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Dr
Daniel Stamate
Honorary
Senior Lecturer, Machine Learning Division of Population
Health, Health Services Research & Primary Care The
University of Manchester
Email
daniel.stamate@manchester.ac.uk
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Profile
and activity
Overview
Dr
Stamate's background is in Computer Science and Mathematics. He
got his PhD from University of Paris-Sud – currently
Paris-Saclay
University, and his current research is in the core Data
Science areas of Machine Learning and Computational Statistics,
with applications in Health and in industry. His Data
Science & Soft Computing Lab in London has multiple
collaborations with research teams in several universities,
including King's College London, University of Manchester, Oxford
University, Imperial College London, UCL, Yale University,
Maastricht University, and with EMIF-AD - the European Medical
Information Framework - Alzheimer's Disease Consortium. He is
mainly based at University of London – Goldsmiths College,
where he is a Senior Lecturer / Associate Professor in Data
Science, and he is also an Honorary Senior Lecturer in Machine
Learning in the School of Health Sciences at the University of
Manchester where he collaborates with Prof David Reeves' team on
predicting risk of Dementia with Statistical Learning and Machine
Learning using routine primary care records – Clinical
Practice Research Datalink (CPRD). In London, Dr Stamate
initiated and led the developments of the Data Science MSc at
Goldsmiths College - University of London, programme which he led
between 2014 and 2020, and which was mostly replicated in a Data
Science MSc online programme at the University of London.
Recent
research awards
Best
Quality/Novelty Research Paper Award 2021 at the International
Conference on Engineering Applications of Neural Networks
Research
interests and collaborations
Daniel
Stamate's current research is in the broader areas of Data
Science, Statistical Learning, AI – Machine Learning, Deep
Learning, NLP, with particular interests in:
Predicting
risk of dementia with AI-ML and Statistical Learning using
routine primary care records – research in collaboration
with the Division of Population Health, Health Services Research
& Primary Care, School of Health Sciences at the University
of Manchester
Machine
Learning in Computational Psychiatry in particular predicting
risk of Psychosis – research in collaboration with the
Institute of Psychiatry at King’s College London,
Maastricht University, Yale University
Machine
Learning approaches to diagnosing Alzheimer's type dementia and
to biomarker discovery – in collaboration with King's
College London, Oxford University, UCL and centres in the
EMIF-AD consortium
Soft
Computing, Evolutionary/ Genetic Algorithms and Applications –
collaborations with Frankfurt University of Applied Sciences,
and University of Iasi
He
has had various research collaborations with industry, including
Transport for London, Sherwin-Williams, Hitachi Europe,
Santander
Research
Publications
Research
Grants:
◆
January
2023- December 2025, Measurement of playful parenting at scale
using machine learning, work package part of Transforming systems
to take playful parenting and learning through play to scale
grant, in collaboration with Oxford (lead institution),
Stellenbosch University and partners, funded
by LEGO Foundation, total value £11m.
Machine Learning work package value £400k.
Work package Co-lead
with
Prof Mark Tomlinson and Dr Caspar Addyman of Stellenbosch.
◆
January
2022- December 2024, Behavioural science to boost sustainable
travel KTP - Innovate UK grant in collaboration with Prof
Jonny Freeman (PI) and Hitachi Europe, value £191k.
Co-I.
◆
December
2022- April 2023, TENSOR AKT – Innovate UK grant in
collaboration with Sherwin-Williams, AI research investigating
innovative Artificial Neural Network approaches for predicting
spectral curves with industry applications, value £38k.
PI.
◆
September
2020-September 2022, CHROMA KTP – Innovate UK grant in
collaboration with Sherwin-Williams, AI research investigating
innovative Machine Learning approaches for predicting spectral
curves with industry applications, value £208k.
PI.
◆
July
2018 – June 2022, Predicting risk of dementia using routine
primary care records - CPRD, Alzheimer’s Research UK grant,
investigating Machine Learning and Statistical prediction
modelling for estimating risk of dementia. Study covered on
BBC
News, value
£240k,
in
collaboration with Prof David Reeves (PI) and his team at the
University of Manchester. Chief
Investigator at
Goldsmiths, University of London, value £110k,
leading on the Machine Learning developments in this project.
◆
July
2019- July 2020, Automated measurement of responsive caregiving at
scale using machine learning, Royal Academy of Engineering grant,
value £20k,
in collaboration with Caspar Addyman (PI). Co-I.
◆
September
2017-September 2019, Data Science Research and Postgraduate
Mobility grant, EU Erasmus+ funded, 54k
Euro. PI.
◆
April
2014- April 2018, Prediction Modelling Approaches to Data-driven
Computational Psychiatry project funded by Saudi Government, value
£175k,
supporting PhD work of Wajdi Alghamdi at Goldsmiths, University of
London. PI.
◆
April
2000- March 2002, Integrating imperfect information from multiple
web sources, Marie Curie individual grant, 108k
Euro, Birkbeck,
University of London. PI.
Recent
roles
Vice
President of British
Data Science Society (formerly British Classification
Society), since 2023
Chair
of Session on Deep
Learning and Boosting at AIAI 2023: International Conference
on Artificial Intelligence Applications and Innovations, Leon,
Spain, 2023
Chair
of Hybrid Session at ICANN 2022: International Conference on
Artificial Neural Networks, Bristol, UK, 2022
Chair
Operationalising
Data Science track , Moderator of the panel on Data Science
Techniques that Improve Data Access, Quality, Management and
Analytics, FIMA
Europe - The World’s Leading Data Event for Top
Investment Banks and Asset Managers, London 2018
Co-organiser
Special
Session on Machine
Learning Applications in Psychiatry
at
the 16th IEEE International Conference on Machine Learning and
Applications (17th
IEEE
ICMLA), Orlando, 2018
Co-organiser
Special Session on Data
Science in Computational Psychiatry and Psychiatric Research
at
the IEEE Data Science and Advanced Analytics International
Conference (5th
IEEE
DSAA), Turin, 2018
Chair
Empowering Big Decisions with Big Data and AI Session at the
London
Business Conference,
Goldsmiths,
University of London, 2018
Co-organiser
Special Session on
Machine
Learning Applications in Psychiatric Research at
the 16th IEEE International Conference on Machine Learning and
Applications (16th
IEEE
ICMLA), Cancun, 2017
Editorial
Board Member Journal
of Multiple-Valued Logic and Soft Computing
Recent
Conference Program Committees
32nd International
Conference on Artificial Neural Networks, ICANN 2023 19th IFIP
International Conference on Artificial Intelligence Applications
and Innovations, AIAI 2023 24rd International Conference
on Engineering Applications of Neural Networks, EANN 2023 18th
IFIP International Conference on Artificial Intelligence
Applications and Innovations, AIAI 2022 23rd International
Conference on Engineering Applications of Neural Networks, EANN
2022 17th IEEE International Conference on Machine Learning
and Applications, ICMLA 2018 5th IEEE International Conference
on Data Science and Advanced Analytics, 2018 14th
International Conference on Artificial Intelligence Applications
and Innovations, AIAI 2018 20th International Symposium on
Symbolic and Numeric Algorithms for Scientific Computing, 2018
16th International Conference of Information Technologies and
Mathematical Modelling, 2017 17th International Conference on
Computational Science and Applications - Big Data Warehousing and
Analytics session, 2017 10th International Conference on the
Quality of Information and Communications Technology, 2016 EVOLVE
Conference: Evolving from Natural Computing and Data Mining,
2015 7th Computer Science and Electronic Engineering
Conference, 2015
Recent
talks
Predicting
risk of dementia with machine learning and statistical learning:
results on the CPRD and ELSA cohorts. In NIHR Statistics
Group's Event: Analysis of classification problems using machine
learning and statistical methodologies in routine data,
Southampton, 2023
Predicting
Risk of Dementia with Survival Machine Learning and Statistical
Methods: Results on the English Longitudinal Study of Ageing
(ELSA) Cohort. Institute of Psychiatry, Psychology &
Neuroscience, NIHR Maudsley Biomedical Centre, London 2022
On
some Classification and Survival Machine Learning Approaches to
Dementia Risk Prediction, Diagnosis and Biomarker Discovery,
British Classification Society, Colchester, 2022
AI
industry expert speaker on
Data Science and Machine Learning at
FIMA
Europe - The
World’s Leading Data Event for Top Investment Banks and
Asset Managers, London 2017-2021
PhD
supervision
Current
students
Jiri
Marek, part time PhD candidate in Computer Science, University of
London - Goldsmiths. Working in Behavioural Finance and Machine
Learning, started 2017 , 1st
supervisor.
Mihai
Ermaliuc, part time PhD candidate in Computer Science, University
of London - Goldsmiths. Working on Generative Adversarial
Networks and Deep Learning algorithms and their applications in
Health for predicting risk of Dementia on CPRD, started 2018, 1st
supervisor.
Mohamed
Saber, part time PhD candidate in Computer Science, University of
London - Goldsmiths. Working in Financial Fraud Detection with
Machine Learning, started 2018, 1st
supervisor.
John
Langham, part time PhD candidate in Computer Science, University
of London - Goldsmiths. Working on predicting risk of Dementia
with Machine Learning using routine primary care records - CPRD,
started 2020, 1st
supervisor.
Henry
Musto, part time PhD candidate in Computer Science, University of
London - Goldsmiths. Working on predicting Dementia with Machine
Learning and Statistical Learning approaches, started 2020, 1st
supervisor.
Completed
Rapheal
Olaniyan,
PhD in Computer Science, thesis: Applied Natural Language
Processing and Machine Learning in Algorithmic Trading,
University of London - Goldsmiths, completed December 2021, 1st
supervisor.
Wajdi
Alghamdi,
PhD in Computer Science, thesis: Predictive Modelling Approach to
Data-driven Computational Psychiatry, University of London -
Goldsmiths, completed June 2018, 1st
supervisor.
Majed
Alsanea,
PhD in Computer Science, thesis: Factors Affecting the Adoption
of Cloud Computing in Saudi Arabia’s Government Sector,
University of London - Goldsmiths, completed 2015, 2nd supervisor
(1st supervisor Dr Jenn Barth).
Yann
Loyer, PhD in Computer Science, thesis (French): Hypotheses
versus programmes logiques : une approche semantique de
l'integration d'information en logique multi-valuee, University
of Paris-Sud, France, completed 2001, co-advisor (1st supervisor
Prof Nicolas Spyratos).
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