IEEE Transactions on Affective Computing, 2026
Survey paper on the use of LLMs in mental healthcare.
Wu et al. (2026). "Affective language processing for mental healthcare: A survey of LLM-based approaches across the care continuum." IEEE Transactions on Affective Computing.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
ACL, 2017
The enhanced personality module with improved recognition from speech and text using deep learning frameworks is described, which improves personality recognition results with a CNN model on top of pre-trained word embeddings.
Siddique et al. (2017). "Zara Returns: Improved personality induction and adaptation by an empathetic virtual agent." ACL.
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.
arXiv, 2026
VEILED benchmark description manuscript.
Tan et al. (2026). "VEILED: Vulnerability expression through indirect language evaluation dataset." arXiv.
JMIR Preprints, 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.
OSF, 2026
Analyzing 1,710 sessions from a Singapore youth chatline, this study found that suicide-related chats (24% of sessions) were longer, more message-intensive, and produced lower user-reported outcomes than non-suicidal ones. Normalization showed the strongest association with positive outcomes in suicidal sessions, followed by psychoeducation and making strengths explicit—a pattern distinct from non-suicidal sessions, offering concrete guidance for training frontline workers.
Chung, Lim, and Kampman. (2026). "Worker Skills Associated With Outcomes In Suicidal-Related Youth Chat Sessions." OSF.
arXiv, 2025
Overall, it is demonstrated that DEHP campaigns can achieve national engagement for mental health awareness through multi-channel distribution and creative, narrative-driven designs.
Yan et al. (2025). "Evaluation of A National Digitally-Enabled Health Promotion Campaign for Mental Health Awareness using Social Media Platforms Tik Tok, Facebook, Instagram, and YouTube." arXiv.
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.
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.
GitHub, 2026
Python package for estimating static and time-varying functional connectivity (TVFC), including a Wishart process implementation alongside DCC-MGARCH and sliding-window baselines.
Kampman et al. (2024). "Time-varying functional connectivity as Wishart processes." Imaging Neuroscience.