Events

Forthcoming events

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

C2D3 Computational Biology Annual Symposium 2024
C2D3 event

C2D3 Computational Biology Annual Symposium 2024

Wednesday, 15 May 2024, 9.45am to 5.00pm

We warmly invite you to the C2D3 Computational Biology Annual Symposium 2024!

This event is open to everyone in the Computational Biology Community.

Packaging and Publishing Python Code for Research workshop
University of Cambridge event

Packaging and Publishing Python Code for Research workshop

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

Core Statistics using R (In person)

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

7th Cambridge International Conference on Machine Learning and AI in (Bio)Chemical Engineering

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
C2D3 event

AI and Statistical Innovations for Palaeoecological Research - 5 day 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.

POSTPONED to 30 April: The UK AI Safety Institute

Tuesday, 23 April 2024, 2.00pm to 3.00pm
Speaker: Nitarshan Rajkumar (University of Cambridge & UK AI Safety Institute)
Venue: Lecture Theatre 2, Computer Laboratory, William Gates Building

This talk will present an overview of efforts the UK government has been taking on AI over the past year, including the AI Research Resource, the AI Safety Summit, and with a focus on the AI Safety Institute (AISI). AISI is the world’s first state-backed organization focused on advanced AI safety for the public benefit, and is working towards this by bringing together world-class experts to understand the risks of advanced AI and enable its governance.

"You can also join us on Zoom":https://cam-ac-uk.zoom.us/j/92041617729

Discussing the Stanford AI Report

Wednesday, 24 April 2024, 12.00pm to 1.30pm
Speaker: Bruno Mlodozeniec, Julien Horwood, Runa Eschenhagen
Venue: Cambridge University Engineering Department, CBL Seminar room BE4-38.

This week's reading group session will discuss the recently released Stanford AI Report, available at https://aiindex.stanford.edu/report/

Discussing the Stanford AI Report

Wednesday, 24 April 2024, 12.00pm to 1.30pm
Speaker: Bruno Mlodozeniec, Julien Horwood, Runa Eschenhagen, University of Cambridge
Venue: Cambridge University Engineering Department, CBL Seminar room BE4-38.

This week's reading group session will discuss the recently released Stanford AI Report, available at https://aiindex.stanford.edu/report/

Statistics Clinic Easter 2024 I

Wednesday, 24 April 2024, 5.30pm to 7.00pm
Speaker: MR5 at the CMS
Venue: MR5

This free event is open only to members of the University of Cambridge (and affiliated institutes). Please be aware that we are unable to offer consultations outside clinic hours.

If you would like to participate, please sign up as we will not be able to offer a consultation otherwise. Please sign up through the following link: https://forms.gle/F1RuUcbdL9yGU3Gz7. Sign-up is possible from Apr 18 midday until Apr 22 midday or until we reach full capacity, whichever is earlier. If you successfully signed up, we will confirm your appointment by Apr 24 midday.

Tagging Anglo-Saxon Stone Sculptures Using Multi-Label Image Classification ML Techniques

Thursday, 25 April 2024, 2.00pm to 3.00pm
Speaker: Zeynep Aki - RSE, University of Durham
Venue: West 2, West Hub

This project involves developing a machine learning model to automatically classify images of Anglo Saxon Stone Sculptures based on their features, referred to as "tags". The aim is to have a model that can accurately identify various characteristics from these sculptures, such as animals, patterns, and architectural details, in images it has not seen before.

The process begins with data preparation, where images and associated metadata are standardized to ensure uniformity and relevance. This step involves converting images to a common format, organizing them systematically, and refining the metadata to align with the model's needs. This preparation is crucial as it directly impacts the model's ability to learn and generalize from the training data.

Following data preparation, the project employs Convolutional Neural Networks (CNNs) for the training phase. CNNs are chosen for their effectiveness in image recognition tasks. The training involves adjusting the model to identify and learn from the patterns and features in the training dataset. This includes resizing images for consistency, specifying model architecture with layers designed for feature extraction and classification, and selecting optimization and loss functions appropriate for a multi-label classification task.

This project showcases the potential of applying advanced machine learning techniques to cultural heritage preservation, offering a novel tool for cataloging and studying historical artifacts. It illustrates how technology can aid in the detailed analysis of cultural artifacts, providing deeper insights and facilitating easier access to information about our historical heritage.

View all talks...