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Events and Talks

 

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

Events

Online Social Data School: June 2025 Uni of Cambridge
Seminar Series: AI and the Digital Uni of Cambridge
Working on HPC clusters (online live training) C2D3 event
ai@cam AI Sciencepreneurship bootcamp Uni of Cambridge
AI Workflows for Literary Studies: Bridging Close and Distant Reading through Josephine Miles’ Eras and Modes in English Poetry… Uni of Cambridge
CHIA Annual Conference: Shaping the Future of AI Uni of Cambridge
Data for Policy 2025 Conference – Europe Edition External
Erlangen AI Hub Conference 2025 External
Training Workshop: LLM Hands on Workshop Uni of Cambridge
AI in Women's Health: Bridging Research and Patient Voices External
Edge AI Workshop with Qualcomm Technologies Uni of Cambridge
Training Workshop: AI & Large Language Models Uni of Cambridge
Language Models and Intelligent Agentic Systems C2D3 event
AI and human embryos Uni of Cambridge
Cambridge ELLIS Seminar Series Uni of Cambridge
Turing event: Pint of Science 2025 External
Exploring Interdisciplinary Frontiers C2D3 event
AI workshop series: LLMs Hands On workshop Uni of Cambridge
AI workshop series: Packaging and Publishing Python Code for Research Uni of Cambridge
Cambridge Enterprise: Ideas to Reality Programme Uni of Cambridge
AI workshop series: An Introduction to Diffusion Models in Generative AI Uni of Cambridge
Cambridge Multimodal Imaging Neuroscience Data hackathon Uni of Cambridge
An Introduction to Docker Uni of Cambridge
AI Cafe at CMS. Uni of Cambridge
AI workshop series: Hands On AI workshop Uni of Cambridge
AI workshop series: LLMs Hands On workshop Uni of Cambridge
AI workshop series: AI and Large Language Models Uni of Cambridge
AI for Bibliographical Record Creation: Hopes and Anxieties Uni of Cambridge
AI workshop series: Generative AI Uni of Cambridge
The AI Patent Revolution: Accelerating Entrepreneurs : Member's event External
AI for Researchers: A Beginners’ Guide Uni of Cambridge
Cambridge Enterprise: Research Tools 101 Uni of Cambridge
Cambridge Enterprise: Consultancy 101 Uni of Cambridge
AI Café: AI and Education Uni of Cambridge
Good Practices for Reproducible Open Source Code Uni of Cambridge
AI and Education Initiative Launch- Introductory Session Uni of Cambridge
Accelerate Programme for Scientific Discovery – Lent Term workshops in AI for… Uni of Cambridge
Accelerate Programme for Scientific Discovery – Lent Term workshops in AI for…
Centre for Human-Inspired AI (CHIA): Early Career Conference 2025 Uni of Cambridge
First Steps in Coding with R Uni of Cambridge
Cambridge Social Data School Q&A Uni of Cambridge
CDH Open: Digital Editing in the Age of AI | Dr James Cummings
Prof. Max Kleiman-Weiner: Computational morality
Women in Robotics
Accelerate Programme AI for Science lunchtime seminar Uni of Cambridge
Large Language Models in Practice: A Hands-On Journey from Data Collection to Insight Discovery Uni of Cambridge
Accelerate Programme for Scientific Discovery – Michaelmas Term workshops in AI for Science Uni of Cambridge
Synthetic Biology UK 2024 Uni of Cambridge
Validation data: strategies to avoid overuse (Invitation only workshop) C2D3 event
AI for Science Summit, University of Cambridge Uni of Cambridge

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.,