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We are excited to present the Best Practices in AI Afternoon 2 which will be held on the 11th of November, 12-5pm at Design Studio 01 (D05), Pam Liversidge Building, Broad Lane, S1 3JD.
The afternoon will consist of talks and walkthroughs on best practices for research, design, development, and deployment of AI. It will focus on practical aspects such as tooling, optimisation, profiling, tips and tricks to supercharge AI in your research!
Buffet lunch and coffee will be provided.
This event is held in collaboration between the Research Software Engineering (RSE) group and the Centre for Machine Intelligence (CMI) in the University of Sheffield, and the N8 CIR.
Register to attend in-person or remotely
Denis Newman-Griffis, University of Sheffield Shaun Donnelly, University of Sheffield Su Golder, University of York Roy Ruddle, University of LeedsDenis Newman-Griffis (they/them) is a Senior Lecturer in the School of Computer Science and Theme Lead for AI-Enabled Research in the University of Sheffield’s Centre for Machine Intelligence. Their award-winning work investigates the responsible use of AI for human flourishing, including pioneering work on responsible AI in research funding and assessment, ethical practice for AI in policy and public services, and critical perspectives on AI for health and disability. Denis is a British Academy Innovation Fellow and a former Research Fellow of the Research on Research Institute.
Shaun is a research software engineer at the University of Sheffield with a background in astrochemistry and AI-driven computational chemistry. Since joining the RSE team in December 2024, he’s been exploring how large language models can transform research workflows, from Retrieval Augmented Generation to tool-driven Agentic AI. He’s particularly interested in exploring how to make research software more accessible by the use of natural language as an interface between researchers, their software and their research workflows.
Su is an Associate Professor in the Department of Health Sciences. She has a background as an information specialist with over 25 years’ experience in literature searching. She has specialist expertise in systematic review methodology - including automation and AI, systematic reviews of adverse effects, and using social media as a data source. During her post-doctoral NIHR (National Institute for Health Research) fellowship she has expanded on her work in her PhD by investigating the use of unpublished data, text mining and social media in the retrieval of adverse effects data. In her current research she continues to undertake systematic reviews, develop systematic review methodology, conduct network analyses and to use social media to gain wider information as well as healthcare user and provider perspectives. She is currently an elective member of the Cochrane Methods Executive and one of the founders and co-convenors of the Cochrane Adverse Effects Methods Group.
Roy Ruddle is a Professor of Computing at the University of Leeds, and Director of Research Technology at the Leeds Institute for Data Analytics (LIDA). Roy has worked in both industry and academia, and specialises in interdisciplinary research into interactive visualization, data quality and data science workflows. His Leeds Virtual Microscope (LVM) has been commercialised by the healthcare company Roche and was a REF2021 Impact Case Study. He was an Alan Turing Institute Fellow from 2018 – 2023, and is currently principal investigator on two major projects: Making Visualization Scalable (MAVIS) for explainable AI (funded by the EPSRC) and AI for Dynamic prescribing optimisation and care integration in multimorbidity (DynAIRx; funded by the NIHR). He has published a practitioner’s guide for rigorously and efficiently checking data quality (https://doi.org/10.5518/1481) and an associated software package as open source (https://pypi.org/project/vizdataquality/).
In-person spaces are limited, please register to attend!
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