Events 32 x 13.1 ( with space) ppt.png

Events and Talks

 

In AI, Machine Learning and Data Science across the University and beyond.

Events

Cantab Capital Institute for the Mathematics of Information – Industry Engagement Uni of Cambridge
Statistics and modelling for policy in a COVID-zero setting External
Cambridge Public Health & Department of Engineering Workshop Uni of Cambridge
Accelerate Science's 2021 Annual Symposium External
Cambridge Zero Research Symposium: AI & Sustainability Uni of Cambridge
Structured missingness workshop External
Machine learning can identify newly diagnosed patients with Chronic… Uni of Cambridge
The Turing Lectures: The science of movement External
Cambridge-Turing sessions reloaded: collaborative data science and AI research C2D3 event
The cost of data: making sense in digital society Uni of Cambridge
The Turing Lectures: What are your chances? External
Aviva & University of Cambridge Annual Partnership Showcase C2D3 event
Entrepreneurial pathways to impact: Spinning-out your research Uni of Cambridge
Applied Process Mining for Management C2D3 event
Turing Data Study Group - Applications now open External
The Alan Turing Institute - DCEng Summit External
The Alan Turing Institute - Turing trustworthy digital identity conference External
Data x Biomedical Science Summer Event Series - Tuesday 20 July 2021 External
The Alan Turing Institute Digital Twins Workshop External
The Turing Lectures - Policy fights back: Mitigating algorithmic bias in AI… External
Data x Biomedical Science Summer Event Series - Tuesday 13 July 2021 External
The Trinity Challenge - Awards Ceremony External
Cambridge-Turing sessions: collaborative data science and AI research C2D3 event
Breaking the code: Alan Turing’s legacy in 2021 External
Cambridge Public Health Conference 2021: Children and Young People’s Mental… Uni of Cambridge
Cambridge Computational Biology Institute (CCBI)​ Annual Symposium 2021​ C2D3 event
Launch Event of the Cambridge Mathematics of Information in Healthcare (CMIH) Uni of Cambridge
UKCRIC Digital Theme Workshop Uni of Cambridge
An AI revolution in science? Using machine learning for scientific discovery Uni of Cambridge
NVIDIA GTC 21 External
CAMBRIDGE FESTIVAL: Health data research and COVID-19 Uni of Cambridge
CAMBRIDGE FESTIVAL: Bias in data: How technology reinforces social stereotypes Uni of Cambridge
CAMBRIDGE FESTIVAL: AI: Hype vs reality Uni of Cambridge
CAMBRIDGE FESTIVAL: Empathetic machines: Can chatbots be built to care? Uni of Cambridge
CAMBRIDGE FESTIVAL: Artificial Intelligence and unfair bias: Addressing… Uni of Cambridge
The Turing Presents: AI UK External
Data Science Careers Fair Uni of Cambridge
The CCAIM Seminar Series - Prof. Dana Pe’er Uni of Cambridge
The Trinity Challenge Town Hall - Panel discussion External
The Trinity Challenge Town Hall - Q&A session 1 External
The Trinity Challenge Town Hall - Q&A session 2 External
The CCAIM Seminar Series - Prof. Isaac (Zak) Kohane Uni of Cambridge
AI medicine and novel drug target discovery Uni of Cambridge
The Alan Turing Institute Research programmes showcase: Urban analytics External
The Alan Turing Institute Research programmes showcase: Artificial intelligence External
IRIS Machine Learning Workshop External
The Alan Turing Institute Research programmes showcase: Defence and security External
Modelling Solutions to the Impact of COVID-19 on Cardiovascular Waiting… Uni of Cambridge
Healthcare Research Showcase - Department of Computer Science and Technology Uni of Cambridge
The Alan Turing Institute Research programmes showcase: Finance and economics External

Talks

Upcoming related talks from talks@cam

Date Title Speaker Abstract
BSU Seminar: "Estimating conditional means under missingness-not-at-random with incomplete auxiliary variables" Maya Mathur, Associate Professor, Stanford Medicine

Estimators assuming missingness at random (MAR) can fail under missingness not at random (MNAR). Introducing complete auxiliary variables sometimes restores MAR by breaking dependence between analysis variables and missingness. However, if the auxiliaries are themselves incomplete, MAR typically remains violated.

Cambridge AI in Medicine Seminar - July 2026 Mengling Feng and Kai He

Sign up on Eventbrite: https://medai-july2026.eventbrite.co.uk
BSU Seminar: "Personalized Federated Training of Diffusion Models with Local Differential Privacy" Kumar Kshitij Patel, Yale Institute for Foundations of Data Science (FDS)

Diffusion models are now the dominant approach for high-fidelity image generation, yet they remain highly vulnerable to privacy attacks, including reconstruction and membership inference attacks (e.g.,