW 2016.
Chancellor, S., Birnbaum, M. L., Caine, E., Silenzio, V., and De Choudhury, M. (2019). A Taxonomy of Ethical Tensions in Inferring Mental Health States from Social Media. In Proceedings of the 2nd ACM Conference on Fairness, Accountability, and Transparency (Atlanta GA, January 29-31, 2019), forthcoming. FAT* 2019.
Koutsouleris, N.*, Hauser, T. U., Skvortsova, V., and De Choudhury, M.* (2022). From promise to practice: towards the realisation of AI-informed mental health care. The Lancet Digital Health. * Authors contributed equally
Ernala, S. K., Birnbaum, M. L., Candan, K., Rizvi, A., Sterling, W. A., Kane, J. M., and De Choudhury, M. (2019). Methodological Gaps in Predicting Mental Health States from Social Media: Triangulating Diagnostic Signals. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland, May 4-9, 2019). CHI 2019.
Malgaroli, M., Schultebraucks, K., Myrick, K.J., Loch, A.A., Ospina-Pinillos, L., Choudhury, T., Kotov, R., De Choudhury, M. and Torous, J. (2025). Large language models for the mental health community: framework for translating code to care. The Lancet Digital Health.
Yoo, D. W., Woo, H., Pendse, S., Lu, N., Birnbaum, M., Abowd, G., and De Choudhury, M. (2024). Missed Opportunities for Human-Centered AI Research: Understanding Stakeholder Collaboration in Mental Health AI Research. In Proceedings of the ACM on Human-Computer Interaction, CSCW. PACM-HCI 2024.
Nguyen, V.C., Taher, M., Hong, D., Possobom, V.K., Gopalakrishnan, V.T., Raj, E., Li, Z., Soled, H.J., Birnbaum, M.L., Kumar, S. and De Choudhury, M. (2025). Do Large Language Models Align with Core Mental Health Counseling Competencies? In Proceedings of NAACL 2025.
Kim, S., Razi, A., Alsoubai, A., Wisniewski, P., and De Choudhury, M. (2024). Assessing the Impact of Online Harassment on Youth Mental Health in Private Networked Spaces. In Proceedings of the 17th International AAAI Conference on Web and Social Media. ICWSM 2024.
Zhou, J., Chen, A., Shah, D., Schwab Reese, L., and De Choudhury, M.. (2025). A Risk Taxonomy and Reflection Tool for LLM Adoption in Public Health. In Proceedings of the ACM on Human-Computer Interaction, CSCW. PACM-HCI 2025.
Saha, K., Sugar, B., Torous, J., Abrahao, B., Kiciman, E., and De Choudhury, M. (2019). A Social Media Study on The Effects of Psychiatric Medication Use. In Proceedings of the 13th International AAAI Conference on Web and Social Media (Munich, Germany, June 11-14, 2019). ICWSM 2019.
Das Swain, V., Gao, L., Wood, W., Matli, S., Abowd, G. and De Choudhury, M.. (2023). Algorithmic Power or Punishment: Information Worker Perspectives on Passive Sensing Enabled AI Phenotyping of Performance and Wellbeing. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. CHI 2023.
Pendse, S. R., Stapleton, L., Kumar, N., De Choudhury, M., and Chancellor, S. (2024). Advancing a consent-forward paradigm for digital mental health data. Nature Mental Health, 1-10.