Profile and activity

• Overview
  
  Dr Stamate's background is in Computer Science and Mathematics. He got his PhD in Computer Science from University of Paris-Sud – currently Paris-Saclay University, and his current research is in the core Data Science areas of AI - Machine Learning, Deep Learning and Statistical Learning, with applications in Health, Finance. His Data Science & Soft Computing Lab in London develops AI based research and has multiple collaborations with research teams in several centres in UK, EU and USA, including King's College London, University of Manchester, Oxford University, Imperial College London, UCL, Glasgow University, Birkbeck - University of London, Yale University (USA), Maastricht University (Netherlands), University of Iasi (Romania), Frankfurt University of Applied Sciences (Germany), and with EMIF-AD - the European Medical Information Framework - Alzheimer's Disease Consortium (EU and UK). In particular, in the School of Health Sciences at the University of Manchester he collaborates with Prof David Reeves and his team on predicting risk of Dementia with Statistical Learning and AI - Machine Learning using routine primary care records (CPRD). He has supervised several PhD students and is open to accept new students for doctoral studies in the area of Machine Learning and its applications. In London Dr Stamate initiated and led the developments of the Data Science MSc at University of London (Goldsmiths College), programme which he currently co-leads. He now serves as Vice-President of the British Data Science Society. Moreover, he often collaborates with industry in the area of AI.

• 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.

Current and Recent roles

• Vice President of British Data Science Society (formerly British Classification Society)

• 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 (17^th 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 (5^th 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 (16^th 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 , 1^st 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, 1^st supervisor.

• Mohamed Saber, part time PhD candidate in Computer Science, University of London - Goldsmiths. Working in Financial Fraud Detection with Machine Learning, started 2018, 1^st 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, 1^st 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, 1^st 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, 1^st supervisor.

• Wajdi Alghamdi, PhD in Computer Science, thesis: Predictive Modelling Approach to Data-driven Computational Psychiatry, University of London - Goldsmiths, completed June 2018, 1^st 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).