Publications

You can also find my articles on my Google Scholar profile or on my Semantic Scholar profile.

The let’s talk Digital Peer Support Forum for Youth Mental Health and Wellbeing in Singapore: A Three-Year Process Evaluation and Framework Description

Published in JMIR Mental Health Preprint, 2026

let’s talk is a Singaporean mental health forum. This article describes it Theory of Change and discusses a process evaluation of its first three years.

Weng et al. (2026). "The let’s talk Digital Peer Support Forum for Youth Mental Health and Wellbeing in Singapore: A Three-Year Process Evaluation and Framework Description." JMIR Mental Health Preprint. https://preprints.jmir.org/preprint/93122?__hstc=102212634.14f593a7c963c881e74404adec922d27.1769221425735.1769221425735.1771072738575.2&__hssc=102212634.1.1771072738575&__hsfp=f734356bbadfb3939a292bb8ea08f78c&_gl=1*ke4azx*_gcl_au*MjEyNjQ3Njk4My4xNzY5MjIxNDI1*_ga*MjA3MjU2NTMxNS4xNzY5MjIxNDI1*_ga_YP0XNYBWWC*czE3NzEwNzI3MzgkbzIkZzAkdDE3NzEwNzI3MzgkajYwJGwwJGgw

Worker skills associated with outcomes in suicidal-related youth chat sessions

Published in OSF, 2026

Introduction: Text-based chatlines have become preferred entry points for youth seeking mental health support, yet most research examines dedicated crisis services rather than general chatlines where suicide emerges alongside diverse concerns. This study compared suicidal-related and non-suicidal sessions within a general youth chatline to identify session characteristics and worker skills associated with positive outcomes.Methods: We analyzed 1,710 chat sessions (202,336 messages) from QuickChat, a Singapore youth chatline between 2016 and 2020. Large language models classified sessions as suicidal or self-harm related(n=406, 24%) or non-suicidal (n=1,304, 76%).User-reported outcomes measured service quality and coping ability. Twelve therapeutic skills were coded from 79,587 worker messages. Multilevel regression models examined skill-outcome associations.Results: Suicidal-related sessions were significantly longer, contained more messages, and yielded lower outcomes. Suicidal ideation was most prevalent (85%), followed by self-harm (43%). In suicidal sessions, normalization demonstrated the strongest associations with all outcomes, followed by teaching/psychoeducation and making strengths explicit. These patterns differed substantially from non-suicidal sessions.Conclusion: Suicidal-related sessions within general chatlines demand greater engagement and differentiated responses from workers. Normalization and psychoeducation emerge as effective techniques for improving outcomes in suicidal chats. These findings provide actionable guidance for training frontline workers in general youth services.

Chung, Lim, and Kampman. (2026). "Worker Skills Associated With Outcomes In Suicidal-Related Youth Chat Sessions." OSF. https://osf.io/preprints/socarxiv/qaet6_v1

Mind the Gap: Aligning Knowledge Bases with User Needs to Enhance Mental Health Retrieval

Published in NeurIPS GenAI4Health Workshop, 2025

Access to reliable mental health information is vital for early help-seeking, yet expanding knowledge bases is resource-intensive and often misaligned with user needs. This results in poor performance of retrieval systems when presented concerns are not covered or expressed in informal or contextualized language. We present an AI-based gap-informed framework for corpus augmentation that authentically identifies underrepresented topics (gaps) by overlaying naturalistic user data such as forum posts in order to prioritize expansions based on coverage and usefulness. In a case study, we compare Directed (gap-informed augmentations) with Non-Directed augmentation (random additions), evaluating the relevance and usefulness of retrieved information across four retrieval-augmented generation (RAG) pipelines. Directed augmentation achieved near-optimal performance with modest expansions–requiring only a 42% increase for Query Transformation, 74% for Reranking and Hierarchical, and 318% for Baseline–to reach ~95% of the performance of an exhaustive reference corpus. In contrast, Non-Directed augmentation required substantially larger and thus practically infeasible expansions to achieve comparable performance (232%, 318%, 403%, and 763%, respectively). These results show that strategically targeted corpus growth can reduce content creation demands while sustaining high retrieval and provision quality, offering a scalable approach for building trusted health information repositories and supporting generative AI applications in high-stakes domains.

Chan et al. (2025). "Mind the Gap: Aligning Knowledge Bases with User Needs to Enhance Mental Health Retrieval." NeurIPS GenAI4Health Workshop. https://arxiv.org/abs/2509.13626

Harnessing digital phenotyping to advance university student mental health (Brightline) in Singapore: study protocol for a prospective observational study

Published in BMJ Open, 2025

This study will employ an observational study design over a 6-month period, recruiting 500 students from a major public university in Singapore, to identify the digital biomarkers associated with depression, anxiety, stress, loneliness and affect among university students.

Ito et al. (2025). "Harnessing digital phenotyping to advance university student mental health (Brightline) in Singapore: study protocol for a prospective observational study." BMJ Open. https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2025-103652

Conversational Self-Play for Discovering and Understanding Psychotherapy Approaches

Published in AI4X Conference, 2025

This paper explores conversational self-play with LLMs as a scalable approach for analyzing and exploring psychotherapy approaches, evaluating how well AI-generated therapeutic dialogues align with established modalities.

Kampman, Onno P. (2025). "Conversational Self-Play for Discovering and Understanding Psychotherapy Approaches." AI4X Conference. https://www.semanticscholar.org/paper/Conversational-Self-Play-for-Discovering-and-Kampman-Xing/5cfc7ea13348b11fb52bed98dd431b8c1809f4b6

Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia

Published in ACL, 2025

SEA-VL, an open-source initiative dedicated to developing high-quality, culturally relevant data for SEA languages, aims to bridge the representation gap in SEA, fostering the development of more inclusive AI systems that authentically represent diverse cultures across SEA.

Cahyawijaya et al. (2025). "Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia." ACL. https://www.semanticscholar.org/paper/Crowdsource%2C-Crawl%2C-or-Generate-Creating-SEA-VL%2C-a-Cahyawijaya-Lovenia/7a9256ca9fc67513e7c9b40d2d47dbd3617a02f8

A Multi-Agent Dual Dialogue System to Support Mental Health Care Providers

Published in arXiv, 2024

A general-purpose, human-in-the-loop dual dialogue system to support mental health care professionals and found that the proposed responses matched a reasonable human quality in demonstrating empathy, showing its appropriateness for augmenting the work of mental health care providers.

Kampman et al. (2024). "A Multi-Agent Dual Dialogue System to Support Mental Health Care Providers." arXiv. https://arxiv.org/pdf/2411.18429

SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages

Published in EMNLP, 2024

This work introduces SEACrowd, a comprehensive resource center that fills the resource gap by providing standardized corpora in nearly 1,000 SEA languages across three modalities, and assesses the quality of AI models on 36 indigenous languages across 13 tasks.

Lovenia et al. (2024). "SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages." EMNLP. https://aclanthology.org/2024.emnlp-main.296.pdf

Time-varying functional connectivity as Wishart processes

Published in Imaging Neuroscience, 2024

The WP outperformed a sliding window approach with adaptive cross-validated window lengths and a dynamic conditional correlation-multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) baseline on the external stimulus prediction task, while being less prone to false positives in the TVFC null models.

Kampman et al. (2024). "Time-varying functional connectivity as Wishart processes." Imaging Neuroscience. https://direct.mit.edu/imag/article/doi/10.1162/imag_a_00184/121101/Time-varying-functional-connectivity-as-Wishart

Modeling the machine learning multiverse

Published in NeurIPS, 2022

This work model the multiverse with a Gaussian Process surrogate and apply Bayesian experimental design to efficiently explore high-dimensional and often continuous ML search spaces, and synthesize conflicting research on the effect of learning rate on the large batch training generalization gap.

Bell et al. (2022). "Modeling the Machine Learning Multiverse." NeurIPS. https://www.semanticscholar.org/paper/Modeling-the-Machine-Learning-Multiverse-Bell-Kampman/eefa6192e5d515ff9f9d0f185cfded82fdeb85f1

Investigating Audio, Video, and Text Fusion Methods for End-to-End Automatic Personality Prediction

Published in ACL, 2018

It is shown that a multimodal fusion approach outperforms each single modality channel, with an improvement of 9.4% over the best individual modality (video).

Kampman et al. (2018). "Investigating Audio, Video, and Text Fusion Methods for End-to-End Automatic Personality Prediction." ACL. https://www.semanticscholar.org/paper/Investigating-Audio%2C-Video%2C-and-Text-Fusion-Methods-Kampman-Barezi/949cc039d4c5fea8313e2d7d67fbd95a15b18259

Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision

Published in ICASSP, 2018

The bidirectional LSTM model with attention is found to be the best model in terms of accuracy and f-score and it is shown that distant supervision fine-tuning enhances the model’s performance by 1.6% accuracy and 2.1% f-score.

Winata et al. (2018). "Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision." ICASSP. https://www.semanticscholar.org/paper/Nora-the-Empathetic-Psychologist-Winata-Kampman/a770ead81c84ec2f0c845dcc821a3f7763765808

Nora the Empathetic Psychologist

Published in INTERSPEECH, 2017

Nora is a new dialog system that mimics a conversation with a psychologist by screening for stress, anxiety, and depression. She understands, empathizes, and adapts to users using emotional intelligence modules trained via statistical modelling such as Convolutional Neural Networks. These modules also enable her to personalize the content of each conversation.

Winata et al. (2018). "Nora the Empathetic Psychologist." INTERSPEECH. https://www.semanticscholar.org/paper/Nora-the-Empathetic-Psychologist-Winata-Kampman/a770ead81c84ec2f0c845dcc821a3f7763765808