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

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.

Fields Institute, Toronto, Canada

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.

QAIO 2026 is a workshop at the 18th International Conference on Agents and Artificial Intelligence (ICAART), focusing on the intersection of quantum computing, artificial intelligence, and optimization. Researchers and industry experts will present the latest advancements and discuss the future of these cutting-edge technologies.

Isaac Newton Institute for Mathematical Sciences, Cambridge

This two-day workshop on Reinforcement Learning for Science: Discovery and Automation, is a follow-up event to the Isaac Newton Institute satellite programme “Bridging Stochastic Control and Reinforcement Learning.” The workshop will bring together leading experts from academia and industry to demonstrate how tailored reinforcement learning (RL) techniques can accelerate scientific discovery and drive engineering automation. It will feature invited talks and panel discussions showcasing how RL can be effectively adapted and integrated into workflows spanning medicine, the physical sciences, robotics, and complex engineering systems. By highlighting practical applications and fostering interdisciplinary dialogue, the workshop aims to catalyze new collaborations and promote the broader adoption of RL in high-impact scientific and engineering contexts.

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

University of Maia and Institute for Research Development, Training and Advice – IRDTA, Brussels/London

AIces 2026 is a multidisciplinary research training school featuring 12 courses, 2 keynote lectures, a round table, three open debate sessions, and a participant symposium. It focuses on AI across three thematic lines: cognition, ethics, and society, and is aimed at graduate students, researchers, and industry practitioners.

Email: david@irdta.eu

AI, Ethics, Cognitive Science

Social Networks and Social Aspects of Information Technology,    Events for IT Students and Early Career Researchers

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.

Isaac Newton Institute for Mathematical Sciences, Cambridge

The purpose of the workshop is to bring together specialists from several communities: spectral geometers, numerical analysts, and researchers working in AI geometric data processing and PDEs. The development of AI based spectral analysis requires a deep interaction between them. Our primary objective is to exchange perspectives and gain a deeper understanding of the potential role that artificial intelligence and geometric data processing may play in addressing research questions in spectral geometry, and ultimately to foster the development of new collaborations. We aim to explore how AI might provide new methods, insights, or tools that could help us tackle some of numerical problems related either to the computation of the spectrum or the optimization of the geometry in relationship with spectral functionals.

Algebra,    Geometry and Topology

Izmir Katip Celebi University & Nevsehir Haci Bektas Veli University

Six Years at the Forefront of Science and Future Technology. For the past five years, the International Artificial Intelligence and Data Science Congress (ICADA) has been successfully organized under the leadership of the İzmir Kâtip Çelebi University (İKÇÜ) Artificial Intelligence and Data Science Application and Research Center, establishing itself as a prestigious brand in the field. This year, we are proud to present the 6th ICADA, hosted by Nevşehir Hacı Bektaş Veli University (NEVÜ) in the inspiring atmosphere of Cappadocia. Artificial intelligence and data science have evolved beyond mere technical disciplines to become the fundamental driving forces reshaping the paradigms of the modern world from engineering to health, and from social sciences to the arts. Our congress aims to foster interdisciplinary interaction, pave the way for transforming theoretical knowledge into practical applications, and encourage knowledge sharing on a global scale. As the architect of this organization, İzmir Kâtip Çelebi University (İKÇÜ) Artificial Intelligence and Data Science Application and Research Center continues to guide the academic ecosystem not only through this congress but also through the “Journal of Artificial Intelligence and Data Science” (JAIDA), which contributes high-quality research to scientific literature, as well as through numerous workshops and R&D projects conducted throughout the year. We are honored to welcome distinguished scientists and industry representatives to join us on this journey to expand the frontiers of science and design the future today.

Event Secretariat;     Email: icadacongress@gmail.com

Artificial Intelligence, Data Science, Cyber Security, Basic & Applied Science, Engineering IoT & Industry 4.0, Finance & Economy, Communication Technologies, Business & Management, Art & Design & Construction, Law & Legal Technologies, Social Sciences, Media & Social Life, Climate & Energy, Education, Technology, Smart City, Information, Health

Higher School of Technology of Casablanca, Hassan II University, Casablanca, Morocco

We are pleased to invite you to participate in the 4th International Conference on Connected Objects and Artificial Intelligence(COCIA’2026), which will be held in Casablanca, Morocco, from April December 28–30, 2026.

COCIA’2026 is organized by the The Laboratory of Networks, Computer Science, Telecommunication & Multimedia (RITM), Higher School of Technology of Casablanca, Hassan II University, Casablanca, Morocco, in collaboration with the Moroccan Association of Researchers in Technology and Artificial Intelligence (AMaCTIA).

Email: cociaconference@gmail.com

Design of connected objects, Connected Systems, Industrial IoT IoT architecture for intelligent environments IoT Edge and Cloud Architectures IoT Communication Technologies Energy Efficiency and Sustainability in IoT Robotic IoT Systems Smart Systems IoT for Healthcare, e-Health IA for embedded systems, IA for IoT, Big data for IoT IA for biomedicine, Biological models for IA Artificial Intelligence and Databases Cognitive intelligence in vehicular networks Intelligent systems for software computing Sensor-based intelligent systems Intelligent systems for wireless applications Intelligent sensors and sensing application Antennas, RF, Wireless Sensor 5G and next generation Communication systems and Network Analog and Digital Signal Processing Biomedical signal processing RFID and BLE technologies in intelligent systems Analog and digital circuit design Electronic devices and sensors Electronics and embedded systems Electronic Systems, Energy Systems Smart Homes, Cities and Communities Smart Grids & Microgrids

Internet of Things (IoT) and Sensor Networks,    Mobile Computing & Telecommunication

Open Data Science Conference

​This is more than just a conference; it’s the essential event for data science practitioners, AI builders, technical leaders, and anyone looking to pivot into an AI-driven career. With 300+ hours of content from 250+ expert speakers, you'll gain job-ready skills and strategic insights you can implement immediately. At ODSC AI East, we prioritize tangible value and immersive learning, ensuring you walk away certified and skilled. Our expansive agenda is packed with cutting-edge workshops and deep-dive tutorials across the most in-demand domains.

Email: jhenayon@yahoo.com

​Hands-On Training: Master crucial skills in LLMs, Generative AI & RAG, AI Engineering & MLOps, Data Science, and Machine Learning with expert-led, hands-on sessions. ​Focused Tracks: Explore specialized content in Agentic AI & Workflow Automation, Data Engineering, and AI for Robotics. ​Learn from Engineers Who Ship: Our speakers are the industry's top practitioners and researchers, renowned for bringing real-world innovations and strategies to the stage.

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

Lake Como School of Advanced Studies (LCSAS)

Implementation and Applications of Artificial Intelligence,    Social Networks and Social Aspects of Information Technology

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.

QCon Conferences

The AI conference for senior developers, architects, and engineering leaders. Move beyond the hype and learn how real teams accelerate the entire software lifecycle with AI. Get blueprints for building enterprise-scale, resilient, and secure AI systems.

Marketing Team;     Email: maria.popa@c4media.com

artificial intelligence, software engineering, software development, software architecture

Software Engineering,    Implementation and Applications of Artificial Intelligence

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

Coveros

Don't Just Watch the AI Revolution—Help Lead It. Join the premier conference dedicated to Artificial Intelligence and Machine Learning, live in Seattle and virtually online. AI Con USA is the essential gathering for tech & engineering leaders, researchers, and innovators, covering the critical topics that matter now: real-world AI in practice, RAG, AI strategy, agentic, and much more. This is your unparalleled opportunity for knowledge exchange, career-defining networking, and gaining the actionable insights you need to keep your organization at the absolute forefront.

Client Support;     Phone: [9042780524];     Email: clientsupport@coveros.com

Fundamentals of AI, Agentic, AI Workforce, Machine Learning, LLMs, AIOps, AI Governance

K. Jbilou, Department of Mathematics LMPA, University ULCO, Calais France and Marilena Mitrouli Department of Mathematics University of Athens, Greece

Phone: [0033626622981];     Email: jbilou@univ-littoral.fr

We are delighted to confirm the following distinguished plenary speakers: Stefano Serra Capizzano, Italy Marco Donatelli, Italy Stelios Georgiou, Australia Marcos Raydan, Portugal Lothar Reichel, USA Yousef Saad, USA Hassane Sadok, France

Numerical Analysis and Computational Mathematics,    Modeling and Simulation

Isaac Newton Institute for Mathematical Sciences, Cambridge

This workshop explores recent advances in the use of flexible machine learning techniques alongside semiparametric and nonparametric statistical methods in causal inference. Recent methodological work has focused on combining modern machine learning tools with the inferential rigor of semiparametric and nonparametric frameworks to estimate causal parameters in complex, high-dimensional settings. The aim is to move beyond the predictive focus typical of standard machine learning, and instead develop estimators that enable valid causal inference while achieving desirable statistical properties such as efficiency and robustness. The workshop will highlight cutting-edge developments and foster discussion on future directions in this rapidly evolving area.

Lake Como School of Advanced Studies (LCSAS)

Thermodynamics, Fluid Dynamics and Statistical Physics,    Courses and Events for Physics Students

Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, Croatia

Conference Secretariat;     Email: splitech@fesb.hr

IoT, AI, Smart City/Environment, e-health, RFID, Energy, Engineering Modeling

Internet of Things (IoT) and Sensor Networks,    Embedded Systems & Ubiquitous Computing, RFIDs

European Association for the Study of the Liver (EASL)

Artificial intelligence is sweeping all over medicine. But where is it useful, and where should clinicians be cautious? How can we shape the path towards equitable and explainable algorithms while mitigating biases and vulnerabilities for hepatology? In this interdisciplinary EASL school, you will learn key aspects of clinical artificial intelligence, all the way from understanding other people’s publications and claims towards your own kick-off for AI-enhanced research and clinical practice.

General hepatology; EASL Schools & Masterclasses

Hepatology,    Healthcare Technologies

Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH

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

Wilhelm and Else Heraeus-Foundation

To boost our ability to simulate quantum mechanical systems, we believe that insights from theoretical computer science perfectly complement the existing knowledge in physics. In particular, our own recent research has shown that automated reasoning techniques, originally developed to analyze complex (classical) information systems, show great promise in the domain of quantum mechanics. Moreover, these new approaches have been shown to break boundaries between existing approaches, combining their strengths and overcoming individual limitations. Based on these developments, we aim to realize fundamentally novel formalisms for representing and processing quantum information.

Quantum Mechanics and Quantum Technology,    Information Theory, Foundations of Computer Science

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)

Wilhelm and Else Heraeus-Foundation

We have identified leading experts whose work sits at the convergence of quantum computing and AI, and who combine scientific excellence with the proven ability to deliver clear and engaging lectures suited to early-career researchers. All of them have agreed to contribute to the school, ensuring that participants receive a rigorous introduction.

Quantum computing has emerged as one of the most promising technologies of the 21st century, yet its practical use cases and programming paradigms remain unfamiliar. At the same time, artificial intelligence and machine learning are transforming research and industry at an unprecedented pace. The intersection of these two areas, Quantum Machine Learning (QML) represents a rapidly expanding topic, but its concepts are still only partially understood and rarely taught in a coherent, hands-on framework.

Courses and Events for Physics Students,    Courses and Events for Math Students and Early Career Researchers

Lake Como School of Advanced Studies (LCSAS)

Courses and Events for Math Students and Early Career Researchers,    Courses and Events for Physics Students

Recent years have seen an explosion of cross-disciplinary research linking machine learning and theoretical physics. In this workshop, we focus on generative methods as applied to lattice field theory, with QCD as a long-term target. With many approaches being explored at various levels of maturity (normalising flow, including stochastic and continuous normalising flow, diffusion models, transport models, ...) and a deepening understanding from the perspective of theoretical physics (links to the renormalisation group, gauge symmetries, stochastic quantisation, random matrix theory, ...), it is time to take stock and constructively assess the applicability of the methodology and future perspectives in the field. The meeting will bring together researchers from the theoretical physics community, active in lattice field theory and statistical physics, with researchers in ML, to optimise the possibility of knowledge exchange. As the field is evolving rapidly, we take care to invite proponents of new and promising methods, as they are developed between the submission of the proposal and the delivery of the workshop. The key outcome will be the advancement of a common foundation and the agreement on a set of targets for the short and intermediate term.

Institute of Mathematics & its Applications (IMA)

AI Unlocked 2026, will bring together the brightest minds from academia, industry, and government to explore the power, promise and purpose of artificial intelligence. This two-day congress, chaired by Dr. Anjulika Salhan, a global expert in AI-driven innovation and founder of Systems Holdings, will be held at the prestigious Hyatt Regency in Birmingham. We invite you to join a dynamic community of AI pioneers shaping the future of machine learning and intelligent systems. World-class speakers from across sectors will share their research, breakthroughs, and real-world applications of AI, ranging from deep learning architectures and causal models to AI in finance, healthcare, governance, and regulation.

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.

MICAD 2026 organizing committee

We are pleased to welcome you to the 7th International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2026), which will take place in Edinburgh, United Kingdom, from October 22 to 24, 2026.

Over the past six editions—Oxford (2020), Birmingham (2021), Leicester (2022), Cambridge (2023), Manchester (2024), and London (2025)—MICAD has established itself as a leading international forum in Europe, bringing together researchers, clinicians, engineers, and industry professionals from around the world to exchange ideas and advance the field of medical imaging and computer-aided diagnosis.

MICAD 2026 aims to foster high-level academic exchange on multimodal medical image intelligent analysis, medical imaging foundation models, trustworthy and explainable artificial intelligence, intelligent computer-aided diagnosis systems, and the deep integration of medical imaging with clinical decision-making and precision medicine.

The conference will feature a comprehensive and dynamic technical program, including Keynote and invited speeches delivered by internationally recognized experts. In addition, Oral and Poster Sessions will present cutting-edge research findings, methodological innovations, and clinically relevant applications. To recognize excellence, Best Paper Awards will be presented, and participants will benefit from valuable networking opportunities with leading academics, clinicians, and industry innovators.

Publication

All submitted papers will undergo a rigorous peer-review process facilitated by our conference committees. Accepted and presented papers will be featured in the MICAD 2026 Conference Proceedings which will be published with Springer in their "Lecture Notes in Electrical Engineering (LNEE)" (ISSN: 1876-1100). Furthermore, the proceedings will be submitted for indexing to prominent databases including EI-Compendex, SCOPUS, and Springerlink.

Submission

1.Open conf submission system: https://www.micad.org/openconf/openconf.php

2.Email: cfp@micad.org

Choose a way to submit, please do not repeat the submission.

Conference Secretariat;     Phone: [+86-15618780723];     Email: cfp@micad.org

AI-Powered Medical Imaging Imaging Modalities and Computational Techniques Image Processing, Analysis & Visualization Computer-Aided Diagnosis and Decision Support Robotics, Devices & Smart Systems in Healthcare Photonics and Emerging Biomedical Imaging Technologies

Quantum Mechanics and Quantum Technology,    Quantum Information Theory and Quantum Computing

South African AI Association

Back for its 9th edition, AI Expo Africa is the largest Enterprise AI & Intelligent Automation trade event and conference in Africa. AI Expo Africa 2026 will run 28th- 29th Oct 2026 in Johannesburg South Africa at the prestigious Sandton Convention Centre, uniting 3500+ decision makers, investors, buyers, suppliers, innovators, SMMEs and global brands. Learn more about sponsoring, speaking & tickets here >> www.aiexpoafrica.com

Event Manager;     Email: enquiries@aiexpoafrica.com

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