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Dr
Daniel Stamate
Honorary
Senior Lecturer, Machine Learning Division of Population
Health, Health Services Research & Primary Care School
of Health Sciences University of Manchester Email
daniel.stamate AT manchester.ac.uk
Senior
Lecturer – Associate Professor in Data
Science Computing Department University of London –
Goldsmiths College
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Profile
and activity
Overview
Dr
Stamate's background is in Computer Science and Mathematics. He
got his PhD in the area of reasoning with data under
uncertainty – part of the broader area of mathematical
foundations of computer science and AI - soft computing, from
University of Paris-Sud – currently Paris-Saclay
University, which is the 1st research university in France,
and which is ranked the 13th
in the world overall, and 1st in the world for Mathematics by
the 2021 Academic Ranking of World Universities (ARWU). Dr
Stamate's current research is in the core Data Science areas of
Machine Learning and Computational Statistics with applications
in Health but also in Finance. He founded and leads the Data
Science & Soft Computing Lab in London, which has
strong links and collaborates with several universities (below)
and also with industry. His lab’s team in London
comprises 9 regular members including 3 academic staff, 1
postdoc KTP Associate and 5 PhDs; and 6 associated members as
external collaborators. Dr Stamate currently supervises 5 PhD
students as main supervisor. He and his team have collaborated
closely with research teams from 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, which comprises several major
research centres in Europe. He is mainly based in Computing
Department at Goldsmiths, University of London, where he is a
Senior Lecturer (Associate Professor) in Data Science, and is
also an Honorary Senior Lecturer 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 Machine Learning using routine primary care records. In
London, Dr Stamate initiated and led the developments of one of
the first Data Science MSc in UK, at Goldsmiths College,
University of London, and was a Programme Director since 2014.
Research
awards
Best
Quality/Novelty Research Paper Award 2021 at the International
Conference on Engineering Applications of Neural Networks
Marie
Curie Research Fellowship Award 2000
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 and Statistical Learning using
routine primary care records (CPRD) - in collaboration with
the Division of Population Health, Health Services Research &
Primary Care, School of Health Sciences at the University of
Manchester – Prof David Reeves, Prof Darren Ashcroft,
Prof Evan Kontopantelis
Machine
and Statistical Learning in Computational Psychiatry –
in collaboration with the Institute of Psychiatry at King’s
College London – Prof Daniel Stahl, Prof Sir Robin
Murray
Machine
Learning approaches to diagnosing Alzheimer's type dementia
and to biomarker discovery – in collaboration with
King's College London, Oxford University, UCL and other
centres in the EMIF-AD consortium – Dr Cristina
Legido-Quigley, Prof Sir Simon Lovestone
Deep
Learning and Generative Adversarial Network algorithms in
collaboration with University of London, Birkbeck – Prof
George Magoulas
NLP
with applications in text mining and sentiment analysis, stock
market forecasting, and in fraud detection – in
collaborations with Santander, Cardiff
Machine
Learning optimisations in colour industry, in collaboration
with Sherwin-Williams company USA
He
previously worked on other research problems involving Machine
Learning in Health, including Prediction modelling to
understand asthma heterogeneity - in collaboration with
Imperial College London – Prof Adnan Custovic
Research
output is listed in publications.
Project
lead:
◆
September
2020-September 2022, Lead Academic/ PI in CHROMA KTP grant
researching into Machine Learning state of the art approaches
to colour optimisation, co-funded by Innovate UK, partner
company Sherwin-Williams, grant value £208,310.
Goldsmiths, University of London team: Dr Daniel Stamate –
Lead, Dr Asei Akanuma – KTP Associate.
◆
July
2018 – June 2022, Chief Co-Investigator / Lead at
Goldsmiths, University of London: Predicting risk of dementia
using routine primary care records (CPRD). ARUK grant £240,000
in
collaboration with Prof David Reeves' team at University of
Manchester. London share £110,000. Dr Stamate is leading
on the Machine Learning aspects of this
study
funded by Alzheimer's Research UK covered
on BBC
News. London
team: Dr Daniel Stamate – Lead, Collaborators: Prof Fionn
Murtagh, John Langham, Mihai Ermaliuc - PhD candidates, Dr
Charlotte Wu - Clinician.
◆
September
2017-September 2019, Project Lead: Data Science Research and
Postgraduate Mobility. EU Erasmus+ funded,
54,000
Euro. Led by
Goldsmiths,
University of London, with partner institution National
Research Tomsk State University.
◆
April
2014- April 2018, Project Lead: Prediction Modelling Approaches
to Data-driven Computational Psychiatry. Funding body: Saudi
Government, value £175,000+,
supporting PhD work of Wajdi Alghamdi at Goldsmiths, University
of London
◆
April
2000- March 2002, PI: Integrating imperfect information from
multiple web sources, EU funded, 108,000
Euro Marie
Curie Individual Grant, University of London.
Project
participant:
◆
July
2019- July 2020, collaborator contributing in the Machine
Learning developments of the research project: Automated
measurement of responsive caregiving at scale using machine
learning. Funded by Royal Academy of Engineering, PI Caspar
Addyman, £20,000.
Current
or recent roles
AI
expert speaker at FIMA Europe, the largest Data conference
for Banks and Asset Managers, November 2021, contributing on
Applying AI and ML to enterprise data to drive more insights
and faster decision making
Co-organising
the Data
Science in Computational Psychiatry and Psychiatric Research
Special Session at the IEEE / ACM / ASA Data Science and
Advanced Analytics International Conference (6th
IEEE/
ACM/ ASA DSAA), Washington DC, October 2019
Speaker
at Prediction
Modelling Winter School, King's College London, December 2018
Co-organising
the Machine
Learning in Psychiatric Research special session at the 16th
IEEE International Conference on Machine Learning and
Applications (17th
IEEE
ICMLA), Orlando, December 2018
Chairing
the Operationalising
Data Science track and moderated the panel discussion on
Data Science Techniques that Improve Data Access, Quality,
Management and Analytics in the 2018 November edition of FIMA
Europe – a global network event conference attended
by senior teams of data experts from 120+ top investment banks
and asset management firms.
Co-organising
the Data
Science in Computational Psychiatry and Psychiatric Research
Special Session at the IEEE Data Science and Advanced Analytics
International Conference (5th
IEEE
DSAA), Turin, October 2018
Chairing
the Empowering Big Decisions with Big Data and AI session in
London
Business Conference, Goldsmiths,
13th September 2018
Editor
Journal of Multiple-Valued Logic and Soft Computing
Expert
speaker on Data Science, Big Data & Artificial Intelligence
at Data & Finance industry major event FIMA Europe, London
November 2017
Organising
the Machine Learning in Psychiatric Research special session at
the 16th IEEE International Conference on Machine Learning and
Applications (16th
IEEE
ICMLA), Cancun, 2017
Speaker
at 2015-2017 Summer School editions of Prediction modelling and
personalised medicine in medical research using modern
statistical methods, Department of Biostatistics and Health
Informatics, IoPPN, King's College London
Recent
Conference Program Committees 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 (Data Science in
Computational Psychiatry special session), 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
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).
Current
or recent teaching lead roles Goldsmiths, University of London
Programme
initiator and director - Data Science MSc Machine Learning
(MSc and Bsc) Data Science Research Topics (MSc) Statistics
and Statistical Data Mining (MSc) Data Programming
(MSc) Machine Learning and Statistical Data Mining (MSc) Data
Mining (Bsc) Final Project (MSc, BSc)
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