Research

Research from the University of Cambridge is accelerating progress in AI’s core technical capabilities, enabling the application of AI technologies across sectors and disciplines, and building understandings of their impact and the levers to promote their ethical development.

Find out more about our research and innovation community

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How sure is sure? Incorporating human error into machine learning

Researchers are developing a way to incorporate one of the most human of characteristics – uncertainty – into machine learning systems.

Is Data Justice key to Climate Justice?

Biased artificial intelligence needs human help to avoid harmful climate action, Cambridge researchers say.

Machine learning models can produce reliable results even with limited training data

Researchers have determined how to build reliable machine learning models that can understand complex equations in real-world situations while using far less training data than is normally expected.

AI-driven techniques reveal new targets for drug discovery

Researchers have developed a method to identify new targets for human disease, including neurodegenerative conditions such as Alzheimer’s disease.

Scientists begin building AI for scientific discovery using tech behind ChatGPT

An international team of scientists, including from the University of Cambridge, have launched a new research collaboration that will leverage the same technology behind ChatGPT to build an AI-powered tool for scientific discovery.

UK needs AI legislation to create trust so companies can ‘plug AI into British economy’

Legislating for AI safety and transparency will allow British industry and education to put resources into AI development with confidence, argue researchers.

Cambridge and Google partner to facilitate AI research

The University of Cambridge and Google are building on their long-standing partnership with a multi-year research collaboration agreement and a Google grant for the University’s new Centre for Human-Inspired AI to support progress in responsible AI that is inspired by and benefits people.

AI trained to identify least green homes by Cambridge researchers

First of its kind AI-model can help policymakers efficiently identify and prioritize houses for retrofitting and other decarbonizing measures.

Artificial intelligence beats doctors in accurately assessing eye problems

A study has found that the AI model GPT-4 significantly exceeds the ability of non-specialist doctors to assess eye problems and provide advice.

Training AI models to answer ‘what if?’ questions could improve medical treatments

Machines can learn not only to make predictions, but to handle causal relationships. An international research team shows how this could make medical treatments safer, more efficient, and more personalised.

AI able to identify drug-resistant typhoid-like infection from microscopy images in matter of hours

Artificial intelligence (AI) could be used to identify drug resistant infections, significantly reducing the time taken for a correct diagnosis, Cambridge researchers have shown. The team showed that an algorithm could be trained to identify drug-resistant bacteria correctly from microscopy images alone.

Research community

Cambridge University hosts a vibrant community of over 12,000 staff across over 150 Departments. Researchers across disciplines are engaged in developing, deploying, and understanding the impact of AI technologies. Find out more about members of our AI research community here.

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Nathan Luke Abraham

Alexandre Almeida

Glenster Ann Kristin

Alex Archibald

John Aston

Shahar Avin

Julius Christopher Baeck

Somenath Bakshi

Soumya Banerjee

Viviana Bastidas Melo

Haydn Belfield

Alan Blackwell

Michael Boemo

Emma Boland

Paula Buttery

Albert Cardona

Matthew Castle

Colm-cille Caulfied

Stephen Cave

Jerry Chen

Maurice Chiodo

Alec Christie

Alice Cicirello

David Coomes

Ann Copestake

Diane Coyle

Mireia Crispin-Ortuzar

Gábor Csányi

Matt Davis

Ramit Debnath

Lynn Dicks

Eleanor Drage

Richard Durbin

Chris Edsall

Carl Henrik Ek

Guy Emerson

Sebastian Eves-van den Akker

Anne Ferguson Smith

Marla Fuchs

Diana Fusco

Indira Ganesh

Christos Genakos

Zoubin Ghahramani

Jenny Gibson

Sam Gilbert

Mark Girolami

Simon Godsill

Andrew Grace

Ongar Gulati

Ajay Halai

Namshik Han

Richard Harrison

José Miguel Hernández Lobato

Manuel Herrera

Tomasz Hollanek

Scott Hosking

Reham Hosny

Julian Huppert

Fumiya Iida

Milena Ivanova

Florian Jaeckle

Sadiq Jaffer

Mateja Jamnik

Mark Johnson

Henrik Jönsson

Napoleon Katsos

Francis Kelly

Ross King

Kate Knill

Anna Korhonen

Zoe Kourtzi

Markus Kraft

Nikhil Krishnan

Nicholas Lane

Alexei Lapkin

Neil Lawrence

Emily Lines

Pietro Liò

Erik Mackie

Anil Madhavapeddy

Alexandru Marcoci

Florian Markowetz

Kerry McInerney

Vito Mennella

Gos Micklem

Tim Minshall

Jessica Montgomery

Anna Moore

Sarah Morgan

Henry Moss

Sach Mukherjee

Leila Muresan

Andy Neely

Gina Neff

Katarzyna Nowaczyk-Basińska

Sean O hEigeartaigh

Amy Orben

Dominic Orchard

Kwadwo Oti-Sarpong

Harry Owen

Silviu Petrovan

Pauline Luise Pfuderer

Chris Pickard

Brechtje Post

Richard Prager

Lucia Reisch

Sam Reynolds

Robert Rouse

Henrik Salje

Sjors Scheres

Carola-Bibiane Schönlieb

Emily Shuckburgh

Jatinder Singh

Rebecca Smith

Gordon Smith

Elizabeth Soilleux

Keshav Srinivasan

William Sutherland

Apolline Taillandier

Kenza Tazi

Sarah Teichmann

Ulrike Tillmann

Mo Vali

Jane Walsh

Li Wan

Robert Wardrop

Staci Weiss

Adrian Weller

Alex Woolgar

Junwei Yang

Are you working on AI at Cambridge University?

In such a dynamic field, it isn’t easy to keep our researcher database up to date. If you think you should be on the list above, submit a profile via our website update form at the link below, or a pull request via our GitHub repo.