Richard Everson

Richard Everson

University of Exeter

H-index: 47

Europe-United Kingdom

About Richard Everson

Richard Everson, With an exceptional h-index of 47 and a recent h-index of 29 (since 2020), a distinguished researcher at University of Exeter, specializes in the field of machine learning, optimisation, multi-objective optimisation.

His recent articles reflect a diverse array of research interests and contributions to the field:

Community detection model for dynamic networks based on hidden Markov model and evolutionary algorithm

What would other emergency stroke teams do? Using explainable machine learning to understand variation in thrombolysis practice

Using machine learning and clinical registry data to uncover variation in clinical decision making

Collective self-understanding: A linguistic style analysis of naturally occurring text data

C3-IoC: A career guidance system for assessing student skills using machine learning and network visualisation

AI‐guided patient stratification for neurodegenerative disorders using unsupervised trajectory modelling

A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators

Context-Aware Generative Models for Prediction of Aircraft Ground Tracks

Richard Everson Information

University

Position

Professor of Machine Learning

Citations(all)

11356

Citations(since 2020)

5506

Cited By

7624

hIndex(all)

47

hIndex(since 2020)

29

i10Index(all)

98

i10Index(since 2020)

57

Email

University Profile Page

University of Exeter

Google Scholar

View Google Scholar Profile

Richard Everson Skills & Research Interests

machine learning

optimisation

multi-objective optimisation

Top articles of Richard Everson

Title

Journal

Author(s)

Publication Date

Community detection model for dynamic networks based on hidden Markov model and evolutionary algorithm

Artificial Intelligence Review

Amenah D Abbood

Bara’a A Attea

Ammar A Hasan

Richard M Everson

Clara Pizzuti

2023/9

What would other emergency stroke teams do? Using explainable machine learning to understand variation in thrombolysis practice

medRxiv

Kerry Pearn

Michael Allen

Anna Laws

Thomas Monks

Richard Everson

...

2023

Using machine learning and clinical registry data to uncover variation in clinical decision making

Intelligence-Based Medicine

Charlotte James

Michael Allen

Martin James

Richard Everson

2023/1/1

Collective self-understanding: A linguistic style analysis of naturally occurring text data

Behavior Research Methods

Alicia Cork

Richard Everson

Elahe Naserian

Mark Levine

Miriam Koschate-Reis

2023/12

C3-IoC: A career guidance system for assessing student skills using machine learning and network visualisation

International Journal of Artificial Intelligence in Education

Adán José-García

Alison Sneyd

Ana Melro

Anaïs Ollagnier

Georgina Tarling

...

2023/12

AI‐guided patient stratification for neurodegenerative disorders using unsupervised trajectory modelling

Alzheimer's & Dementia

Michael C Burkhart

Liz Yuanxi Lee

Delshad Vaghari

Jenny Venton

Spencer Thomas

...

2023/12

A probabilistic model for aircraft in climb using monotonic functional Gaussian process emulators

Proceedings of the Royal Society A

Nick Pepper

Marc Thomas

George De Ath

Enrico Olivier

Richard Cannon

...

2023/3/29

Context-Aware Generative Models for Prediction of Aircraft Ground Tracks

arXiv preprint arXiv:2309.14957

Nick Pepper

George De Ath

Marc Thomas

Richard Everson

Tim Dodwell

2023/9/26

The Early Detection of Neurodegenerative diseases initiative: emerging insights from the initial implementation of a digital toolkit

Alzheimer's & Dementia

Federica Marinaro

Claire Morvan

Rhoda Au

Simon Bond

Michael C Burkhart

...

2023/6

Guiding surrogate-assisted multi-objective optimisation with decision maker preferences

Finley J Gibson

Richard M Everson

Jonathan E Fieldsend

2022/7/8

Clinical pathway simulation

Michael Allen

Charlotte James

Julia Frost

Kristin Liabo

Kerry Pearn

...

2022/10

Optimising Diversity in Classifier Ensembles

SN Computer Science

Carina Ivaşcu

Richard M Everson

Jonathan E Fieldsend

2022/5

Using simulation and machine learning to maximise the benefit of intravenous thrombolysis in acute stroke in England and Wales: the SAMueL modelling and qualitative study

Michael Allen

Charlotte James

Julia Frost

Kristin Liabo

Kerry Pearn

...

2022/10/1

The Deep Dementia Phenotyping (DEMON) Network: A global platform for innovation using data science and artificial intelligence

Alzheimer's & Dementia

Janice M Ranson

Ahmad Al Khleifat

Donald M Lyall

Danielle Newby

Laura M Winchester

...

2022/12

Use of clinical pathway simulation and machine learning to identify key levers for maximizing the benefit of intravenous thrombolysis in acute stroke

Stroke

Michael Allen

Charlotte James

Julia Frost

Kristin Liabo

Kerry Pearn

...

2022/9

Patient and public involvement

Lyvonne N Tume

Kerry Woolfall

Barbara Arch

Louise Roper

Elizabeth Deja

...

2020/5

How Bayesian should Bayesian optimisation be?

George De Ath

Richard M Everson

Jonathan E Fieldsend

2021/7/7

Optimising diversity in classifier ensembles of classification trees

Carina Ivaşcu

Richard M Everson

Jonathan E Fieldsend

2021

Asynchronous -Greedy Bayesian Optimisation

George De Ath

Richard M Everson

Jonathan E Fieldsend

2021/12/1

Multi-objective Bayesian optimisation using an exploitative attainment front acquisition function

Finley J Gibson

Richard M Everson

Jonathan E Fieldsend

2021/6/28

See List of Professors in Richard Everson University(University of Exeter)

Co-Authors

H-index: 100
Stephen Roberts

Stephen Roberts

University of Oxford

H-index: 85
Ken E. Evans

Ken E. Evans

University of Exeter

H-index: 32
Gavin Tabor

Gavin Tabor

University of Exeter

H-index: 30
Trevor Bailey

Trevor Bailey

University of Exeter

H-index: 29
Jonathan Fieldsend

Jonathan Fieldsend

University of Exeter

H-index: 22
vitaly schetinin

vitaly schetinin

University of Bedfordshire

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