Events

Forthcoming events

This page lists C2D3 events, University events, as well as related external conferences and events of interest to our members.

AI UK 24 Fringe
External event
Monday, 4 March 2024, 9.00am to Friday, 29 March 2024, 4.00pm

At a series of events between 4 and 29 March,

With great power comes great responsibility
University of Cambridge event
Tuesday, 19 March 2024, 5.00pm to 6.00pm

What comes to mind first when you think of artificial intelligence (AI)? Perhaps self-driving cars or maybe Netflix recommendations. AI takes many forms, but one thing is sure, it has the potential to transform almost all the areas of society: supporting cancer diagnostics, predicting droughts and hurricanes, and even driving the Mars rovers. Yet, it isn’t rocket science and is more accessible than you think. Who knows, in the coming years, you may even leverage AI yourself to change the world.

How will AI affect the democratic process?
University of Cambridge event
Wednesday, 20 March 2024, 6.00pm to 7.30pm

With elections due in many countries in 2024, including the UK, a panel of experts - Dr Ella McPherson, Associate Professor of the Sociology of New Media and Digital Technology at the University of Cambridge; Dr Melisa Basol, Research manager at Moonshot, a social impact business focused on ending online harms; Jonnie Penn, best-selling author and AI researcher at Cambridge; and journalist and author Chris Stokel-Walker - will discuss what role AI will play and how we mitigate any risks.

The Meta Lab: Accelerating learning with AI and VR
University of Cambridge event
Saturday, 23 March 2024, 10.00am to 4.00pm

The award-winning Meta Lab uses emerging digital technology in innovative ways to create transformational educational experiences. Come and meet Lab Director, Dr Chris Macdonald, and experience a pioneering VR Public Speaking project. On the new platform, students can steadily build resilience through a self-paced gamified journey where the virtual ‘audience’ increases as tasks are completed. The platform leverages the massive potential of virtual reality exposure therapy.

Functional genomics and AI: super sleuths in the search for new therapies
University of Cambridge event
Tuesday, 26 March 2024, 3.30pm to 4.30pm

What is functional genomics and why is AI (artificial intelligence/machine learning) key to its interpretation? To find out this and more, join the Milner Therapeutics Institute as we take you on a tour through these new areas of medical research. Be ready to encounter big data, big ideas, big models and big collaborations!

Full details: https://www.festival.cam.ac.uk/events/functional-genomics-and-ai-super-sleuths-search-new-therapies

University of Cambridge event
Wednesday, 17 April 2024, 2.00pm to 4.00pm

Have you thought about using AI in your research but aren’t sure how to get started? Or are you already using AI and have run into challenges with implementation? Come and meet the Accelerate team to find the support you need.

AIUK 24
External event
Friday, 19 April 2024, 10.00am to Saturday, 20 April 2024, 4.20pm

The UK’s national showcase of data science and artificial intelligence (AI)

Hosted by The Alan Turing Institute, AI UK 2024 will be an in-depth exploration of how data science and AI can be used to solve real-world challenges. Our diverse programme has been thematically structured around the latest innovations from across the AI ecosystem. With a broad range of interactive content, expect to hear the latest thinking on fundamental AI, digital twins, algorithmic bias, AI ethics – and much more.

Packaging and Publishing Python Code for Research workshop
University of Cambridge event
Wednesday, 1 May 2024, 9.00am to 5.00pm

Would you like to learn how to package and share your code? The Accelerate Programme are planning a one day workshop to equip researchers with knowledge of workflows and tools they can use to package and publish their code. Participants will have the opportunity for hands on experience packaging and publishing a project.

University of Cambridge event
Monday, 13 May 2024, 9.30am to Wednesday, 15 May 2024, 5.00pm

This award winning course is intended to provide a strong foundation in practical statistics and data analysis using the R software environment. The underlying philosophy of the course is to treat statistics as a practical skill rather than as a theoretical subject and as such the course focuses on methods for addressing real-life issues in the biological sciences.

There are three core goals for this course:

7th Cambridge International Conference on Machine Learning and AI in (Bio)Chemical Engineering
University of Cambridge event
Tuesday, 2 July 2024, 10.00am to Wednesday, 3 July 2024, 5.00pm

02-03 July 2024
Main conference In person-only event

Paleo workshop
University of Cambridge event
Monday, 8 July 2024, 9.00am to Friday, 12 July 2024, 5.00pm

Co-organisers: Dr. J. Andrés Christen (CIMAT), Dr. Maarten Blaauw (Queen's University Belfast), Dr. Joan-Albert Sánchez-Cabeza (UNAM), Dr. Ana Carolina Ruiz Fernández (UNAM) and Dr. Lysanna Anderson (USGS)

Welcome to the PaleoStats Workshop: AI and Statistical Innovations for Palaeoecological Research

Forthcoming talks

A collation of interesting data science talks from across the University.

Towards a Unified Model of Contrast Sensitivity

Tuesday, 19 March 2024, 3.00pm to 4.00pm
Speaker: Maliha Ashraf, University of Cambridge
Venue: SS03 - William Gates Building

CSFs represent the human visual system’s ability to detect contrast variations and have found important applications in engineering, where they can be used to optimise designs to cater to human perceptual limits. A comprehensive CSF model requires consideration of stimulus parameters, including spatial and temporal frequencies, luminance, and colour, among others. Despite an extensive collection of contrast sensitivity measurements in the literature, no current model covers the full stimulus parameter space. The inception of ModelFest project more than two decades ago marked a pivotal moment in the research towards a unified visual detection modelling approach (Carney et al., 1999). The ambitious initiative laid the foundation for integrating diverse stimuli measurements under a cohesive framework. For CSF modelling specifically, the physiological models from Barten (1999) and the analytical Pyramid of Visibility models (Watson and Ahumada, 2016; Watson, 2018) have been key advancements in the research area. Our work on CSF during the last few years has been inspired by these approaches. The study by Wuerger et al. (2020) marks the beginning of our work on modelling CSF. This work emphasises the importance of colour modulations alongside spatial frequency and luminance. The work by Mantiuk et al. (2020) extends this framework to include background chromaticity effects and compared cone contrast and post-receptoral contrast encodings. In the proposed stelaCSF model (Mantiuk et al., 2022), achromatic contrast sensitivity was modelled by synthesising 11 distinct datasets. This work aimed for a robust and generalised model that could predict sensitivity across spatial and temporal frequencies, luminance, size and eccentricity. The latest iteration of our work, the castleCSF model (Ashraf et al., 2024), combines the strengths of preceding studies and uses datasets from 18 studies to predict sensitivity to spatial and temporal frequencies, any arbitrary contrast modulation direction in the colour space, mean luminance and chromaticity of the background, eccentricity, and stimulus area with a mean error of 3.59 dB. One major feature of our model, distinguishing it from other current works, is its use of the same set of parameters to explain data from 18 different studies, demonstrating its robustness and generalisability. This model offers insights into the mechanisms affecting contrast sensitivity for different stimulus parameters using an analytical modelling approach informed by known behaviour of physiological components governing contrast sensitivity.

Zoom link: https://cam-ac-uk.zoom.us/j/88955492403?pwd=WFFnTGxBaXBiSG1rSFNiWFZsV1JI...

Neural likelihood-free inference

Wednesday, 20 March 2024, 11.00am to 12.30pm
Speaker: Yanzhi Chen, University of Cambridge
Venue: Cambridge University Engineering Department, CBL Seminar room BE4-38.

Likelihood-free inference (LFI) is a technique for Bayesian inference in implicit statistical models. Such models have wide application in science and engineering, from inferring the R-value of an epidemic, to analyzing a stochastic volatility model. In this reading group, we will provide an introductory overview on LFI, covering methods, use cases and challenges. Specifically, we will focus on recent neural network-based methods. No preliminary knowledge is assumed.

No reading is required, but the following materials may be useful:

[1] Neural posterior estimate: https://arxiv.org/abs/1605.06376, NeurIPS 2016

[2] Neural likelihood estimate: https://arxiv.org/abs/1805.07226, AISTATS 2019

[3] Neural sufficient statistics: https://arxiv.org/abs/2010.10079, ICLR 2021

[4] Review: https://www.pnas.org/doi/10.1073/pnas.1912789117, PNAS 2020

Psychophysical tests of human visual encoding models

Thursday, 21 March 2024, 2.00pm to 3.00pm
Speaker: Prof. Thomas S. A. Wallis, Technical University of Darmstadt
Venue: SS03 - William Gates Building

The human visual system compresses the information about the world implicit in the light entering our eyes. Decades of research in vision science has provided good hypotheses for the features that are encoded by the early visual system and made available for cognition and action. One approach to testing these hypotheses uses analysis by synthesis: one can generate artificial image stimuli that should differentiate competing encoding accounts, or for which an encoding account makes a strong prediction about discriminability. A classical example from vision is colour metamerism. Two spectrally-distinct surfaces will appear to be the same colour as long as the ratios of cone activations are identical (and context is comparable). I will present work extending this concept to the discriminability of photographic scenes. I will show examples from past work in which we used this logic to psychophysically test a popular analogy for vision in the periphery, as that of a "texture-like" representation. We find two extant models fail to adequately capture image discriminability, and we speculate about what ingredients might be missing. Ongoing work extends this using a data-driven approach, and expands to test other models. Overall, classical psychophysical methods combined with hypotheses from vision science and modern tools in image synthesis provide a powerful approach to test the functional encoding of visual information.

Zoom link: https://cam-ac-uk.zoom.us/j/84318599913?pwd=WmxmYXpMSCtzeG0rakdaZzZ6Z2R5...

A Privacy-Preserving Architecture and Data-sharing Model for Cloud-IoT Applications

Thursday, 21 March 2024, 3.00pm to 4.00pm
Speaker: Dr Jenjira Jaimunk, Department of Computer Engineering, Chiang Mai University
Venue: FW11

Many service providers offer their services in exchange for users' private data. Despite new regulations created to protect users' privacy, users are often given little choice over the way their data is collected and used. To address privacy concerns in cloud-IoT applications, this study proposes to use an architecture, called Data Bank, which gives users fine-grained control over their data. Data Bank uses a category-based data access (CBDA) model which covers the whole data life-cycle, from data collection from IoT devices to data sharing with services. It shows how dynamic policies can be specified using a new attribute-based instance of CBDA, and describe the use of policy graphs to visualise and analyse policies.

https://ieeexplore.ieee.org/document/9880537

Short bio:
Dr Jenjira Jaimunk was a PhD student at King’s College London and is now a lecturer at the Department of Computer Engineering, Chiang Mai University. She is interested in
research on privacy for cloud-IoT platforms and privacy by design. Her research looks at designing an architecture to improve the process of disclosing information on individuals while min*imising the breach of user privacy.

Tech law vs tech design: why can't we be friends?

Friday, 22 March 2024, 3.00pm to 4.00pm
Speaker: Dr Tristan Henderson, University of St Andrews
Venue: FW26

Modern technologies seem to bring almost as many harms as benefits, and
legislators are rolling out myriad new regulations to mitigate such harms.
The EU for instance has recently introduced the Digital Services Act, Digital
Markets Act, AI Act, Data Act and many more. Such technology laws are often
intended to be _technology neutral_ in an attempt to ensure that they have
broad application but also do not quickly go out of date. But if laws are
truly technology neutral, then why do we need so many?

In this talk I will first look at technology neutrality from a legal
perspective. Then, with my computer science hat on, I will look at how
systems designers build long-lived systems, which often have similar design
aims as long-lived laws. I will attempt to show what designers of technology
laws could learn from designers of technology systems, and vice versa. I will
then discuss some ongoing work trying to leverage similarities between the
two disciplines.

Bio: Tristan Henderson is a Senior Lecturer in Computer Science at the University
of St Andrews, where he is meant to be in charge of Postgraduate Research and
the Responsible Computing Research Group (insomuch as anyone can be in charge
of anything in academia). His current research interests revolve around the
intersection between computer science and law, with a particular focus on
digital rights. Tristan has an MA in Economics, an MSc and PhD in Computer
Science and an LLM in Innovation, Technology and the Law, which perhaps
explains why he is so confused about interdisciplinary work. For more see
https://tnhh.org/