Section page Events from around the Turing Network Conferences, workshops, and other events from around the Turing Network Events from around the Turing Network
The Turing Lectures A series of inspiring talks by leading figures in data science and AI The Turing Lectures
Impact The Turing works with a range of partners with far-reaching, real-world impact across sectors Impact
The Turing Alphabet Introducing the Turing Alphabet: demonstrating the breadth of the Institute. The Turing Alphabet
Research projects Turing Research and Innovation Cluster in Digital Twins Turing Research and Innovation Cluster in Digital Twins
Research projects AI for Numerical Weather Prediction (NWP): AI4NWP Developing artificial intelligence algorithms to fundamentally transform weather forecasting and the prediction of local and global weather events AI for Numerical Weather Prediction (NWP): AI4NWP
Event PhD Connect 2024 Thursday 21 Nov 2024 - Friday 22 Nov 2024 Time: 09:00 - 18:00 Claire Morris Piotr Mirowski Beatriz Costa Gomes Christopher Windows-Yule Alexandros Zenonos Francisco Gomez Medina Mishka Nemes PhD Connect 2024
Research spotlight Premdeep Gill Enrichment student Premdeep Gill is studying Antarctic seals and their sea ice habitats through satellite data, to better understand how they are coping with climate change Premdeep Gill
Research spotlight Erin Young As co-lead of the Turing’s Women in Data Science and AI project, Research Fellow Erin Young’s vital research maps the gendered career trajectories in data science and AI Erin Young
Why work at the Turing? Find out some of the reasons why the Turing is a competitive employer Why work at the Turing?
Data Study Groups Events bringing together some of the country’s top talent from data science, artificial intelligence, and wider fields, to analyse real-world data science challenges Data Study Groups
Partnering with the Turing We work with a wide range of partners to help deliver our mission of changing the world using data science and artificial intelligence Partnering with the Turing
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Towards Autonomous Robotic Systems Conference (TAROS) Wednesday 03 Jul 2019 - Friday 05 Jul 2019 Time: 09:00 - 18:00
Geospatial Standards for Data Science Tuesday 12 Mar 2019 Time: 14:30 - 17:30 Dr Paul Cripps Dr. Gobe Hobona
Palaeoanalytics Using data science and machine learning to develop cross-disciplinary analytical methods in human evolutionary studies
Bringing the social city to the smart city Using AI and machine learning to understand and simulate cities
Topological hierarchies in complex image data Developing new techniques for understanding complex image data to enable and advance biological discovery
From field data to global indicators Investigating how plant data can be efficiently and reliably linked across data infrastructures around the world
Measurement theory for data science and AI Modelling the skills of learning machines and developing standardised benchmark tests
Health and medical sciences Accelerating the scientific understanding of human disease and improving human health through data-driven innovation in AI and statistical science
Research publications Anomaly detection in networks with application to financial transaction networks This paper is motivated by the task of detecting anomalies in networks of financial... A. Elliott, M. Cucuringu, M. M. Luaces, P. Reidy, and G. Reinert, Anomaly detection in networks with application to financial transaction networks, (arXiv 1901.00402) (2019)
Towards Autonomous Robotic Systems Conference (TAROS) Wednesday 03 Jul 2019 - Friday 05 Jul 2019 Time: 09:00 - 18:00
Geospatial Standards for Data Science Tuesday 12 Mar 2019 Time: 14:30 - 17:30 Dr Paul Cripps Dr. Gobe Hobona
Interpretation, modelling and verification of information How data science and artificial intelligence help people understand/handle information correctly?
Learning the organisational principles of living systems Can machine learning and artificial intelligence help us to discover and understand biological principles?
Machine learning for radio frequency applications How do we generate robust, real-time machine learning algorithms with integrated hardware and software for radio frequency applications?