Edinburgh Data Science Meetup - Agentic AI Special

Edinburgh's community meetup for people working in - or just interested in - Data Science & AI. We're a friendly bunch of professionals, academics, students and others who enjoy meeting over drinks and pizza and listening to insightful talks.

You can now RSVP either here or via luma.com!

This event is kindly sponsored by FreeAgent - the Edinburgh-based accounting software provider for small businesses. We will be hosted at their offices from 5.15pm on Wednesday 25th November.

We have two great talks on our themed special exploring building AI agents:

Suleman Imdad Your AI Passed the Test. That's the Problem
A model can ace its evaluation and still fail the moment it meets reality. This talk digs into why, with findings from my own research:
• An 8B open model that changes its behaviour when it senses a safety test (a trick usually blamed on frontier models), and why its family predicts that better than its size
• The standard benchmark split that understates how badly drug-discovery models fail on genuinely new chemistry, on 21 of 22 endpoints
• What running a machine-learning system with real money teaches you about validation that no leaderboard will
• A practical checklist for catching a flattering evaluation before production does

Dr. Jon Minton Data as graphs: how to get AI agents to know what you know and they need to know
Say "data" to a statistician and they picture a table; say "graph" and they picture a chart. A computer scientist pictures nodes and edges. Working well with AI agents means holding both at once.
In two years we've moved from prompt engineering to context engineering, then loops, and now graphs. Jon Minton argues that the durable skill isn't the tooling. It's graph thinking: building a growing web of linked notes, decisions and preferences that an agent can traverse, so it keeps learning what you know and what you need to know. That web runs back from Codd's normal forms through tidy data to slipboxes and hypertext.
Drawing on the past few months of running a small consultancy with agents, Jon looks at the connections in both directions. Some carry knowledge from people to agents. Others carry it back from agents to people, as charts, reports and the occasional task the agent hands to its human. He also looks at where those connections turn into dark patterns: humans used as the machine's hands, or one model passing a refused task to another.

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Schedule:
5.15pm - 6.30pm - Networking with pizza and drinks
6.30pm - 7.45pm - Talks with Q&A
7.45pm onwards - Continued networking.

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Speaker bios:
Suleman Imdad is the founder of Steel.vision, an Edinburgh-based company working on AI assurance (evaluating, testing and safety-checking enterprise AI systems) and edge AI for industrial field work. Over 15+ years he has built production software and AI systems for clients including Apple, Walmart, Nike, PwC and CVS, and he also builds and runs a live, real-money ML trading system. He is completing an MS in Artificial Intelligence at Johns Hopkins University, where his research covers transformer quantization and activation steering.

Dr. Jon Minton is a statistician, health economist, and public health researcher with approximately 20 years of experience across academia, public health, and the medical technology sector. He began his career in research at the University of York, contributing to systematic reviews and health technology assessments for NICE, before moving to the University of Sheffield to focus on health economic modelling. He subsequently spent five years at the University of Glasgow researching mortality trends and health inequalities, followed by six years at Public Health Scotland, where he supported the Scottish Government’s COVID-19 response and led population health analysis initiatives. From 2024 to 2026, he worked as a Senior Statistician at Smith+Nephew, specialising in clinical trial analysis, statistical methodology, and automation. Most recently, he founded Knowledge Tree and Butterfly Ltd, an Edinburgh-based consultancy focused on integrating agentic AI and reproducible analytical methods into research and software development.

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FreeAgent is an accessible venue, if you have any questions about accessibility please get in touch.