AI and Data Sandpit Workshop

About the Workshop

Join us on 23 January 2026 at City St George’s, University of London for the ACE Network’s Sandpit workshop of the AI and Data Theme.

The ACE, AI and Data GCRC: https://ukcontrol.org/gcrc-ai-and-data leverages the synergy between Automatic Control Engineering and Artificial Intelligence, with applications including autonomous systems, healthcare, energy, and manufacturing.

Purpose of the Workshop

This event will:

• Address the key challenges highlighted in our earlier Industry/Roadmap Workshop (https://ukcontrol.org/post-event-report-ai-and-data-industry-and-roadmap-workshop). The details are provided below.
• Encourage partnerships between industry and academic/research institutions in the field of AI and Data, with a specific focus on the interplay between control theory and AI.
• Speed up the progress of concrete ideas by drafting proposals and fostering team collaborations.

Agenda

Morning
– 9:30 – 9:45: Welcome and Introduction (Nabil Aouf, City St Georges, University of London and Kostas Margellos, University of Oxford)
– 9:45 – 11:00: Academic and Industry Presentations – (Prof Michael Fisher, University of Manchester, Prof Ajay Chakravarthy, Head of Artificial Intelligence, Thales, UK and Prof Nick Colosimo, Head of Group Science and Technology. BAE Systems, UK)
– 11:00 – 11:15: Break
– 11:15 – 12:15: Interactive Brainstorming Sessions 
– 12:15 – 1:00: Lunch

Afternoon
– 1:00 – 1:30: Interactive Outcome Session 
– 1:30 – 3:00: Breakout Sessions – Draft proposals.
– 3:00 – 3:15: Break
– 3:15 – 3:30: Closing and Next Steps

The interactive activity session will focus on discussing ideas for project proposals that capitalise on the opportunities/challenges identified in the industry/roadmap workshop. The main areas identified are listed below:

Identified Priority Challenges:

Opportunities/Challenges on integrating AI methods in Control Engineering

– Ensuring safety with AI modules in the loop
– Trust in data-driven algorithms/controllers
– Merging AI models with Dynamical models, synergy between conventional and data-driven counterparts
– Lack of appropriate data (scarcity, quality, security, IP)
– Models are not explainable (e.g. deep Learning)

Opportunities/Challenges on integrating Control Engineering tools with AI methods

– How to embed physical properties, interpretability
– Applying control theoretic tools to AI training
– Using control to enable robustness
– Leverage rigour and guarantees
– Interfacing control with AI algorithms; redefine control paradigm