Dr Daniel Stamate

Daniel’s research interests concern problems and applications involving soft computing and data science.

Staff details

Dr Daniel Stamate


Senior Lecturer in Data Science




d.stamate (@gold.ac.uk)



I am a Machine Learning scientist, Data Science team leader, Director of Data Science MSc Programme, and industry AI – Machine Learning expert speaker and consultant. I established and lead the Data Science & Soft Computing Lab which has collaborations with various research groups at King’s College London, University of Manchester, Imperial College London, Maastricht University, and National Research Tomsk State University, and with companies in the City of London such as Santander Bank, Mizuho Investment Bank, etc. At Goldsmiths, I initiated, designed and run the MSc in Data Science - which inspired and was mostly replicated into similar online programme to come at University of London. I have a background in Computer Science and Mathematics, holding an MSc degree in Computer Science & Mathematics from University of Iasi - Faculty of Mathematics, and a PhD in Computer Science from University of Paris-Sud - LRI Computer Science Laboratory.

Publications and research outputs

Book Section

Stamate, Daniel. 2008. Imperfect Information Representation through Extended Logic Programs in Bilattices. In: Bernadette Bouchon-Meunier; Christophe Marsala; Maria Rifqi and Ronald R Yager, eds. UNCERTAINTY AND INTELLIGENT INFORMATION SYSTEMS. London: World Scientific, pp. 419-432. ISBN 978-981-279-234-1


Stamate, Daniel; Kim, Min; Proitsi, Petroula; Westwood, Sarah; Baird, Alison; Nevado-Holgado, Alejo; Hye, Abdul; Bos, Isabelle; Vos, Stephanie; Vandenberghe, Rik; Teunissen, Charlotte E; Kate, Mara Ten; Scheltens, Philip; Gabel, Silvy; Meersmans, Karen; Blin, Olivier; Richardson, Jill; Roeck, Ellen De; Engelborghs, Sebastiaan; Sleegeres, Kristel; Bordet, Régis; Rami, Lorena; Kettunen, Petronella; Tsolaki, Magd; Verhey, Frans; Alcolea, Daniel; Lléo, Alberto; Peyratout, Gwendoline; Tainta, Mikel; Johannsen, Peter; Freund-Levi, Yvonne; Frölich, Lutz; Dobricic, Valerija; Frisoni, Giovanni B; Molinuevo, José L; Wallin, Anders; Popp, Julius; Martinez-Lage, Pablo; Bertram, Lars; Blennow, Kaj; Zetterberg, Henrik; Streffer, Johannes; Visser, Pieter J; Lovestone, Simon and Legido-Quigley, Cristina. 2019. A metabolite-based machine learning approach to diagnose Alzheimer’s-type dementia in blood: Results from the European Medical Information Framework for Alzheimer's Disease biomarker discovery cohort. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 5, pp. 933-938.

Nikolaev, Nikolay; Smirnov, Evgueni; Stamate, Daniel and Zimmer, Robert. 2019. A Regime-Switching Recurrent Neural Network Model Applied to Wind Time Series. Applied Soft Computing, 80, pp. 723-734. ISSN 1568-4946

Stamate, Daniel; Katrinecz, Andrea; Stahl, Daniel; Verhagen, Simone J.W.; Delespaul, Philippe A.E.G.; van Os, Jim and Guloksuz, Sinan. 2019. Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches. Schizophrenia Research, 209, pp. 156-163. ISSN 0920-9964

Conference or Workshop Item

Musto, Henry; Stamate, Daniel; Logofatu, Doina and Ouarbya, Lahcen. 2024. 'On a Survival Gradient Boosting, Neural Network and Cox PH Based Approach to Predicting Dementia Diagnosis Risk on ADNI'. In: 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Istanbul, Turkey 5 - 8 December 2023.

Stamate, Daniel; Haran, Riya; Rutkowska, Karolina; Davuloori, Pradyumna; Mercure, Evelyne; Addyman, Caspar and Tomlinson, Mark. 2023. 'Predicting High vs Low Mother-Baby Synchrony with GRU-Based Ensemble Models'. In: Artificial Neural Networks and Machine Learning – ICANN 2023. Heraklion, Crete, Greece 26-29 September 2023.

Musto, Henry; Stamate, Daniel; Pu, Ida and Stahl, Daniel. 2023. 'Predicting Alzheimer’s Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort'. In: Computational Collective Intelligence. ICCCI 2023.. Budapest, Hungary 27–29 September 2023.

Research Interests

My current research is in the broader areas of Data Science and AI – Machine Learning, NLP. In particular I am interested in Machine Learning, Statistical Learning, and Predictive Modelling with a particular focus on: (a) NLP, text mining and sentiment analysis approaches to stock market forecasting and fraud detection; (b) Predictive modeling & computational psychiatry – ongoing work in collaboration with Institute of Psychiatry, Psychology and Neuroscience at King’s College London; (c) Predicting risk of dementia using routine primary care records, work in collaboration with University of Manchester and other partner universities; (d) Novel machine and statistical learning approaches to understand heterogeneous manifestations of asthma in early life, work in collaboration with the Department of Medicine, Imperial College London; (e) Decision trees and ensemble based methods with parameterised impurity families and statistical pruning (f) Mobility big data analytics – focusing on analysing smart card Oyster data of Transport for London. Another component of my research focuses on data uncertainty approaches, and Soft Computing. I previously worked in statistical databases, databases with uncertain information, and information integration. I supervise several PhD students in Data Science; prospective applicants are welcome to email me.