Publications

Journal Papers

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

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. [PDF]

Time-varying functional connectivity as Wishart processes

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. [PDF]

Properties of V1 and MT motion tuning emerge from unsupervised predictive learning

Journal of Vision, 2022

Unsupervised predictive learning creates neural-like single-unit tuning, population tuning statistics, and integration of locally-ambiguous motion signals, and provides an interrogable model of why motion computations take the form they do.

Storrs et al. (2022). "Properties of V1 and MT motion tuning emerge from unsupervised predictive learning." Journal of Vision. [PDF]

Conference Papers

Mind the Gap: Aligning knowledge bases with user needs to enhance mental health retrieval

NeurIPS GenAI4Health Workshop, 2025

This paper introduces a gap-informed framework for expanding mental health knowledge bases, using naturalistic data like forum posts to identify underrepresented topics and prioritize which content to add. In a case study across four RAG pipelines, this directed approach reached ~95% of an exhaustive reference corpus’s performance with expansions as small as 42%, compared to the 232–763% required by random augmentation.

Chan et al. (2025). "Mind the Gap: Aligning Knowledge Bases with User Needs to Enhance Mental Health Retrieval." NeurIPS GenAI4Health Workshop. [PDF]

Conversational self-play for discovering and understanding psychotherapy approaches

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. [PDF]

Crowdsource, crawl, or generate? Creating SEA-VL, a multicultural vision-language dataset for Southeast Asia

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. [PDF]

SEACrowd: A multilingual multimodal data hub and benchmark suite for Southeast Asian languages

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. [PDF]

Modeling the machine learning multiverse

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. [PDF]

Perspectives on machine learning from psychology’s reproducibility crisis

ICLR Workshop of Science and Engineering of Deep Learning, 2021

This short paper presents select ideas from psychology’s reformation, translating them into relevance for a machine learning audience.

Bell and Kampman (2021). "Perspectives on Machine Learning from Psychology's Reproducibility Crisis." ICLR Workshop of Science and Engineering of Deep Learning. [PDF]

Variational inference for neural network matrix factorization and its application to stochastic blockmodeling

ICML Workshop on Learning and Reasoning with Graph-Structured Data, 2019

Neural network matrix factorization with variational inference.

Kampman and Heaukulani. (2019). "Variational inference for neural network matrix factorization and its application to stochastic blockmodeling." ICML Workshop on Learning and Reasoning with Graph-Structured Data. [PDF]

Adapting a Virtual Agent to User Personality

International Workshop on Spoken Dialogue Systems Technology, 2018

The study suggests that especially the Openness user personality trait correlates with a stronger preference for agents with more gentle personality, and people also sense more empathy and enjoy better conversations when agents adapt their personalities.

Kampman et al. (2018). "Adapting a Virtual Agent to User Personality." International Workshop on Spoken Dialogue Systems Technology. [PDF]

Investigating audio, video, and text fusion methods for end-to-end automatic personality prediction

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. [PDF]

Attention-based LSTM for psychological stress detection from spoken language using distant supervision

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. [PDF]

Nora the empathetic psychologist

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. [PDF]

Preprints & Manuscripts

Language-centered mental healthcare with large language models: A comprehensive survey across the care continuum

Preprint, 2026

Survey paper on the use of LLMs in mental healthcare.

Wu et al. (2026). "Language-centered mental healthcare with large language models: A comprehensive survey across the care continuum." Preprint. [PDF]

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, 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. [PDF]

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

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. [PDF]

A multi-agent dual dialogue system to support mental health care providers

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. [PDF]

Towards universal end-to-end affect recognition from multilingual speech by ConvNets

arXiv, 2019

We propose an end-to-end affect recognition approach using a Convolutional Neural Network (CNN) that handles multiple languages, with applications to emotion and personality recognition from speech.

Bertero et al. (2019). "Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets." arXiv. [PDF]

Media & Perspectives

Mind the Language Gap: Building an Inclusive AI Future for Southeast Asia

Tech For Good Institute, 2025

Perspective piece arguing that closing the linguistic gap in Southeast Asian AI systems — through initiatives like SEACrowd — is essential for equitable digital inclusion and for preventing harmful misinterpretations in critical sectors like healthcare.

Kampman & Lovenia (2025). "Mind the Language Gap: Building an Inclusive AI Future for Southeast Asia." Tech For Good Institute. [Read]