What’s coming up at #AAAI2025?

From Tuesday 25 February to Tuesday 4 March 2025, Philadelphia will play host to the 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025). The event will feature invited talks, tutorials, workshops, and an extensive technical programme. There are also a whole host of other sessions, including a doctoral consortium, diversity and inclusion activities, posters, demos, and more. We (AIhub) will be running a science communication training session on Wednesday 26 February.

Invited talks

There are eight invited talks this year. There will also be a presidential address from current AAAI president Francesca Rossi.
Francesca Rossi – Presidential Address: AI Reasoning and System 2 Thinking
Susan Athey – Predicting Career Transitions and Estimating Wage Disparities Using Foundation Models
Andrew Ng – AI, Agents and Applications
Yuhuai (Tony) Wu – Reasoning at Scale
Christoph Schuhmann – Democratizing AI through Community Organizing
Alondra Nelson – Title to be confirmed
Subbarao Kambhampati – T(w)eaching AI in the Age of LLMs
Stuart J. Russell – Can AI Benefit Humanity?
David Chalmers – Propositional Interpretability in Humans and AI Systems

Science communication for AI researchers – an introduction

We (AIhub) will be running a short course on science communication on Wednesday 26 February. Find out more here.

Tutorial and lab forum

The tutorial and lab forum will be held at the beginning of the conference, on Tuesday 25 and Wednesday 26 February.

• TH01: Bridging Inverse Reinforcement Learning and Large Language Model Alignment: Toward Safe and Human-Centric AI Systems
• TH02: Building trustworthy ML: The role of label quality and availability
• TH03: Fairness in AI/ML via Social Choice
• TH04: Foundation Models meet Embodied Agents
• TH05: Multi-modal Foundation Model for Scientific Discovery: With Applications in Chemistry, Material, and Biology
• TH06: Pre-trained Language Model with Limited Resources
• LH01: DAMAGeR: Deploying Automatic and Manual Approaches to GenAI Red-teaming
• TQ01: Advancing Offline Reinforcement Learning: Essential Theories and Techniques for Algorithm Developers
• TQ02: Unified Semi-Supervised Learning with Foundation Models
• LQ01: SOFAI Lab: A Hands-On Guide to Building Neurosymbolic Systems with Metacognitive Control
• TQ03: Reinforcement Learning with Temporal Logic objectives and constraints
• TH07: Concept-based Interpretable Deep Learning
• TH08: Evaluating Large Language Models: Challenges and Methods
• TH09: Foundation Models for Time Series Analysis: A Tutorial
• TH10: Neurosymbolic AI for EGI: Explainable, Grounded, and Instructable Generations
• TQ04: Deep Representation Learning for Tabular Data
• TQ05: LLMs and Copyright Risks: Benchmarks and Mitigation Approaches
• TQ06: Physics-Inspired Geometric Pretraining for Molecule Representation
• TQ07: From Tensor Factorizations to Circuits (and Back)
• TQ08: KV Cache Compression for Efficient Long Context LLM Inference: Challenges, Trade-Offs, and Opportunities
• TQ09: Supervised Algorithmic Fairness in Distribution Shifts
• LQ03: Developing explainable multimodal AI models with hands-on lab on the life-cycle of rare event prediction in manufacturing
• TH11: (Really) Using Counterfactuals to Explain AI Systems: Fundamentals, Methods, & User Studies for XAI
• TH12: Advancing Brain-Computer Interfaces with Generative AI for Text, Vision, and Beyond
• TH13: AI for Science in the Era of Large Language Models
• TH14: Causal Representation Learning
• TH15: Graph Neural Networks: Architectures, Fundamental Properties and Applications
• TH16: Machine Learning for Protein Design
• TH17: The Lifecycle of Knowledge in Large Language Models: Memorization, Editing, and Beyond
• TH18: Thinking with Functors — Category Theory for A(G)I
• TH19: User-Driven Capability Assessment of Taskable AI Systems
• TQ10: Artificial Intelligence Safety: From Reinforcement Learning to Foundation Models
• TQ11: Hallucinations in Large Multimodal Models
• TQ12: Graph Machine Learning under Distribution Shifts: Adaptation, Generalization and Extension to LLM
• LQ02: Continual Learning on Graphs: Challenges, Solutions, and Opportunities
• TH20: AI Data Transparency: The Past, the Present, and Beyond
• TH21: Data-driven Decision-making in Public Health and its Real-world Applications
• TH22: Decision Intelligence for Two-sided Marketplaces
• TH23: Inferential Machine Learning: Towards Human-collaborative Vision and Language Models
• TH24: Machine Learning for Solvers
• TH25: Model Reuse: Unlocking the Power of Pre-Trained Model Resources
• TH26: Symbolic Regression: Towards Interpretability and Automated Scientific Discovery
• TH27: Tutorial: Multimodal Artificial Intelligence in Healthcare
• TQ13: Curriculum Learning in the Era of Large Language Models
• TQ14: Hypergraph Neural Networks: An In-Depth and Step-by-Step Guide
• TQ15: The Quest for A Science of Language Models
• LQ04: Financial Inclusion through AI-Powered Document Understanding
• TQ16: When Deep Learning Meets Polyhedral Theory: A Tutorial

Find out more about the tutorials and labs here.

Bridge programme

The bridge programme is designed to bring together two or more communities from different AI disciplines to foster collaborations. There are eleven different sessions this year, and these will be held on Tuesday 25 and Wednesday 26 February.

• B1: AI for Medicine and Healthcare
• B2: Bridge between AI and Scientific Knowledge Organization
• B3: Bridging Cognitive Science and AI to Bridge Neuro and Symbolic AI
• B4: Bridging Planning and Reasoning in Natural Language with Foundational Models (PLAN-FM)
• B5: Collaborative AI and Modeling of Humans
• B6: Combining AI and ORMS for Better Trustworthy Decision Making
• B7: Constraint Programming and Machine Learning
• B8: Continual Causality
• B9: Explainable AI, Energy and Critical Infrastructure Systems
• B10: Knowledge-guided Machine Learning: Bridging Scientific Knowledge and AI
• B11: Learning for Integrated Task and Motion Planning

Find out more about the bridge programme here.

Workshops

There are 49 workshops to choose from this year. These will take place at the end of the main conference, on Monday 3 and Tuesday 4 March.

• W1: Translational Institute for Knowledge Axiomatization
• W2: Advancing Artificial Intelligence through Theory of Mind (ToM4AI): Bridging Human Cognition and Artificial Intelligence
• W3: AI for Public Missions
• W4: AI for Social Impact: Bridging Innovations in Finance, Social Media, and Crime Prevention
• W5: AI for Urban Planning
• W6: AI Governance: Alignment, Morality, and Law
• W7: AI to Accelerate Science and Engineering (AI2ASE)
• W8: AI4EDU: AI for Education: Tools, Opportunities, and Risks in the Generative AI Era
• W9: Artificial Intelligence for Cyber Security (AICS)
• W10: Artificial Intelligence for Music
• W11: Cooperative Multi-Agent Systems Decision-Making and Learning: Human-Multi-Agent Cognitive Fusion
• W12: Deployable AI Workshop
• W13: Economics of Modern ML: Markets, Incentives, and Generative AI
• W14: Preparing Good Data for Generative AI: Challenges and Approaches (Good-Data)
• W15: Innovation and Responsibility for AI-Supported Education
• W16: MARW: Multi-Agent AI in the Real-World Workshop
• W17: Planning in The Era of Large Language Models
• W18: Post-Singularity Symbiosis: Preparing for a World with Superintelligence
• W19: Preventing and Detecting LLM Generated Misinformation
• W20: Privacy-Preserving Artificial Intelligence
• W21: Quantum Computing and Artificial Intelligence (QC+AI)
• W22: Web Agent Revolution: Enhancing Trust and Enterprise-Grade Adoption Through Innovation
• W23: Imageomics: Discovering Biological Knowledge from Images Using AI
• W24: Workshop on Datasets and Evaluators of AI Safety
• W25: Workshop on Document Understanding and Intelligence
• W26: Workshop on Multi-Agent Path Finding
• W27:Foundation Models for Biological Discoveries (FMs4Bio)
• W28: Advancing LLM-Based Multi-Agent Collaboration
• W29: AI Agent for Information Retrieval: Generating and Ranking
• W30: AI4Research: Towards a Knowledge-grounded Scientific Research Lifecycle
• W31: Artificial Intelligence for Time Series Analysis (AI4TS): Theory, Algorithms, and Applications
• W32: Artificial Intelligence for Wireless Communications and Networking (AI4WCN)
• W33: Artificial Intelligence with Causal Techniques
• W34: Bridging the Gap Between AI Planning and Reinforcement Learning (PRL)
• W35: CoLoRAI – Connecting Low-Rank Representations in AI
• W36: Computational Jobs Marketplace
• W37: DEFACTIFY 4.0 – Workshop Series on Multimodal Fact-Checking and Hate Speech Detection
• W38: FLUID: Federated Learning for Unbounded and Intelligent Decentralization
• W39: Generalization in Planning
• W40: Workshop and Challenge on Anomaly Detection in Scientific Domains
• W41: Knowledge Graphs for Health Equity, Justice, and Social Services
• W42: Large Language Model and Generative AI for Health
• W43: Machine Learning for Autonomous Driving
• W44: MALTA: Multi-Agent Reinforcement Learning for Transportation Autonomy
• W45: Neural Reasoning and Mathematical Discovery — An Interdisciplinary Two-Way Street
• W46: Open-Source AI for Mainstream Use
• W47: Scalable and Efficient Artificial Intelligence Systems
• W48: Towards Knowledgeable Foundation Models
• W49: Workshop on Health Intelligence (W3PHIAI-25)

Find out more about the workshops here.

Links to other events and sessions

• Main technical track
• Demonstration programme
• Diversity and inclusion activities
• EAAI-25: The 15th Symposium on Educational Advances in Artificial Intelligence
• Programme for the Thirty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-25)
• Doctoral Consortium
• Undergraduate Consortium

Lucy Smith is Senior Managing Editor for AIhub.

Lucy Smith is Senior Managing Editor for AIhub.