About

I’m an Assistant Professor in Computer Science & Engineering at the University of Minnesota. I’m also currently a Senior Visiting Faculty Researcher at Google Research. I work with other HCI faculty in the GroupLens lab. Our group builds and develops human-centered AI (HCAI) for mental health and well-being in online interactions. HCAI (or human-centered ML) deliberately balances CS contributions with the needs and values of humans, communities, and stakeholders. This area is rich with use-inspired problems that lead us to improved AI infrastructure for user behavior modeling/prediction, such as measurement, multimodal feature engineering, and developing accurate and robust models with small datasets. However, I also think (and do) a lot about how peoples’ preferences and concerns about AI can inform this design process and intervention strategies. Finally, I critically evaluate these AI systems to develop more ethical research practices and policy for ML and computer science. My domai

Research topics

• Computer Science
• Sociology
• Social Science
• Psychology
• Computer Security
• Political Science
• Psychiatry
• World Wide Web
• Applied psychology
• Social psychology
• Data science
• Internet privacy

Selected publications

• Beyond Content Exposure: Systemic Factors Driving Moderators' Mental Health Crisis in Africa

  arXiv (Cornell University) · 2026-03-03

  preprintOpen accessSenior author

  Content moderators review disturbing content to protect social media users, often at significant cost to their mental health. Recent reports document the mental health conditions of African moderators as notably problematic. Beyond the content itself, what factors contribute to the deteriorating mental health of these workers? We surveyed 134 moderators across Africa to understand their mental health and interviewed 15 moderators to contextualize their experiences. We found that African moderators suffer from high psychological distress and lower well-being compared to moderators in other areas. Former moderators showed significantly higher distress levels, demonstrating long term impact that extends beyond their moderation work. Our interviews showed that systemic and structural labor conditions contribute to moderators' severe psychological distress and diminished mental well-being. Corporate wellness programs promoted by platforms were found ineffective and inadequate. We discuss how this requires holistic attention and structural solutions by all involved parties to improve moderators' mental health.

  PublisherDOI

• FedMental: Evaluating Federated Learning for Mental Health Detection from Social Media Data

  arXiv (Cornell University) · 2026-05-18

  preprintOpen accessSenior author

  Social media text data are often used to train Machine Learning (ML) models to identify users exhibiting high-risk mental health behaviors. However, sharing this sensitive data poses privacy risks and limits the growth of benchmark datasets. We comprehensively evaluate whether privacy-preserving ML techniques can enable safer data sharing while preserving performance. Specifically, we apply federated learning (FL) and Differentially Private FL for two widely-studied mental health prediction tasks: depression detection on X (Twitter) and suicide crisis detection on Reddit. We simulate realistic data-sharing scenarios by treating each user as a client in a non-IID setting, evaluating across different client fractions, aggregation strategies, and privacy budgets. While FL achieves comparable performance to centralized training (centralized F1 = 85.63; best FL model F1 = 83.16) on depression identification, we find that Differentially Private FL has a large performance-privacy trade-off (up to F1 = 27.01 drop) even with low levels of noise (epsilon = 50). This is due to the distortion of highly informative yet sparse mental health linguistic markers related to mental health, like health topics and emotion words. This research empirically demonstrates the potential and limitations of current privacy preservation techniques for mental health inference tasks.

  PublisherDOI

• Unraveling Entangled Feeds: Rethinking Social Media Design to Enhance User Well-being

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Social media platforms have rapidly adopted algorithmic curation with little consideration for the potential harm to users' mental well-being. We present findings from design workshops with 21 participants diagnosed with mental illness about their interactions with social media platforms. We find that users develop cause-and-effect explanations, or folk theories, to understand their experiences with algorithmic curation. These folk theories highlight a breakdown in algorithmic design that we explain using the framework of entanglement, a phenomenon where there is a disconnect between users' actions and platform outcomes on an emotional level. Participants' designs to address entanglement and mitigate harms centered on contextualizing their engagement and restoring explicit user control on social media. The conceptualization of entanglement and the resulting design recommendations have implications for social computing and recommender systems research, particularly in evaluating and designing social media platforms that support users' mental well-being.

  PublisherOA PDFDOI

• Unraveling Entangled Feeds: Rethinking Social Media Design to Enhance User Well-being

  arXiv (Cornell University) · 2026-02-17

  preprintOpen accessSenior author

  Social media platforms have rapidly adopted algorithmic curation with little consideration for the potential harm to users' mental well-being. We present findings from design workshops with 21 participants diagnosed with mental illness about their interactions with social media platforms. We find that users develop cause-and-effect explanations, or folk theories, to understand their experiences with algorithmic curation. These folk theories highlight a breakdown in algorithmic design that we explain using the framework of entanglement, a phenomenon where there is a disconnect between users' actions and platform outcomes on an emotional level. Participants' designs to address entanglement and mitigate harms centered on contextualizing their engagement and restoring explicit user control on social media. The conceptualization of entanglement and the resulting design recommendations have implications for social computing and recommender systems research, particularly in evaluating and designing social media platforms that support users' mental well-being.

  PublisherDOI

• AI CHAOS! 2nd Workshop on the Challenges for Human Oversight of AI Systems

  Universität des Saarlandes · 2026-01-01

  otherOpen access
  PublisherDOI

• Characterizing Delusional Spirals through Human-LLM Chat Logs

  ArXiv.org · 2026-03-17

  articleOpen access

  As large language models (LLMs) have proliferated, disturbing anecdotal reports of negative psychological effects, such as delusions, self-harm, and ``AI psychosis,'' have emerged in global media and legal discourse. However, it remains unclear how users and chatbots interact over the course of lengthy delusional ``spirals,'' limiting our ability to understand and mitigate the harm. In our work, we analyze logs of conversations with LLM chatbots from 19 users who report having experienced psychological harms from chatbot use. Many of our participants come from a support group for such chatbot users. We also include chat logs from participants covered by media outlets in widely-distributed stories about chatbot-reinforced delusions. In contrast to prior work that speculates on potential AI harms to mental health, to our knowledge we present the first in-depth study of such high-profile and veridically harmful cases. We develop an inventory of 28 codes and apply it to the $391,562$ messages in the logs. Codes include whether a user demonstrates delusional thinking (15.5% of user messages), a user expresses suicidal thoughts (69 validated user messages), or a chatbot misrepresents itself as sentient (21.2% of chatbot messages). We analyze the co-occurrence of message codes. We find, for example, that messages that declare romantic interest and messages where the chatbot describes itself as sentient occur much more often in longer conversations, suggesting that these topics could promote or result from user over-engagement and that safeguards in these areas may degrade in multi-turn settings. We conclude with concrete recommendations for how policymakers, LLM chatbot developers, and users can use our inventory and conversation analysis tool to understand and mitigate harm from LLM chatbots. Warning: This paper discusses self-harm, trauma, and violence.

  PublisherOA PDF

• Social and Emotional Uses of AI

  2026-04-13

  articleOpen access

  More and more people look to generative AI for social and emotional support — presenting profound interpersonal and societal risks. In this workshop, we invite HCI researchers across the sub-communities of digital safety, digital mental health and well-being, and responsible AI to come together and articulate a shared research agenda for HCI to lead the design, governance, and safeguarding of social and emotional uses of AI. Workshop participants will engage in a series of talks and group discussions focused on defining and addressing foundational, methodological, and translational challenges towards safer AI use.

  PublisherDOI

• Opportunities and Barriers for AI Feedback on Meeting Inclusion in Socioorganizational Teams

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Inclusion is important for meeting effectiveness, which is in turn central to organizational functioning. One way of improving inclusion in meetings is through feedback, but social dynamics make giving feedback difficult. We propose that AI agents can facilitate feedback exchange by being psychologically safer recipients, and we test this through a meeting system with an AI agent feedback mediator. When delivering feedback, the agent uses the Induced Hypocrisy Procedure, a social psychological technique that prompts behavior change by highlighting value-behavior inconsistencies. In a within-subjects lab study (n = 28), the agent made speaking times more balanced and improved meeting quality. However, a field study at a small consulting firm (n = 10) revealed organizational barriers that led to its use for personal reflection rather than feedback exchange. We contribute a novel sociotechnical system for feedback exchange in groups, and empirical findings demonstrating the importance of considering organizational barriers in designing AI tools for organizations.

  PublisherDOI

• Opportunities and Barriers for AI Feedback on Meeting Inclusion in Socioorganizational Teams

  ArXiv.org · 2026-01-16

  articleOpen accessSenior author

  Inclusion is important for meeting effectiveness, which is in turn central to organizational functioning. One way of improving inclusion in meetings is through feedback, but social dynamics make giving feedback difficult. We propose that AI agents can facilitate feedback exchange by being psychologically safer recipients, and we test this through a meeting system with an AI agent feedback mediator. When delivering feedback, the agent uses the Induced Hypocrisy Procedure, a social psychological technique that prompts behavior change by highlighting value-behavior inconsistencies. In a within-subjects lab study ($n=28$), the agent made speaking times more balanced and improved meeting quality. However, a field study at a small consulting firm ($n=10$) revealed organizational barriers that led to its use for personal reflection rather than feedback exchange. We contribute a novel sociotechnical system for feedback exchange in groups, and empirical findings demonstrating the importance of considering organizational barriers in designing AI tools for organizations.

  PublisherOA PDF

• Large Language Models for Mental Health: A Multilingual Evaluation

  Open MIND · 2026-02-02

  preprint

  Large Language Models (LLMs) have remarkable capabilities across NLP tasks. However, their performance in multilingual contexts, especially within the mental health domain, has not been thoroughly explored. In this paper, we evaluate proprietary and open-source LLMs on eight mental health datasets in various languages, as well as their machine-translated (MT) counterparts. We compare LLM performance in zero-shot, few-shot, and fine-tuned settings against conventional NLP baselines that do not employ LLMs. In addition, we assess translation quality across language families and typologies to understand its influence on LLM performance. Proprietary LLMs and fine-tuned open-source LLMs achieve competitive F1 scores on several datasets, often surpassing state-of-the-art results. However, performance on MT data is generally lower, and the extent of this decline varies by language and typology. This variation highlights both the strengths of LLMs in handling mental health tasks in languages other than English and their limitations when translation quality introduces structural or lexical mismatches.

  DOI

Frequent coauthors

• Munmun De Choudhury

  24 shared

• Hanlin Li

  The University of Texas at Austin

  12 shared

• Brent Hecht

  Northwestern University

  10 shared

• Steven A. Sumner

  National Center for Injury Prevention and Control

  8 shared

• Eshwar Chandrasekharan

  6 shared

• Loren Terveen

  University of Minnesota

  6 shared

• Nicholas Vincent

  University of East Anglia

  5 shared

• Joseph A. Konstan

  University of Minnesota

  4 shared

Labs

• GroupLensPI

  Not provided

Education

• Ph.D., Human Centered Computing

  Georgia Tech

• Other

  Northwestern University

Awards & honors

• National Science Foundation (NSF)
• Northwestern’s Center for Advancing Safety of Machine Intell…
• Cisco Research
• Google
• Amazon Research Award