UKRI Inter AI CDT Conference 2023

Building on the success of last year's event, the UKRI Inter AI CDT Conference is taking place on the 30th and 31st October 2023 at the Bristol Hotel on Bristol Harbourside.


Building on the success of last year’s event, the UKRI Inter AI CDT Conference is taking place in person on the 30th and 31st October 2023 at The Bristol Hotel on Bristol Harbourside. Students, academics and industry representatives associated with the ART-AI CDT based at the University of Bath, the Interactive AI CDT based at the University of Bristol and the Foundational AI CDT based at UCL will come together for this 2 day conference with the aim of enabling collaboration as well as delivering pertinent sessions.

Our key note speakers are Jasmine Grimsley & Sarah-Jane Smyth from The London Data Company on day 1, and Jakob Zeitler Founder of Matterhorn and Steven Schockaert from Cardiff University on day 2.

On each conference day there will be two sessions running in parallel in both the morning and afternoon. Upon registration, participants will be asked to sign up to the session of their choice prior to the event. Please note, we will do our best to accommodate first choice of sessions for the conference, however there is a limit on numbers per session, so places will be offered on a first come first served basis.

At the end of day 1, there is a poster competition for students at the MShed, which is a short walk from The Bristol Hotel. All students are encouraged to participate. If you would like to submit a poster, please e-mail your poster to your CDT by 12 noon Monday 9th October 2023, and we will print it out on your behalf. There will be a prize giving ceremony at the conference dinner at the end of day 1 with a prize of a £30 voucher for the best poster from each CDT voted by a panel of judges.

For a detailed event programme, information on all the sessions and information on how to get to The Bristol Hotel please see below. To register and select the sessions you wish to attend, please click on the ‘register’ button and fill out the form. Registration deadline: Monday, 9th October 2023, 12 noon.

Event Programme

Day 1 – Monday 30th October 2023

08:30 Registration, tea/coffee, breakfast roll & networking – Meeting & Events Lobby

09:20 Welcome to Day 1 – Gabriel Brostow, Professor in Computer Science at UCL – Ballroom

09:30 Keynote Speakers – Jasmine Grimsley & Sarah-Jane Smyth, The London Data Company – Ballroom

10:30 Refreshment break & networking – Meeting & Events Lobby

Morning sessions

11:00 AI Vision talks – The William Jessop Suite

11.00 Reproducibility – Ballroom

13:00 Lunch & networking – Meeting & Events Lobby

Afternoon sessions

14:00 NLP talks – The William Jessop Suite

14:00 Bristol student-led session: ChatGPT search in own data – Ballroom

16:00 Poster session, pre dinner drinks & networking at the MShed

18:30 Conference Dinner & Prize Giving for poster session – Ballroom

Day 2 – Tuesday 31st October 2023

09:00 Registration, tea/coffee & networking – Meeting & Events Lobby

09:20 Welcome to Day 2 – Eamonn O’Neill, Head of Computer Science Department & ART-AI CDT Director, University of Bath – Ballroom

09:30 Keynote Speaker – Jakob Zeitler, Founder of Matterhorn – Ballroom

10:30 Refreshment break & networking – Meeting & Events Lobby

Morning sessions

11:00 AI in energy and environment – The William Jessop Suite

11:00 Bayesian tutorial Ballroom

13:00 Lunch & networking – Meeting & Events Lobby

Afternoon sessions

14:00 Bath student-led session: BCI workshop with NeuroCONCISE – Ballroom

14:00 UCL student-led session: Making AI Safe – The William Jessop Suite

15:30 Keynote Speaker – Steven Schockaert, Professor of Artificial Intelligence at Cardiff University – Ballroom

16:30 Close – Peter Flach, Professor of Artificial Intelligence & Interactive AI CDT Director, University of Bristol – Ballroom

17:00 Refreshments and networking – Meeting & Events Lobby

Information about the sessions

Day 1 – Morning

AI Vision – The William Jessop Suite

Prof Lourdes de Agapito (UCL) will be introducing 3 keynote speakers discussing their research in the field of AI Vision. 

Christian Rupprecht (University of Oxford)


Unsupervised Computer Vision in the Time of Large Models.

With larger and larger models trained on billions of images (and sometimes text) entering the research landscape of computer vision is changing. The lines between unsupervised, few-shot and supervised learning are becoming blurry as using these larges models introduces information at a scale that is very difficult to assess and categorize. In this talk we will analyse the current state of the field, future research directions and some current practical applications with and without the use of large models.


After completing his BSc and MSc at the Technical University of Munich, Christian Rupprecht obtained his PhD in 2018 advised by Nassir Navab (Technical University of Munich) and Gregory D Hager (Johns Hopkins University). He joined Oxford University first as a PostDoc in Engineering Science with Andrea Vedaldi, then as a Departmental Lecturer in Computer Vision, and now an Associate Professor in Computer Science and Tutorial Fellow at Magdalen College.

Laura Sevilla-Lara (University of Edinburgh)

Ed Johns (Imperial College London)

Reproducibility – Ballroom

Best Practices in Research Software Engineering and ML

Presenters – Christopher Woods and Daladier Sampaio Neto (University of Bristol)

Sharing your scripts or code with others can be scary. It may run on your computer today, but how can you trust that it will work correctly somewhere else or at another time? We can raise similar questions about machine learning models or experiments. How to ensure that the experiments results are reproducible? How to track the best models from multiple experiments? How to keep track of a deployed model? We’ll share some of the best tactics in research software engineering and ML-Ops that will give you the confidence to share your software and model results with others. We will show you how you can trust that your code will give the same answers on other computers or at future points in time. We’ll reveal the best practices that will help you turn the code you write into software or models that can be shared and developed by your research community.

Day 1 – Afternoon

NLP talks – The William Jessop

Chaired by Dr Harish Tayyar Madabushi, University of Bath. Confirmed speakers:

  • Elena Kochkina, JPMorgan AI Research
  • Michael Schlichtkrul, University of Cambridge

Bristol student-led session – Ballroom

ChatGPT search in own data

An application and workflow that enables natural language search and synthesis own data through:

  • preprocessing of local text repositories
  • local filtering of relevant text through keyword
  • search in subset of highest ranking documents with ChatGPT

The workshop will include a presentation of the main concepts, a demonstration, a practical hands-on session, and a discussion on risks and opportunities for deployment in organisations.

Day 2 – Morning

AI in energy and environment

Chaired by Professor Lorraine Whitmarsh, University of Bath. Three invited speakers will talk for 25 minutes each, with questions from the floor, about how they use AI to address environmental issues/questions in their work. This will be followed by a wider panel discussion on future directions and challenges for AI and the environment. Confirmed speakers:

  • Andrew Barnes, University of Bath: Rain forecasting
  • Jatinder Mehimi, Environment Agency
  • Travis Coan, University of Exeter: Big data analysis of social media

Bayesian tutorial

Chaired by Dr Jeremias Knoblauch – Generalised Bayesian methods for machine learning

In this tutorial, I summarize a recent line of research and advocate for generalisations of Bayesian inference. The main thrust of this argument relies on identifying a tension between the assumptions motivating the standard Bayesian posterior on the one hand, and the realities of modern large-scale Bayesian machine learning on the other hand. Generalised Bayesian posteriors are useful both conceptually and practically: they can address various challenges that arise with the standard Bayesian paradigm in the context of machine learning including robustness to model misspecification, poorly chosen priors, computational challenges, and insights into the links between variational and ensemble methods.

Day 2 – Afternoon

UCL student-led session

Making AI Safe

An interactive session brainstorming the risks of AI and strategies to mitigate them with Reuben Adams & Robert Kirk

Do you think AI is going to kill us all, or do you think there’s nothing to worry about? Somewhere in between? Come to this interactive session where you’ll try and understand in more detail what you think will happen. Working with others, you’ll brainstorm risks from AI, propose solutions and then try to find ways in which those solutions won’t work. Through this “builder-breaker” workshop you’ll build a more detailed understanding of the potential risks from AI and how optimistic you are about solutions to them.

Bath student-led session

BCI workshop with NeuroCONCISE (Details TBC)

Keynote Speakers – Day 1

Jasmine Grimsley & Sarah-Jane Smyth

Title: Data for Good: Delivering Ethical and Sustainable Data Driven Insights

Abstract: This presentation from The London Data Company (LDCo) offers an examination of ethical and sustainable dimensions of AI solutions. Introducing practical methodologies for curbing environmental impact, fostering trust, and actively participating in a future where AI contributes positively to societal and ecological challenges.

Sarah-Jane Smyth and Jasmine Grimsley will demonstrate the impacts of AI technology on business processes and data for decision making insights. Jasmine and Sarah-Jane’s careers in data come from very different paths but they have come together to champion Ethical and Sustainable Data Driven Insights through a shared understanding of its value. Prior to her role as Chief Data Officer and co-founder of The London Data Company Jasmine had careers in both academia and the public sector, where she led on clinical research, capability building in ethical AI nationally and internationally, and on delivering ethical insights for government on a broad range of policy areas including the COVID-19 response. Sarah-Jane is the CEO and founder of The London Data Company, prior to this she has had careers as a Royal Air Force Officer and broadly across the spectrum of delivering digital and data transformations for the Public Sector.

The current AI landscape is analogous to a modern-day Industrial Revolution, generating both positive and negative outcomes for sociality and the planet. AI driven tools are being used by The London Data Company to effectively evaluate AI and algorithms across the expansive spectrum of ethics, encompassing issues beyond personal data control, such as bias, discrimination, and privacy breaches. Including assessment for resilience for the rising threat of adversarial attacks, capable of compromising AI-generated insights, further accentuates the need for resilient and secure AI models. The establishment of trust in AI systems necessitates addressing these concerns through the development of transparent, equitable, and intelligible models. The ethical review also spotlights a less-discussed key element of data ethics, sustainability. The escalating expansion of data storage and processing, especially evident in generative AI applications, has led to elevated energy consumption and environmental impact. The notion of “dark data,” surplus and unused data contributing to environmental detriments, is explored, accompanied by strategies to mitigate this phenomenon. The imperative of considering the entire lifecycle of AI models, encompassing both training and operational phases, is emphasized to accurately assess their environmental implications.


Jasmine Grimsley (PhD) LDCo CDO and Co-Founder

Jasmine is highly skilled at solving real world problems across sectors using data and improving data science and AI capability within organisations. She is an experienced academic, educator, and data leader with a strong background in health, social, and biomedical data and data ethics.  She develops data science enabled insights and AI facilitated insights for the public and private sector.

Sarah-Jane Smyth LDCo CEO and Founder

A Digital and Data Decarbonisation Specialist. Sarah-Jane is passionate about the power the right data, correctly applied, has in transforming society. With a vision for bringing integrity to the Data Industry and creating movements for good.

Extensive experience in portfolio management, business consultancy, consulting and digital delivery, and a sought-after wealth of knowledge in both the private and government sectors. The London Data Company is Sarah-Jane’s experience put into practice, leading a team of enlightened individuals ethically applying data for the betterment of issues facing society and businesses alike.

Keynote Speakers – Day 2

Jakob Zeitler

Title: Machine Learning for Material Science: Using Bayesian Optimisation to create a sustainable materials future

Abstract: Learning a statistical model of natural phenomena is usually limited by the cost of running experiments. With machine learning, we can choose the experiments with the maximum gain of statistical information, e.g. for optimising yield of alternative protein. Bayesian Optimisation is such a popular method and has found wide applications across robotics, physics, chemistry, material design and drug development. This talk will give an overview of machine learning for optimisation with a specific focus on how to exploit expert knowledge such as molecular simulations, multiple fidelities and more to accelerate the search in a variety of applications.

Bio: Jakob Zeitler is a final year PhD at UCL’s AI Centre where he researches causal inference and efficient experimentation. At the moment he is leading an Innovate UK grant as Founder of Matterhorn that explores efficient discovery of materials using machine learning. He previously interned with Spotify Research, yielding both a paper in the field of causal inference and a patent in the field of music for education. Read more on

Steven Schockaert

Title: Distilling Conceptual Knowledge from Language Models

Abstract: Knowledge about the properties of concepts, and the relationships between them, plays a vital role in many applications. For instance, in the context of zero-shot and few-shot learning, prior knowledge about the meaning of the class labels can often counteract the paucity of the training data. Conceptual knowledge can be modelled in several ways, e.g. using symbolic logic, using natural language assertions, or using vector space encodings. In this talk, I will focus on the latter approach, and in particular on the problem of learning concept embeddings, which intuitively capture the semantic properties of concepts, and relation embeddings, which intuitively capture how concepts are related. I will give an overview of different strategies that have been proposed in recent years for distilling such representations from language models, and provide some examples of the applications they enable.

Bio: Steven Schockaert is a professor at Cardiff University, working at the intersection of Natural Language Understanding and Knowledge Representation and Reasoning. He was the recipient of the ECCAI Doctoral Dissertation Award, the IBM Belgium Prize for Computer Science, and the ACL 2023 outstanding paper award, among others. He is co-editor-in-chief of “AI Communications”, and serves on the editorial board of “Machine Learning” and of “Neurosymbolic Artificial Intelligence”.

Prince Street, Bristol, BS1 4QE

By Car:

Car parking is available for £11 per car for all guests/delegates. Guests/delegates should park in the Prince Street NCP. There are QR codes to scan in the conference centre to register cars and pay, or alternatively at the hotel reception. Please don’t pay directly at the car park machines, or by scanning the QR codes in the car park itself, as you will not receive the discounted rate, and this cannot be refunded once you have paid. Car parking spaces are available on a first come first served basis. The hotel is within the City of Bristol Clean Air Zone and older vehicles may incur a charge.  You can check your car here.

By Train

Bristol Temple Meads Train Station is a 15 minute walk from the hotel. A taxi from the station is around a 5 minute journey and a £5 fare.

By Bus

Bristol Bus/Coach Station is a 15 minute walk from the hotel. Bus service 74 from Bristol Temple Meads stops on Broad Quay (The Centre), which is a continuation of Prince Street and only a short walk from the hotel. Bus service A1 (the airport flyer) links the bus station, the train station and the airport, and stops immediately outside the hotel, but is more expensive than the 74.

Event Info

Start Date 30.10.2023
End Date 31.10.2023
Start Time 8:30am
End Time 5:00pm

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