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

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.

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

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

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.

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.

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

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