Cognitive and mental health (CMH) disorders are increasingly prevalent worldwide and pose significant societal, clinical, and economic challenges. While conventional mental health support methods remain limited by scalability and reactivity, recent advances in artificial intelligence have opened new opportunities for scalable, proactive, and personalized mental health support. Rapid progress has been made in areas such as mental health assessment, empathetic conversational agents, and AI-assisted psychological interventions; however, these efforts remain fragmented across disciplines, and critical challenges related to reliability, interpretability, ethics, and real-world deployment persist. To address these gaps, we propose the International Workshop on AI for Cognitive and Mental Health Support (AI4Mental), a half-day interdisciplinary forum that brings together researchers and practitioners from data mining, machine learning, NLP, HCI, healthcare, and social sciences. The workshop focuses on three complementary pillars: AI as Assessment, AI as Emotional Support, and AI as Psychological Intervention, covering topics ranging from multimodal mental health detection and longitudinal risk modeling to empathetic dialogue systems and responsible interventions. By consolidating emerging research and fostering cross-disciplinary dialogue, AI4Mental aims to advance trustworthy, effective, and socially responsible AI solutions for cognitive and mental health support, aligning closely with SIGKDD's mission on data science for social good.
Xiang-jian Wang, Haoyang Li, Chen Li et al.· Proceedings of the 32nd ACM...· 0 citations
Generative models are emerging as a key technology for accelerating molecular discovery in drug design, materials science, and catalysis by enabling efficient exploration of the vast chemical space of possible molecules. Recent advances in deep generative modeling—including variational autoencoders (VAEs), diffusion models, flow matching methods, and autoregressive transformer-based approaches—have produced a diverse toolkit for generating molecular structures and optimizing their properties. However, these paradigms are often studied independently, leaving many machine learning researchers without a clear understanding of their connections, strengths, and limitations in molecular applications. This tutorial provides a unified introduction to modern generative modeling approaches for molecular generation, covering their theoretical foundations, algorithmic design, and practical considerations for molecular representations such as 1D SMILES strings, 2D molecular graphs, and 3D structures. While the tutorial primarily focuses on generative models for de novo molecular design, we also briefly discuss how similar modeling paradigms extend to reaction prediction and retrosynthesis. By presenting these models within a common framework, the tutorial aims to equip ML researchers and AI-for-science practitioners with a clear conceptual map of the generative modeling landscape for molecular discovery and identify emerging research opportunities in this rapidly evolving area.
Kehan Guo, Yili Shen, Jeeyhun Hwang et al.· Proceedings of the 32nd ACM...· 0 citations