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Events and Talks

 

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

Events

C2D3 event Talk In person

Computational Biology: Seminar Series

8 Oct 2026 - 18 Mar 2027

C2D3 event Talk In person

Beyond Academia - BBC, Head of Applied AI

10 Nov 2026

6 Jan 2027 - 8 Jan 2027

Uni of Cambridge Workshop In person

Accelerate Programme: Michaelmas Term training workshops

14 Sep 2026 - 11 Nov 2026

11 Nov 2026

12 Nov 2026

19 Nov 2026

Uni of Cambridge Conference Hybrid

AI for Science Summit 2026

24 Nov 2026

9 Dec 2026 - 10 Dec 2026

11 Jan 2027 - 15 Jan 2027

External Conference In person

Advances in AI Conference 2027

6 Apr 2027 - 7 Apr 2027

Turing Presents: AI UK External
Neurocomputation & AI in Neuroscience Uni of Cambridge
Computation Day "Optimise, Open and Learn" Uni of Cambridge
Aviva Hackathon (CUDSS Aviva Data Science Challenge) Uni of Cambridge
C2D3 Hierarchical Modelling Workshop C2D3 event
Cambridge University Data Science Society: Delivering personalised… Uni of Cambridge
Data Science Careers Fair Uni of Cambridge
Reliability and reproducibility in computational science External
SynTech CDT networking event, Department of Chemistry Uni of Cambridge
Computational archival science (CAS) symposium: Towards a transatlantic programme External
How can your research influence policy? Uni of Cambridge
Data Profiling Workshop External
Turing Data Study Group External
FinHealthTech: New opportunities at the intersection of health and wealth. External
Fetch.ai Cambridge Winter Warmer External
CCIMI Colloquium: Mark Girolami - The Statistical Finite Element Method Uni of Cambridge
Ensembl Rest API Workshop External
What is the Future of Digitally Enabled Service Business? Uni of Cambridge
Ensembl Browser Workshop External
Cambridge Networks Day 2019
Who are the real people behind artificial intelligence?
Automating the Crowd: Workshop 2
Machine Learning for Environmental Sciences 2019
CCIMI Conference - Geometric and Topological Approaches to Data Analysis
Advances and challenges in Machine Learning Languages
Cambridge Big Data Research Symposium
Cybersecurity for Smart Infrastructure: Challenges and Opportunities
Ensembl browser workshop
Data Challenges in Cardiovascular Research
Personal Data Stores: A new approach to control of online privacy
'Scores of Scores': Possibilities and Pitfalls with Musical Corpora
Hands-off my health records: why sharing your health data matters
Cryptocurrencies and ICO : Trends and Opportunities
Big Data and personalised medicine
Manufacturing Analytics: Preliminary lessons and the way forward
Inaugural meeting for a Consortium for AI in Medicine at Cambridge
High Dimensional Big Data Engineering
Sensors and Data in Robotics
Environmental Science in the Big Data Era
An introduction to the Turing-HSBC partnership in Economic Data Science
Dodgy Data in the news: How to spot it and how to stop it
Big Data Analytics Service Forum
Big Data in Medicine: Tools, Transformation and Translation
Cambridge Networks Day 2017
The Future of Big Data Patent Analytics
National Physical Laboratory UK Workshop on Data Metrology & Standards
Digital Echoes: Understanding Patterns of Mass Violence with Data and Statistics
Scalable Data Processing for Big Data from Laptop, Multi-core, to Cluster Computing
Ethics of Big Data Workshop
Cantab Capital Institute for the Mathematics of Information - Launch Event

Talks

Upcoming related talks from talks@cam

Date Title Speaker Abstract
Beyond Academia - BBC, Head of Applied AI Nikolay Burlutskiy, Head of Applied AI, BBC

The Beyond Academia: AI & Data Science Applications seminar series at the University of Cambridge explores the transformative role of data and artificial intelligence in the world. Designed for postgraduate researchers, early-career academics, and students, the series bridges the gap between high-level research and real-world deployment.

BSU Seminar-"Hierarchical Causal Models" This is a free seminar- to attend remotely please use the following link: https://teams.microsoft.com/meet/3978637967598328?p=nw8RVurjolXhkz4sKy David Blei, Columbia University

Hierarchical Causal Models


David Blei

Columbia University


Title to be confirmed Jindřich Libovický (Charles University)


Neural networks for scientific discovery: hard-won insights Alex Matthew (cusp.ai)

Neural networks are powerful function approximators, but turning that flexibility into scientific discovery often requires a great deal of additional work. In this talk I will discuss two projects applying neural networks to partial differential equations. Although the applications and methods differ, they offer shared lessons about mathematical structure, and optimisation.


Expander Datacenters: From First Principles to the Cloud Prof. Michael Schapira, Hebrew University of Jerusalem

Abstract:

Statistics Clinic Michaelmas 2026 III

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.

Title to be confirmed Anne-Marie Lutgen (University of Luxembourg)


MRC Biostatistics Unit seminar Pawel Morzywolek, University of Copenhagen

Inference on Variable Importance for Treatment Effect Heterogeneity: Shapley Values and Beyond


How Do Language Models Reason? Foundations, Limitations and Neuro-Symbolic Approaches André Freitas (Idiap Research Institute & University of Manchester)

How do language models reason, and what would it take to build AI systems capable of sustained, complex reasoning? This lecture develops the foundations for answering these questions, connecting what we know about the internal workings of language models with methods for evaluating and extending their capabilities.


Optimising Julia code on GPUs: Case studies from ocean and atmosphere modelling Mikolaj Kowalski, Jack Franklin, David Kamm - RSEs, ICCS, University of Cambridge

Julia is a just-in-time (JIT) compiled language with a strong focus on scientific computing and performance. It has gained significant popularity in the climate science community, largely due to its flexibility and excellent native support for GPU computing. Over the last year, our team at the Institute for Computing for Climate Science (ICCS) has collaborated closely with the developers of major Julia-based ocean and atmosphere models to optimize their performance on GPU architectures.

Optiver.com


ALPS: An Efficient Request Scheduler for LLM Inference Dr Chen Chen, Nottingham Trent University

Abstract:

Aligning LLMs to the Human Brain Mariya Toneva (Max Planck Institute for Software Systems)


Title to be confirmed Bálint Gyevnár (CMU)


Amazon.com Amazon.com


Scaling Analytical Benchmarks to Production Complexity   Jan Vincent Szlang, University of Cambridge

Abstract:

Red teaming frontier AI at the UK AI Security Institute M. Pawan Kumar (AI Security Institute)

Abstract: The UK’s AI Security Institute (AISI), situated within the Cabinet Office, works to equip government with an evidenced understanding of the risks posed by advanced AI. AISI's Red Team stress-tests frontier safety measures: finding ways misuse safeguards can fail, investigating unintended and harmful model behaviours, and assessing whether protocols intended to keep models under control actually work.


Statistics Clinic Michaelmas 2026 IV

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.

Validity by Design for LLM "Reasoning" Rachel Lawrence (Microsoft Research Cambridge)


QBS - Dr Alexis Barr Dr Alexis Barr, Imperial College London

Title: Discovery research for better cell cycle targeting in cancer