Conferences  >  Mathematics  >  Neural Networks and Artificial Intelligence, Machine Learning

Fora Group

Ai4 2025, held at the MGM Grand in Las Vegas, is North America’s largest AI industry conference, gathering over 8,000 attendees, 600+ speakers, & 250+ exhibits. Discover the latest applications in AI, explore best practices shaping the future of AI, and enjoy unbeatable networking. Join us as at the epicenter of the global AI community.

Email: info@ai4.io

Artificial Intelligence

Guizhou University

The 8th International Conference on Pattern Recognition and Artificial Intelligence (PRAI 2025) will be held from August 15 to 17, 2025, in Guiyang, China. Sponsored by Guizhou University, IEEE; hosted by the College of Big Data and Information Engineering of Guizhou University, the conference aims to establish a platform for academic exchange and collaboration among global experts, scholars, and professionals. The conference mainly focuses on themes related to pattern recognition and artificial intelligence. We warmly invite experts, researchers and professional technicians in this field to submit papers and attend the conference. In order to ensure the academic quality of this conference and attract more original high-level academic papers, we now openly call for papers. We welcome scientific researchers, teachers and students engaged in pattern recognition and artificial intelligence research to submit papers. Pattern recognition and artificial intelligence are among the core fields driving technological advancement and industrial transformation today. The rapid development of artificial intelligence, combined with the capabilities of deep learning and big data processing, has significantly enhanced the application of pattern recognition in cutting-edge areas such as image processing, speech recognition, natural language processing, and autonomous driving. These technologies have also been widely adopted in industries like healthcare, intelligent transportation, and financial risk management, bringing profound societal impacts. Furthermore, innovative directions such as generative AI, multimodal learning, model interpretability, and edge computing have further expanded the technological potential and application scenarios of this field.

Email: saraxc17@163.com

Track 1. Robot Perception and Intelligent Interaction Chair: Zhanglin Cheng, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China Visual and sensory perception in robotics; Object recognition and manipulation; Human-robot interaction models; Multi-modal sensing and perception; Cognitive architectures for intelligent robots; Navigation and path planning under uncertainty; Social and emotional interaction models for robots; Applications of AI in service robots; Learning from demonstration and imitation; Ethical considerations in robot interaction. Track 2. Image Analysis and Smart Healthcare Chair: Jinhua Liu, Shangrao Normal University, China Medical image processing and analysis; Computer-aided diagnosis and decision support systems; Radiomics and genomics integration; Image-based biomarker discovery; Automation of medical imaging workflows; AI applications in pathology and histology; Image segmentation and feature extraction techniques; Mobile health technologies for image analysis; Visualization techniques for healthcare data; Ethical issues in medical image interpretation. Track 3. Intelligent Manufacturing and Autonomous Drive Chairs: Binhui Tang, Sichuan University, China Smart manufacturing systems and Industry 4.0 technologies; Predictive maintenance and smart logistics; Autonomous vehicle perception and control; Robotic process automation in manufacturing; Human-centered design in smart factories; Quality control and defect detection using AI; Real-time monitoring and optimization of manufacturing processes; Autonomous navigation and path planning for drones and vehicles; Case studies on successful implementation of AI in manufacturing; Safety and ethical implications of autonomous systems.

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Geometry and Topology,    Optimization Theory, Operational Research

Logics have, for many years, laid claim to providing a formal basis for the study and development of applications and systems in Artificial Intelligence. With the depth and maturity of formalisms, methodologies, and logic-based systems today, this claim is stronger than ever. The European Conference on Logics in Artificial Intelligence (or Journées Européennes sur la Logique en Intelligence Artificielle - JELIA) began back in 1988, as a workshop, in response to the need for a European forum for the discussion of emerging work in this field. Since then, JELIA has been organised biennially, with proceedings published in the Springer-Verlag series Lecture Notes in Artificial Intelligence.

This PhD School is part of the broader CWI Research Semester Programme on Learning Enhanced Optimization, contributing to its overarching mission of advancing cutting-edge research in theoretical computer science, operations research and beyond.

Courses and Events for Math Students and Early Career Researchers,    Optimization Theory, Operational Research

Institute for Computational and Experimental Research in Mathematics, Brown University, Providence, RI (ICERM)

Can machines prove theorems? Can they have mathematical ideas? On one hand, category theory offers a formalism for axiomatising ideas from machine learning. On the other hand, mathematicians are excited about the prospect of utilising machine learning techniques to spot new patterns in vast swathes of combinatorial data and hence formulate new conjectures. The purpose of this workshop is to bring together experts from across algebraic combinatorics, category theory, and machine learning in order to make headway on topics at the intersection of these fields.

University of Catania, Italy & University of Florida, USA

LOD 2025, An Interdisciplinary Conference: Deep Learning, Foundation Models & Artificial Intelligence without Borders

Event Secretariat;     Email: lod@icas.cc

Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Big Data, Optimization, Generative AI, Foundation Models, Large Language Models

Information & Knowledge Management, Big Data Computing,    Natural Language Processing, Computational Linguistics

University of Catania, Italy & University of Florida, USA

The Advanced Course and Symposium on Artificial Intelligence & Neuroscience (ACAIN) is a full-immersion four-day Course and Symposium in Tuscany on cutting-edge advances in Artificial Intelligence and Neuroscience with lectures delivered by world-renowned experts. The Course provides a stimulating environment for academics, early career researchers, Post-Docs, PhD students and industry leaders. Participants will also have the chance to present their results with oral talks or posters, and to interact with their colleagues, in a convivial and productive environment.

Event Secretariat;     Email: acain@icas.cc

Artificial Intelligence, Neuroscience, Cognitive Science, Computational Neuroscience, Foundation models, Large Language Models.

Pattern Recognition and Image Processing, Machine Vision,    Neurology

ICAS - University of Catania, Italy

IAISS 2025 is a full-immersion 5-day Summer School in Tuscany on cutting-edge advances in AI and Generative AI with lectures delivered by world-renowned experts. 

Event Secretariat;     Email: iaiss@icas.cc

Artificial Intelligence, Generative Artificial Intelligence, Large Language Models, Foundation Models, Deep Learning, Machine Learning

Natural Language Processing, Computational Linguistics,    Scientific Computing and Data Science

Stripe Conferences

Join us for the AI Industry 4.0 Conference on September 22-23, 2025, in Osaka, Japan. This AI summit, themed “Shaping the Future: New Ideas and Uses in AI,” brings together global experts to talk about the newest AI technologies. Attendees can join research talks, workshops, and hands-on lessons. This global AI Industry 4.0 summit meeting will cover subjects like artificial general intelligence, chatbots, Artificial Intelligence and Robotics for Industry for 4.0. The event lineup includes the AI & ML Summit, Responsible AI Summit, and Big Data Conference. Both professionals and beginners in AI and machine learning will gain practical information and experience from this global AI Event. At one of Japan’s most anticipated AI events, don’t pass up the opportunity to network with the international AI community and learn about the direction artificial intelligence is taking.

Stripe Conferences;     Phone: [+1 630 768 1199];     Email: ai@cognitivetechsummit.com

Smart Manufacturing Systems Predictive Maintenance AI Industrial Robotics Automation AI-driven Quality Control Supply Chain Optimization Digital Twin Technology IoT-Enabled AI AI in Logistics

Implementation and Applications of Artificial Intelligence,    Engineering & Industry (general)

The 1st Workshop on Pragmatic Reasoning in Language Models (PragLM) aims to stimulate research on LLMs as pragmatically competent language users. We invite contributions that will forward the discussion of understanding and improvement of LLMs' capability to generate natural language flexibly and efficiently across contexts, with relations to research on the cognitive and linguistic processes supporting effective, context-sensitive communication. Our interdisciplinary theme brings together researchers in NLP, computational pragmatics, cognitive science, and other fields.

Stripe Conferences

Join us for the Artificial Intelligence Conference on October 13-14, 2025, in Zurich, Switzerland This AI summit, themed “Shaping the Future: New Ideas and Uses in AI,” brings together global experts to talk about the newest AI technologies. Attendees can join research talks, workshops, and hands-on lessons. This global AI Event meeting will cover subjects like artificial general intelligence, chatbots, convolutional neural networks, and deepfakes. The event lineup includes the AI & ML Summit, Responsible AI Summit, and Big Data Conference. Both professionals and beginners in AI and machine learning will gain practical information and experience from this global AI conference. At one of Japan’s most anticipated AI events, don’t pass up the opportunity to network with the international AI community and learn about the direction artificial intelligence is taking.

Stripe conferences;     Phone: [+1 630 768 1199];     Email: ai@cognitivetechsummit.com

AI & Big Data AI & Robotics Data science Deep learning Data Analytics Artificial Intelligence in Healthcare Internet of things (IOT) AI Finance

5th International Conference on Big Data, IOT & NLP (BINLP 2025) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of Big Data, IoT and NLP. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Big Data, Internet of Things and NLP.

Phone: [9677141480];     Email: binlp@myyahoo.com

• Big Data & Business Intelligence, • Information Extraction, Retrieval, • Semantic Processing & NLP,

Information & Knowledge Management, Big Data Computing,    Internet of Things (IoT) and Sensor Networks

The 8th Asia Conference on Cognitive Engineering and Intelligent Interaction (CEII 2025) will be held during December 19-21, 2025 in Hong Kong, China. CEII was initiated in 2018 and held in Shanghai. It has been successfully held for four editions as a workshop in Shanghai AI Submit (SHAI2018).

Phone: [+86-13310183307];     Email: cfp@ceii.asia

Artificial Intelligence and Soft Computing Cognitive Science and Computational Biology Cognitive Engineering and Human-AI Teaming Extended Interaction Models and Systems Computer vision & image processing/recognition

Implementation and Applications of Artificial Intelligence,    Training in Computer Science, Courses

entwickler.de Akademie

Das neue GenAI Bootcamp bei den JavaScript & Angular Days vom 23. - 24. Oktober 2025 in Berlin ist deine praxisnahe Einführung in die Welt der Generativen KI - speziell für Angular-Entwickler:innen. In dieser kompakten Schulung mit Marco Frodl lernst du, wie du moderne GenAI-Technologien im Enterprise-Umfeld verstehst, anwendest und nahtlos in deine Angular-Projekte integrierst.

Was dich erwartet:

- Grundlagen von Generative AI, Prompt Engineering, RAG & Agenten

- Entwicklung von PoCs mit Python, LangChain & LangGraph

- Robuste AI-Schnittstellen für den Zugriff aus dem Frontend

- Integration von AI-Backends in Angular-Anwendungen

- Streaming von Antworten & visuelles Reasoning in der UI

Email: info@entwickler-akademie.de

Generative AI, Angular, Prompt Engineering, RAG, LangChain, LangGraph, TypeScript, AI-Integration, AI-UX, Streaming APIs, Obsidian, Frontend-Entwicklung, Enterprise AI, KI im Frontend, Developer-Portal, AI-Prototyping, Reasoning-UI, MCP-Server, Wissensmanagement, LLM-Anbindung, KI-gestützte Anwendungen

Implementation and Applications of Artificial Intelligence,    Training in Computer Science, Courses

Open Data Science Conference

ODSC West will feature 250+ speakers, instructors, and practitioners, 2,500 of your fellow data and AI practitioners, and hands-on instruction for world-class experts in: AI Agents, GenAI, LLMs, AI Engineering, Machine Learning, Deep Learning, AgentOps, LLMOps, MLOps, Ai for Robotics, Data Engineering, RAG, Reasoning, AI Safety & Security & more. You’ll also be able to build meaningful connections with speakers and peers in a relaxed setting during our networking events like the ODSC Networking Reception, Dinner with Data Scientists, Meet the Speaker session, Book Signing sessions, and more.

Phone: [none];     Email: iryna@odsc.com

ODSCWest, AI, Data Science, LLMs, Machine Learning

Numerical Analysis and Computational Mathematics,    Optimization Theory, Operational Research

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Currently, a paradigm shift is taking place from human interpretation of results towards machine interpretation. The long-term vision here is to automate the benchmarking process further to enable machines to automatically learn the properties of algorithms. This requires a more rigid benchmarking process with all assumptions and design decisions made explicit and accessible for machine interpretation.

Implementation and Applications of Artificial Intelligence,    Scientific Computing and Data Science

South African AI Association

Back for its 8th edition, AI Expo Africa is the largest Enterprise AI trade event and conference in Africa. AI Expo Africa 2025 will run 29-31st Oct 2025 in Johannesburg South Africa at the prestigious Sandton Convention Centre, uniting 2500 decision makers, investors, buyers, suppliers, innovators, SMMEs and global brands. This year it will also be joined by the inaugural UN ITU AI4Good Impact Africa Summit focused on how AI can be used to solve the UN SDGs.

Event Manager;     Email: enquiries@aiexpoafrica.com

Machine / Deep Learning Business Solutions RPA / Intelligent Process Automation CX / UX / Chatbots / Conversational AI Avatars / Agents / Autonomous Enterprise Platform AI / NLP / LLMs / SLMs Creative Media / Generative AI Big Data / Data Ops / Analytics Cloud / Data Centre / Storage / Super Computing ML Ops / Frameworks AI in Security & DevSecOps IoT / Mobile / Edge AI Smart Devices / Hardware Autonomous Systems / Industrial Robots AR VR / Mixed Reality / Metaverse / Web3 Training / Education & Skills Women in AI Applied University R&D AI4Good / Ethics / Privacy / Standards Regulation / Policy / Responsible AI Smart Cities / Smart Society & Smart Citizen National AI Strategies / Community Building / Investing

Implementation and Applications of Artificial Intelligence,    Scientific Computing and Data Science

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

The seminar aims to bring together researchers and practitioners from diverse backgrounds – including those working in robotics, healthcare, natural sciences, machine learning, and knowledge representation – to develop a concrete action plan for advancing practical Neurosymbolic AI applications over the next three to five years.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Research in Bayesian optimisation spans multiple domains, including Machine Learning, Statistics, Engineering, and Operational Research. This makes it a multi-disciplinary field that benefits from a wide range of perspectives and methodologies, but also means the community is dispersed. As a result, there is a need for a forum where researchers from different disciplines can share insights, compare approaches, and collaborate on standardizing tools and benchmarks.

Optimization Theory, Operational Research,    Algorithms and Data Structures

Interlink Events

Join us at the AI & Data Science Virtual Conference 2025, an immersive online event where the brightest minds in artificial intelligence, machine learning, data science, big data, and cybersecurity come together to share insights, discuss trends, and build the future of technology. The event will take place online on Monday, 04 November 2025, from 11:00 AM – 2:00 PM ET. Whether you're a professional working in AI, a data science enthusiast, or a cybersecurity analyst, this online event offers a deep dive into next-gen innovations, real-world applications, and emerging trends that are transforming industries worldwide.

Email: contact@interlinkevents.com

data science, ai, genai, llm, machine learning, automation

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

This seminar brings together researchers from diverse backgrounds in order to study these questions. Which applications are most likely for an adversary to gain from exploiting machine learning models? Are there particular attacks that will be more prevalent than others? What defense strategies are possible—and practical? What is the cost of these defenses to utility?

Information Security and Information Privacy,    Implementation and Applications of Artificial Intelligence

Stripe Conferences

Join us for the Generative AI Conference on November 10-11, 2025, in Osaka, Japan. This AI Event, themed “Shaping the Future: New Ideas and Uses in AI,” brings together global experts to talk about the newest AI technologies. Attendees can join research talks, workshops, and hands-on lessons. This global Generative AI summit meeting will cover subjects like artificial general intelligence, chatbots, and Businessworld Gen AI Summit. The event lineup includes the AI & ML Summit, Responsible AI Summit, and Big Data Conference. Both professionals and beginners in AI and machine learning will gain practical information and experience from this global AI conference. At one of Japan’s most anticipated AI events, don’t pass up the opportunity to network with the international AI community and learn about the direction artificial intelligence is taking.

Stripe Conferences;     Phone: [+1 630 768 1199];     Email: ai@cognitivetechsummit.com

Generative Adversarial Networks AI-driven Content Creation Text Generation Models Creative AI Applications Generative AI Model Training 3D Model Generation Story Generation AI Language Model Advances

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

In this meeting of the Research Training Group "Neuroexplicit Models of Language, Vision, and Action", we will discuss current research and trends in combining neural and human-interpretable models. These model architectures have complementary strengths with respect to generalization, robustness, and interpretability. The overall goal of the RTG is to develop novel neuroexplicit models that accurately solve tasks in natural language processing, computer vision, and action-decision making, and to investigate the theoretical and practical principles of designing effective neuroexplicit models.

Université Sultane Moulay Slimane, Laboratoire d'Ingénierie des Procédés, Informatique et Mathématiques

ICMICSA'25 is a peer-reviewed hybrid conference where researchers can present their paper in front of experts in different disciplines and backgrounds either in online or in-person mode during the conference dates. The conference will provide a platform to disseminate knowledge and results in theory, methodology and techniques in engineering and sciences and it will covers a broad spectrum of trending topics.

Comité d'organisation;     Email: icmicsa@usms.ma

AI, Machine Learning & Data Processing, Deep Learning, Natural Language Processing, Pattern Recognition, Computer Vision, Data Mining, Data Science, Big Data Analytics, Systems & Infrastructure, Cloud and Edge Computing, High­ Performance Computing, Information Systems Software Engineering, Emerging Technologies, IoT and Sensor Networks, Robotics and Automation, Human–Computer Interaction, Bioinformatics, Cyber Security

Information & Knowledge Management, Big Data Computing,    Multidisciplinary or General Events in Informatics

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Inductive programming (IP) research – also called inductive program synthesis – is concerned with learning computer programs from data, typically input/output examples. It incorporates many areas of computer science, especially machine learning, automated reasoning, program verification, programming language theory, and software engineering. IP also has strong relations to research outside computer science, notably in cognitive science, where it can help build models of human inductive learning and intelligent tutoring systems for programming education. In industry, IP supports tools for end-user programming such as the Microsoft Excel plug-in FlashFill.

Software Engineering,    Implementation and Applications of Artificial Intelligence

Generative Artificial Intelligence (GenAI) is fundamentally reshaping our understanding of human-machine interactions. For the first time, machines can generate coherent text, create visually compelling images, produce short videos, and even complete unfinished musical compositions. The widespread adoption of this kind of AI is changing creative production and consumption, reigniting debates on the nature of creativity and its evolving trajectory. AI-driven prompting has introduced new opportunities, empowering individuals from diverse backgrounds—including those without technical expertise—to engage in creative processes in unprecedented ways. However, the rise of GenAI also raises pressing concerns, including job displacement, labour inequities, ethical dilemmas, and a huge concentration of computational resources in a few Big Tech companies.

We are delighted to extend our invitation to the 3rd International Conference on AI-Generated Content (AIGC 2025), scheduled for December 27-28, 2025, in the vibrant city of Hangzhou. Building on the success of AIGC 2023 in Shanghai and AIGC 2024 in Beijing, AIGC 2025 will continue to serve as a premier platform for sharing cutting-edge research and discussing the latest trends in AI-generated content. This conference will adopt a hybrid format, facilitating both in-person and online participation, providing an excellent opportunity for reconnecting with colleagues we haven't had the chance to meet for an extended period.

Conference Secretariat;     Phone: [+86-15618780723];     Email: contact@icaigc.org

T1: Foundations of AI Artificial General Intelligence Machine Learning Deep Learning Transformer Neural Networks T2: AI in Creative Expression Generative AI AI-generated Audio AI-generated Art AI-generated Image T3: Advanced AI Applications Natural Language Processing (NLP) Computer Vision Diffusion Models for Machine Learning AI in Robotics

Pattern Recognition and Image Processing, Machine Vision,    Social Networks and Social Aspects of Information Technology

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

This Dagstuhl Seminar addresses research gaps in the continual assessment of AI systems amid post-deployment changes in requirements, user-specific objectives, deployment environments, and the AI systems themselves.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

The High-dimensional Learning Dynamics (HiLD) workshop aims to advance our understanding of machine learning in the context of high-dimensional data, which is increasingly common as models and datasets grow in size. The workshop focuses on developing insights into how these machine learning algorithms behave in complex, high-dimensional settings, such as deep neural networks and large language models. By bringing together researchers from diverse fields, this workshop aims to develop both practical and theoretical strategies for more efficient and effective training of large-scale machine learning models. This research is critical as it can lead to better-informed decisions in model design and training, ultimately improving computational efficiency and outcomes in AI.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

In addition to exploring new methodologies, the workshop will address key challenges such as the explainability and interpretability of decisions, data privacy, and the practical deployment of models using contextual information. While these challenges are well-recognized in the ML community, particularly with the rise of large language models, they remain underexplored in the Operations Research community. By fostering collaboration and knowledge exchange, the workshop aims to develop scalable models, promote better benchmarking practices, and expand the applicability of contextual optimization to a wider range of real-world problems.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

In this Dagstuhl Seminar, we will explore the particularly important human domain of visual art, in order to obtain further insights into the cognitive ability of abstraction. Art is a culturally old and worldwide established means for humans to express their thoughts, emotions and views about the world, often as an expression of non-expressible or verbalizable information, episodes or experiences. We argue that understanding human reasoning, especially the ability to abstract, art is a valuable and rich candidate to be systematically analyzed. The seminar offers a unique opportunity to gather computer scientists, psychologists, cognitive scientists, artists, and art historians to explore the art domain toward understanding the cognitive tools needed for building AI systems with better abstraction abilities to be used for more understandable representations. The seminar will focus on how AI and art conceptualize abstraction by identifying the similarities and the differences, the role of abstraction in understandability, and how it can contribute to XAI.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Recent years have seen the emergence of a number of research groups that aim to advance quantum computing and to develop a skilled quantum software workforce. A particular challenge that must be overcome for safe quantum computing at scale is the creation of robust, expressive, efficient, and tractable formal verification tools. Since there is currently no event dedicated to this pursuit, the first goal of this Dagstuhl Seminar is to bring together a dedicated community of researchers in a single venue. This will give participants an opportunity to identify and discuss the most pressing challenges and promising directions for quantum programming and verification.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Implementation and Applications of Artificial Intelligence,    Human-Computer Interaction, User Experience, Virtual Reality

Next Business Media

After a successful inaugural edition in 2025, the Data Science Conference (DSC Next 2026) is back for its 2nd edition, scheduled to take place from March 24–26, 2026, at NH Amsterdam Zuid, Netherlands. This landmark event continues to establish itself as one of Europe’s key platforms for connecting data science, machine learning, and AI professionals from academia and industry. With a broader agenda, expanded workshops, and more international participation expected, DSC Next 2026 promises to be even more impactful, offering three full days of insight, innovation, and interaction.

Diwakar Chandra Gour, Event Manager;     Phone: [+91 84483 67524];     Email: diwakar@datasciencenext.com

Data Ethics and Privacy, Big Data Analytics, Machine Learning and AI Applications, Data Visualization, Natural Language Processing, Predictive Analytics, AI in Healthcare, Finance, and Industry, Data Governance and Security

Scientific Computing and Data Science,    Pattern Recognition and Image Processing, Machine Vision

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

DANGER is a conference at the forefront of data science for mathematics, bringing together leading academics in mathematics to discuss and share how AI methods can accelerate their research, and be used to uncover new connections and conjectures. With a focus on number theory and geometry, datasets like sequences of numbers, or lists of shapes can be quickly generated in bulk and fed into these advanced algorithms for analysis. DANGER aims to foster new collaborations across mathematical fields connected by data science, and motivate the responsible use of AI in research to advance the field.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

In addition to code and language reasoning, recent work shows that large language models (LLMs) can achieve promising results on causal reasoning tasks, e.g. in determining cause-effect relationships. This points to a new paradigm for addressing real-world causal tasks by integrating LLMs in the causal inference workflow, but also raises fundamental questions on causal reasoning capabilities of LLMs. That is, given that LLMs are trained on observational data, do LLMs’ capabilities correspond to causal reasoning or are simply a result of dataset memorization; and how reliable are the reasoning outputs? And if not, how can we use causal techniques to improve LLMs’ reasoning capabilities? To address these questions, this Dagstuhl Seminar would bring together experts from causal machine learning and language models to foster collaboration and identify the key research questions at the intersection of the fields.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model (LLM), so it references an authoritative knowledge base outside of its training data sources before generating a response. RAG and VectorDBs are two important concepts in natural language processing (NLP) and multi-modal data management that are pushing the boundaries of what AI systems can achieve. This Dagstuhl Seminar aims to bring together researchers from the emerging areas of RAG, VectorDBs, systems, and applications – providing opportunities for interdisciplinary progress.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

The landscape of drug design is rapidly evolving, with computational methods and artificial intelligence (AI) playing an increasingly important role. This workshop aims to explore cutting-edge mathematical approaches that are transforming the field of computational drug design. We will delve into multiscale modeling techniques that bridge the gap between molecular and macroscopic levels, providing a comprehensive understanding of drug interactions and dynamics. Additionally, we will highlight the integration of generative AI and machine learning (ML) algorithms in predicting drug efficacy, optimizing molecular structure, speeding up drug repurposing, understanding potential toxicity, and thus accelerating the drug discovery and development process. By bringing together diverse experts from academia, industry, and innovation support organizations, and adding early career researchers to the mix, this workshop will foster interdisciplinary collaboration and innovation. Participants will gain insights into the latest developments, discuss challenges, and explore future opportunities in the quest for more effective and efficient drug design.

Applied Mathematics (in general),    Pharmacology and Drug Development

The International Centre for Mathematical Sciences (ICMS)

In the last few years, an important trend has emerged for using data-driven image models, in particular encoded by neural networks. Novel families of hybrid imaging methodologies, mixing data-driven and traditional mathematical approaches (such as optimisation or sampling methods) have flourished. For instance, generative or discriminative networks such as GANS, VAEs or normalising flows, can be either used in optimisation or sampling schemes as data-driven regularisers for solving inverse problems. Similarly, denoising networks or more generally regularising networks can be incorporated into optimisation or sampling schemes leading to Plug-and-Play methods. From another perspective, unrolled optimisation approaches have been investigated to provide robust network architectures as alternative to traditional black-box end-to-end networks. All these approaches have shown a remarkable versatility and efficiency to solve inverse imaging problems.

Numerical Analysis and Computational Mathematics,    Scientific Computing and Data Science

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Autonomous AI agents, empowered by recent advances in Large Language Models (LLMs) and generative AI, are rapidly changing how we interact with, structure, and reason about information. These "agentic" systems go beyond static analysis – they act: interpreting their environment, interacting with humans and other agents, and autonomously executing tasks. This evolution opens new doors for knowledge engineering, especially in the context of increasingly complex, dynamic, and human-centric information spaces. While traditional knowledge engineering emphasized structured symbolic representations (e.g., ontologies, knowledge graphs) and formal reasoning, modern generative agents leverage machine-learned representations and natural language interfaces to emulate intelligent behavior. However, this comes with new challenges: How can we ensure such agents are interpretable, controllable, and aligned with human expectations? What is the role of hybrid systems that combine symbolic reasoning and statistical learning? This meeting will explore agentic AI as a foundation for the next generation of knowledge engineering systems – those that not only understand and transform data, but can also autonomously interact, reason, and collaborate.

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

spatial AI; probabilistic and statistical learning; generative deep learning; simulation-based inference; climate change

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

The aim of this Dagstuhl Seminar is to bring together researchers from different subfields of computer science that study or use techniques in knowledge compilation.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Recently a variety of state-of-the-art methods in machine learning and artificial intelligence have been developed motivated by techniques from Stein’s method, a successful tool from the field of probability theory. These methods have enabled efficient analysis of the large amounts of data being produced in several scientific fields, like neuroscience, information technology, and finance. Motivated by this success, there has been an ever increasing interest in exploring further connections between Stein’s method and machine learning. The focus of this workshop is to consolidate isolated efforts and develop a theoretically principled inferential and computational framework via Stein's method for analyzing increasingly complex models and data objects. This workshop is intended to bring together prominent and promising young and diverse researchers working on Stein’s method and machine learning, and to charter the path for future development in the field.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Particle physics, astrophysics, and cosmology cover the study of the universe from its smallest to largest scales. We study them in order to understand both the fundamental interactions that govern the universe and its large-scale structure and history. Advanced detectors at collider facilities that accelerate particles to near the speed of light and telescopes that monitor the night's sky looking back to the time just after the Big Bang are used to collect vast amounts of data in complicated datasets. Analyzing these data requires modern tools, making use of advanced machine learning and high performance computing infrastructure. This workshop brings together physicists, statisticians, and machine learning experts in order to make the most use of this data and learn as much from it as possible by discussing how to address the complexities of these large and detailed datasets, searching for 1 in a trillion events, and understanding correlations between thousands of quantities.

Mathematical Physics,    Probability and Statistics, Game Theory

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Social Inteliigence in AI Theory of Mind Large Language Models Social Arificial Intelligence Social Agents

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

Explainable AI; Knowledge Representation; Neuro-Symbolic AI; Machine Learning

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Catastrophic events, even though they happen rarely, have a significant impact when they occur. Disastrous climate, financial, insurance or complex network failure events can have devastating social and environmental consequences. A complete risk analysis for modelling and prediction purposes requires understanding how these extreme, rare events occur, and what are the main drivers causing them. Machine learning methods open the road for methodological developments to forecast these extreme events and discover their complex, possibly high-dimensional nature. The aim of this workshop is to bring together researchers contributing to closely related, but culturally disconnected research communities: extreme value theory and machine learning. The goal is to discuss new directions and open mathematical problems, and foster further collaboration. The leading experts will introduce young researchers, postdocs and graduate students to the state-of-the-art in the field.

Probability and Statistics, Game Theory,    Applied Mathematics (in general)

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

This workshop brings together leading experts in neuroscience, cosmology, and AI to develop new methods that not only help us understand the universe but also the neurons that make such understanding possible. With a focus on AI (XAI), participants will create algorithms that provide new insights on complex data by revealing how and why they make decisions. By studying the neurons that help us think and the AI that explains how it "thinks," we are not just pushing the boundaries of science—we are unraveling the very mechanisms that allow us to comprehend our place in the cosmos.

Banff International Research Station for Mathematical Innovation and Discovery (BIRS)

Representation learning is at the heart of a paradigm shift in machine learning, driving many of today’s most advanced artificial intelligence (AI) systems. Representation learning transforms raw data into meaningful features that machines can use to perform a wide range of tasks, such as image recognition, content recommendation, and text generation. However, a key challenge remains: ensuring that these representations are identifiable, a property that is fundamental to reliably deploying AI systems in real-world applications. Although identifiability is critical to developing trustworthy AI systems, the current mathematical understanding of representation learning in general, and identifiability in particular, is extremely limited. To address this gap in understanding, Banff International Research Station will host a workshop focusing on principled approaches for learning identifiable representations and understanding the properties of such representations.