AICatalyst2026: AI in the Biosciences Training Conference
4th September 2026 @ The Guildhall, York
About the Conference
This free training conference is organised by the UKRI Digital Research Skills Catalyst, with funding support from AIBIO-UK.
The programme brings together a mix of expert talks and hands-on workshops designed to introduce AI methods in bioscience research.
These sessions will equip you with working knowledge of a wide range of techniques under the AI umbrella, from traditional Machine Learning to Large Language Models, applied to a range of biological applications.
Attendees will also have the opportunity to present their own work through a poster session, allowing you to network and be part of the nascent AI in Bioscience community, as well as potentially win the coveted Poster Prize!
While this conference primarily is targeted at PhD and postgraduate researchers in the biological, medical, and environmental sciences, we welcome professionals at all career stages. Whether you use AI in your work already or are just wondering how you could incorporate it into your research, this event is open to all.
Date and Location
The conference will take place from 9:30 to 16:15 on Friday 4th September 2026 at The Guildhall in York, a beautiful medieval building in the heart of the city centre that is a short 10 minute walk from York Railway Station.
Registration
Registration has closed
All 100 places have been allocated and as such registration has closed. Please email research-digital-skills@york.ac.uk if you wish to be added to a waiting list in the event of a place becoming freed up.
Travel scholarships
Please note that all scholarship funding has now been allocated.
Through the AIBIO-UK Flexifund, we are pleased to offer travel scholarships to help cover your attendance costs.
Standard Travel: Up to £100 per person for economy rail or bus travel
Long-Distance Travel: Up to £150 per person for those traveling from further afield who require an overnight stay in York (covers economy travel and hotel expenses)
You will need to reclaim these expenses by completing an expense form and submitting receipts after participation in the course. Scholarships will be provided on a first come first served basis. We will contact you within a few days of your registration to confirm whether you have been awarded a scholarship. Lunch and Networking tea/coffee will be included.
Terms and conditions
Please note that reimbursement cannot be processed without valid receipts.
- Train travel You must travel in standard class (unless you have a disability or accessibility needs). You will need to request approval prior to travel if you wish to travel in a higher class than standard.
- Car Travel You should only travel by car if rail travel or public transport is not practical or cost-effective. The HMRC mileage rate for expenses claims is 55p per mile.
- Taxis The University of York expects visitors to use public transport where practicable. Taxis can only be used in the following circumstances:
- if you have a disability that makes it difficult to use public transport
- if you have an early start or late return and there is only very limited public transport at that time
- if you are in an area where there is no public transport
- if you need to travel in an unsafe area.
Programme
The event will be taking place across all three venues within the Guildhall: The Main Hall, The Riverside Lounge, and The Committee Room.
Overview
| Time | Main Hall | Riverside Lounge | Committee Room |
|---|---|---|---|
| 09:30–10:00 | Registration + networking coffee | ||
| 10:00–10:15 | Introductory Welcome | ||
| 10:20–11:20 | Image visualisation with Napari, a modular based framework with AI capabilities | Large Language Models in Bioscience | AI for agriculture |
| 11:20–11:30 | Break | ||
| 11:30–12:30 | Image visualisation with Napari, a modular based framework with AI capabilities | scryptIQ Supervised Learning Taster Session | Funding at the Interface: Tips for writing grants for AI and Biosciences research |
| 12:30–13:30 | Lunch + Poster Sessions | ||
| MORF-BIO Demonstration | |||
| 13:30–14:30 | Galaxy for AI in Bioscience: Tools, Training, and Reproducible Workflows | A whirlwind tour of the FAIR principles | |
| 14:30–14:50 | Coffee Break | ||
| 14:50–15:50 | Biologically-Literate Feature Selection | AI for Multiomics | |
| 16:00–16:15 | Closing Remarks | ||
| 16:15 onwards | Networking at House of Trembling Madness |
Session Details
Image visualisation with Napari, a modular based framework with AI capabilities
Prof Dave Cash (University College London); Dr David Pérez-Suárez (University College London)
Image analysis, visualisation and processing are a set of skills required in any medical and bioscience researcher who works with imaging instruments. Napari is an extensible modular based open source framework that is easy to learn and to develop to. This session will introduce how to use it in your research workflow and present some of the plugins that provide AI capabilities to analyse and process your data.
Large Language Models in Bioscience
Dr Alastair Droop (University of York)
Large Language Models (such as Gemini or ChatGPT) are at the forefront of the current AI hype wave. In this talk Dr Alastair Droop will provide a brief overview of how Large Language Models work, and discuss their use in bioscience research. Common misconceptions and pitfalls will be explored.
AI for agriculture
Prof Elizabeth Sklar (Director of LIAT, University of Lincoln); Prof Simon Parsons (Director of SUSTAIN, University of Lincoln)
This talk is intended to be a broad “taster” session, aiming to introduce participants to popular Artificial Intelligence (AI) concepts that appear in the media or consumer marketing documents (e.g. “deep learning”, “large language model”, etc.). We will discuss how these methods fit within the wider field of AI and how the full range of AI techniques can aid the agri-food sector. We will share examples from our work at the Lincoln Institute for Agri-food Technology (LIAT), University of Lincoln, as well as highlighting seminal work of others drawn from the agri-food domain. Participants will be given the opportunity to ask questions and begin actively thinking about AI concepts within the context of their own work. Resources for follow-on guidance and support will also be highlighted.
scryptIQ Supervised Learning Taster Session
Dr. Laurence Blackhurst (scryptIQ); Dr. Adam Lee (scryptIQ, University College London)
In this brief taster session, we will demonstrate a complete workflow for training supervised machine learning models to make accurate, generalisable predictions using a real-world biological dataset. We will guide participants through the full sequence of steps, from cleaning and pre-processing raw data for machine learning analysis, all the way through to optimally training multiple classification models, generating predictions and evaluating the performance of trained models. We aim to show how classical machine learning methods remain a fast, efficient and accurate way to predict on biological data.
Funding at the Interface: Tips for writing grants for AI and Biosciences research
Prof Andrew French (University of Nottingham, AIBIO-UK lead)
Securing research funding is a challenge in the current climate; securing interdisciplinary funding can be even more difficult. In this session Prof Andrew French will share some tips on writing interdisciplinary funding applications and building collaborative teams for interdisciplinary projects.
MORF-BIO Demonstration
MORF-BIO Team
MORF-BIO is a web-based bioinformatics platform (“Multi-Omics Research Factory”) that enables researchers to explore, visualise, and interpret complex multi-omics and bioprocess data without coding, turning biological data into actionable insights through intuitive, collaborative tools. Find out more here: https://morf-bio.com/
Galaxy for AI in Bioscience: Tools, Training, and Reproducible Workflows
Prof Krzysztof Poterlowicz (University of Bradford); Dr Khaled Jumah (University of Bradford)
Artificial intelligence is increasingly utilised in bioscience, particularly for high-dimensional omics data, bioimaging, and structural biology. However, access to tools, infrastructure, and training remains a significant challenge. This session will introduce Galaxy as an open-source, web-based platform that enables researchers to implement AI methods in a reproducible and user-friendly environment.
A whirlwind tour of the FAIR principles
Dr Evgenij Belikov (EPCC, The University of Edinburgh)
In this session, based on Ed-DaSH materials, we will look at applying the FAIR principles in practice to make your data and code more findable, accessible, interoperable and reusable. Feel free to bring your own projects.
Biologically-Literate Feature Selection
Dr Andrew Mason (University of York)
As increasingly complex and sophisticated methods become available and relatively easy to implement it is easy to dismiss the major determinant of a successful AI or ML driven project: your starting data. Illustrated by examples from ongoing cancer research projects, this talk will explore the impact of data composition, feature selection and integration of multiple data modalities on the identification of clinically-actionable disease subgroups.
AI for Multiomics
Dr Alessandra Vigilante (Kings College London)
Artificial intelligence is reshaping how we work with omics data, moving the field from traditional, rule-based pipelines toward AI-driven approaches. This shift is visible across the board: from established methods of SNP analysis to newer models such as AlphaGenome, and from manual coding to AI-assisted development.
This session is split between a short talk and a live demonstration. The talk will outline this changing landscape, what’s driving the move to AI-based tools and what it means for everyday omics workflows. The demonstration will then put these ideas into practice. Attendees are welcome to follow along on their own laptops or simply watch.
No prior experience with AI tools is required.
Poster Session Details
| Group A | Group B | Group C | Group D |
|---|---|---|---|
| A1. BioCOMPASS: Integrating Biomarkers into Transformer-Based Immunotherapy Response Prediction Sayed Hashim (University of York) |
B1. From Biomass Waste to High Value Chemicals: Building Microbial Terpenoid Production Platform from Lignin Arpit Jindal (University of Warwick) |
C1. Making the invisible visible: Reconstructing the recovery of microbial communities associated with domesticated tree species in the Amazon rainforest Lay Dewi (University of Oxford) |
D1. Machine Learning Prediction of Bulk Density and Saturated Hydraulic Conductivity in Regenerative and Conventional Upland Grassland Catchments Adeyemi Olasore (Heriot-Watt University) |
| A2. AIRBDS: Quantifying AI-readiness in bioscience data Charlie Harrison (AIBIO-UK) |
B2. Targeting a Noncanonical Site on MraY: Naphthylisoquinoline Alkaloids from Ancistrocladus tectorius for Antibacterial Discovery Baiq Nila Sari Ningsih (University of Warwick) |
C2. Cross-recognition and neutralization of eight American snake venoms by camel antibodies produced using new immunization strategies Santiago Tabares (Liverpool School of Tropical Medicine) |
D2. Influence of Surface Morphology on Gas Bubble Nucleation and Evolution in AEM Electrolysers VSSL Prasad Talluri (Newcastle University) |
| A3. Cartographic imprinting distance predicts influenza neutralisation responses in human serological data Aishwarya Bhatta (University of Warwick) |
B3. From patients to microphysiological systems: dissecting virulence, host responses and antifungal resistance in vulvovaginal candidiasis Emily Jones (Quadram Institute Bioscience) |
C3. High throughput isolation of Lewy body affected neurons from human brain using laser capture microscopy Grace Thomson (University of Exeter) |
D3. MetaboViz: A Zero-Install Three-Tier Browser Platform for Interactive AI-assisted Genome-Scale Metabolic Modelling Tamoghna Das (Loughborough University) |
| A4. Mask-conditioned synthetic prostate MRI volumes using a denoising diffusion probabilistic model Hazrat Ali (University of Stirling) |
B4. Lignin Biomass Valorisation via Novel Styrene Pathways Indra Kurniawan Saputra (University of Warwick) |
C4. A Neural Field Theory Framework for EEG Inversion: Connecting Dynamics with Data Jehan Alswaihli (University of Bath) |
D4. Top-Down Engineering of Self-Organising Systems James Stovold (University of York) |
| A5. Generative AI-Assisted Annotation for the Automated Counting and Sizing of Faba Bean Root Nodules Murat Kaya (University of Lincoln) |
B5. Discovery and functional characterisation of novel natural therapeutics Taimoor Khan (University of Warwick) |
C5. Neural-stromal crosstalk in tumour microenvironment Anthea Hiu Ching Ma (University of Sheffield) |
D5. Repurposing Apache Airflow for Test Case Orchestration Hui Shi (Adaptix) |
| A6. Predicting Biological Nitrogen Fixation in Faba Bean Using Mobile Robot–Derived Multispectral Data Murat Kaya (University of Lincoln) |
B6. Comparing ecological trait-based and biological language model approaches for microbiome engineering Hester van Schalkwyk (Imperial College London) |
C6. P-3 is a novel, selective DHCR24 inhibitor that kills ccRCC cells by ER-derived ferroptosis Priyankush Choudhury (Barts Cancer Institute (QMUL)) |
D6. Uncovering the Transcriptome Basis of Nickel Accumulation and Hyperaccumulation Alexander Marks (University of York) |
| A7. Using Interpretable Machine Learning to Infer Form, Fate and Function during Organ Morphogenesis August Weinbren (UCL) |
B7. PHI-base: AI-assisted biocuration and FAIR pathogen-host interaction knowledge for agricultural and human/animal pathogens Hsin-Yu Chang (Rothamsted Research) |
C7. The impact of Healthy Ageing on the Neurobiological basis of memory Suzan Elsheikh (Teesside University) |
|
| A8. The Hub for Applied Bioinformatics (HAB): Supporting the Bioinformatics Community Through Training, Collaboration, and Community Building Alessandra Vigilante |
B8. Analysing Volatile Compounds: A Comparison of Plant-Based Meat analogues and Beef Wardah Almansaf (University of Nottingham) |
C8. Reuniting biology and machine learning: pan-cancer discovery of actionable disease mechanisms Andrew Mason (University of York) |
About the Organisers
UKRI Digital Research Skills Catalyst A UKRI-funded national initiative providing a central hub for digital research training, connecting researchers to high-quality learning resources, workshops, and expert support to develop skills in data science and computational research. Find out more here: https://digitalskillscatalyst.ac.uk/
AIBIO-UK A BBSRC-funded network that brings together the AI and bioscience communities across the UK, supporting collaboration through events, pilot funding, and shared resources to advance the use of AI in biological research. More information on their future funding opportunities can be found here: https://aibio.ac.uk/.