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 Article Balancing accuracy and Interpretability: An R package assessing complex relationships beyond the Cox model and applications to clinical prediction Stamate, Daniel and Stahl, Daniel. 2025. Balancing accuracy and Interpretability: An R package assessing complex relationships beyond the Cox model and applications to clinical prediction. International Journal of Medical Informatics, 194, 105700. ISSN 1386-5056 Identifying individuals at high risk for dementia in primary care: Development and validation of the DemRisk risk prediction model using routinely collected patient data Stamate, Daniel ; Ford, Elizabeth; Ashcroft, Darren M.; Kontopantelis, Evangelos; Van Marwijk, Harm and McMillan, Brian. 2024. Identifying individuals at high risk for dementia in primary care: Development and validation of the DemRisk risk prediction model using routinely collected patient data. PLoS ONE, 19(10), e0310712. ISSN 1932-6203 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 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.
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A Regime-Switching Recurrent Neural Network Model Applied to Wind Time Series 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 Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches 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 Utilising symptom dimensions with diagnostic categories improves prediction of time to first remission in first-episode psychosis Stamate, Daniel ; Murray, Robin and Fisher, Helen L.. 2018. Utilising symptom dimensions with diagnostic categories improves prediction of time to first remission in first-episode psychosis. Schizophrenia Research, 193, pp. 391-398. ISSN 0920-9964 Improving time-efficiency in blocking expanding ring search for mobile ad hoc networks Pu, Ida ; Stamate, Daniel and Shen, Yuji. 2014. Improving time-efficiency in blocking expanding ring search for mobile ad hoc networks. Journal of Discrete Algorithms, 24, pp. 59-67. ISSN 1570-8667 Hypothesis-based semantics of logic programs in multivalued logics Stamate, Daniel ; Loyer, Y. and Spyratos, N.. 2004. Hypothesis-based semantics of logic programs in multivalued logics. ACM Transactions on Computational Logic, 5(3), pp. 508-527. ISSN 15293785 Parametrized semantics of logic programs: a unifying framework Stamate, Daniel ; Loyer, Y. and Spyratos, N.. 2003. Parametrized semantics of logic programs: a unifying framework. Theoretical Computer Science, 308(1-3), pp. 429-447. ISSN 03043975
Conference or Workshop Item Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling Musto, Henry ; Stamate, Daniel ; Logofatu, Doina and Stahl, Daniel. 2024. 'Predicting Deterioration in Mild Cognitive Impairment with Survival Transformers, Extreme Gradient Boosting and Cox Proportional Hazard Modelling'. In: Artificial Neural Networks and Machine Learning – ICANN 2024. Lugano, Switzerland 17 - 20 September 2024. Ensembles of Bidirectional LSTM and GRU Neural Nets for Predicting Mother-Infant Synchrony in Videos Stamate, Daniel ; Davuloori, Pradyumna; Logofatu, Doina; Mercure, Evelyne ; Addyman, Caspar and Tomlinson, Mark. 2024. 'Ensembles of Bidirectional LSTM and GRU Neural Nets for Predicting Mother-Infant Synchrony in Videos'. In: Engineering Applications of Neural Networks: 25th International Conference on Engineering Applications of Neural Networks (EANN 2024). Corfu, Greece 27 - 30 June 2024. On a Survival Gradient Boosting, Neural Network and Cox PH Based Approach to Predicting Dementia Diagnosis Risk on ADNI 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.
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Predicting High vs Low Mother-Baby Synchrony with GRU-Based Ensemble Models 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. Predicting Alzheimer’s Disease Diagnosis Risk Over Time with Survival Machine Learning on the ADNI Cohort 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. Predicting Colour Reflectance with Gradient Boosting and Deep Learning Akanuma, Asei ; Stamate, Daniel and Bishop, Mark (J. M.) . 2023. 'Predicting Colour Reflectance with Gradient Boosting and Deep Learning'. In: Artificial Intelligence Applications and Innovations. Leon, Spain 14 - 17 June 2023. A Neural Network Approach to Estimating Color Reflectance with Product Independent Models Akanuma, Asei and Stamate, Daniel . 2022. 'A Neural Network Approach to Estimating Color Reflectance with Product Independent Models'. In: 31st International Conference on Artificial Neural Network. Bristol, United Kingdom 6 - 9 September 2022. Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes Stamate, Daniel ; Roberts, Angus and Stahl, Daniel. 2022. 'Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes'. In: 18th IFIP International Conference on Artificial Intelligence Applications and Innovations. Hersonissos, Crete, Greece 17 - 20 June 2022. Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort Stamate, Daniel ; Musto, Henry ; Ajnakina, Olesya and Stahl, Daniel. 2022. 'Predicting Risk of Dementia with Survival Machine Learning and Statistical Methods: Results on the English Longitudinal Study of Ageing Cohort'. In: 18th IFIP International Conference on Artificial Intelligence Applications and Innovations - AIAI 2022. Hersonissos, Crete, Greece 17 - 20 June 2022. A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease Musto, Henry ; Stamate, Daniel ; Pu, Ida and Stahl, Daniel. 2022. 'A Machine Learning Approach for Predicting Deterioration in Alzheimer's Disease'. In: 20th IEEE International Conference on Machine Learning and Applications (ICMLA). Pasadena, CA, United States 13-16 December 2021. Predicting risk of dementia with machine learning and survival models using routine primary care records Langham, John ; Stamate, Daniel ; Wu, Charlotte A. ; Murtagh, Fionn ; Morgan, Catharine; Reeves, David; Ashcroft, Darren; Kontopantelis, Evan and McMillan, Brian. 2022. 'Predicting risk of dementia with machine learning and survival models using routine primary care records'. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Houston, TX, United States 9-12 December 2021. Creating Ensembles of Generative Adversarial Network Discriminators for One-Class Classification Ermaliuc, Miha ; Stamate, Daniel ; Magoulas, George D. and Pu, Ida . 2021. 'Creating Ensembles of Generative Adversarial Network Discriminators for One-Class Classification'. In: International Conference on Engineering Applications of Neural Networks. Halkidiki, Greece 25–27 June 2021. A Two-Step Optimised BERT-Based NLP Algorithm for Extracting Sentiment from Financial News Olaniyan, Rapheal ; Stamate, Daniel and Pu, Ida . 2021. 'A Two-Step Optimised BERT-Based NLP Algorithm for Extracting Sentiment from Financial News'. In: IFIP International Conference on Artificial Intelligence Applications and Innovations. Hersonissos, Crete, Greece 25–27 June 2021. Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment Stamate, Daniel ; Smith, Richard ; Tsygancov, Ruslan; Vorobev, Rostislav; Langham, John ; Stahl, Daniel and Reeves, David. 2020. 'Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment'. In: Artificial Intelligence Applications and Innovations. Halkidiki, Greece. Predicting S&P 500 based on its constituents and their social media derived sentiment Olaniyan, Rapheal ; Stamate, Daniel ; Pu, Ida ; Zamyatin, Alexander; Vashkel, Anna and Marechal, Frederic. 2019. 'Predicting S&P 500 based on its constituents and their social media derived sentiment'. In: 11th International Conference on Computational Collective Intelligence ICCCI 2019. Hendaye, France 4-6 September 2019. A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment Stamate, Daniel ; Alghambdi, Wajdi; Ogg, Jeremy; Hoile, Richard and Murtagh, Fionn . 2019. 'A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment'. In: 17th IEEE International Conference on Machine Learning and Applications (ICMLA 2018). Orlando, Florida, United States 17-20 December 2018. On XLE index constituents’ social media based sentiment informing the index trend and volatility prediction Stamate, Daniel ; Olaniyan, Rapheal and Marek, Jiri. 2018. 'On XLE index constituents’ social media based sentiment informing the index trend and volatility prediction'. In: 10th International Conference on Computational Collective Intelligence (ICCCI 2018). Bristol, United Kingdom. PIDT: A Novel Decision Tree Algorithm Based on Parameterised Impurities and Statistical Pruning Approaches Stamate, Daniel ; Alghamdi, Wajdi; Stahl, Daniel; Logofatu, Doina and Zamyatin, Alexander. 2018. 'PIDT: A Novel Decision Tree Algorithm Based on Parameterised Impurities and Statistical Pruning Approaches'. In: 14th IFIP International Conference on Artificial Intelligence Applications and Innovations. Rhodes, Greece. Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use? Stamate, Daniel ; Alghamdi, Wajdi; Stahl, Daniel; Zamyatin, Alexander; Murray, Robin and di Forti, Marta. 2018. 'Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use?'. In: 14th AIAI: IFIP International Conference on Artificial Intelligence Applications and Innovations. Rhodes, Greece. Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning Stamate, Daniel ; Alghamdi, Wajdi; Stahl, Daniel; Pu, Ida ; Murtagh, Fionn ; Belgrave, Danielle; Murray, Robin and di Forti, Marta. 2018. 'Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning'. In: IPMU 2018: 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems. Cadiz, Spain. A New Machine Learning Framework for Understanding the Link between Cannabis Use and First-Episode Psychosis Stamate, Daniel ; Stahl, Daniel; Murray, Robin and Di Forti, Marta. 2018. 'A New Machine Learning Framework for Understanding the Link between Cannabis Use and First-Episode Psychosis'. In: Proceedings of the 12th eHealth Conference. Vienna, Austria. Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life Stamate, Daniel ; Custovic, Adnan; Fleming, Louise; Bush, Andrew and Saglani, Sejal. 2018. 'Predictive Modelling Strategies to Understand Heterogeneous Manifestations of Asthma in Early Life'. In: 16th IEEE International Conference on Machine Learning and Applications 2017. Cancun, Mexico. Predicting Psychosis Using the Experience Sampling Method with Mobile Apps Stamate, Daniel ; Katrinecz, Andrea; Alghamdi, Wajdi; Stahl, Daniel; Delespaul, Philippe; van Os, Jim and Guloksuz, Sinan. 2017. 'Predicting Psychosis Using the Experience Sampling Method with Mobile Apps'. In: ICMLA 2017: 16th IEEE International Conference on Machine Learning and Applications (ICMLA). Cancun, Mexico 18-21 December 2017. A Novel Space Filling Curves Based Approach to PSO Algorithms for Autonomous Agents Stamate, Daniel and Balabanov, Kristiyan. 2017. 'A Novel Space Filling Curves Based Approach to PSO Algorithms for Autonomous Agents'. In: ICCCI 2017: 9th International Conference on Computational Collective Intelligence. Nicosia, Cyprus. Particle Swarm Optimization Algorithms for Autonomous Robots with Leaders Using Hilbert Curves Stamate, Daniel . 2017. 'Particle Swarm Optimization Algorithms for Autonomous Robots with Leaders Using Hilbert Curves'. In: 18th International Conference on Engineering Applications of Neural Networks (EANN 2017). Athens, Greece. A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use Stamate, Daniel ; Vang, Katherine; Stahl, Daniel; Colizzi, Marco; Tripoli, Giada; Quattrone, Diego; Ajnakina, Olesya; Murray, Robin M. and Forti, Marta Di. 2016. 'A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use'. In: 15th IEEE International Conference on Machine Learning and Applications. Anaheim, California, United States. A novel statistical and machine learning hybrid approach to predicting S&P500 using sentiment analysis Murtagh, Fionn ; Olaniyan, Rapheal and Stamate, Daniel . 2015. 'A novel statistical and machine learning hybrid approach to predicting S&P500 using sentiment analysis'. In: 8th International Conference of the ERCIM Working Group on Computational and Methodological Statistics. Senate House, University of London, United Kingdom. Sentiment and stock market volatility predictive modelling - A hybrid approach Stamate, Daniel ; Ouarbya, Lahcen and Logofatu, Doina. 2015. 'Sentiment and stock market volatility predictive modelling - A hybrid approach'. In: 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Paris, France. Social Web-based Anxiety Index's Predictive Information on S&P 500 Revisited Olaniyan, Rapheal ; Stamate, Daniel and Logofatu, Doina. 2015. 'Social Web-based Anxiety Index's Predictive Information on S&P 500 Revisited'. In: SLDS 2015: 3rd International Syposium on Statistical Learning and Data Sciences. Royal Holloway UoL, Egham, United Kingdom. Scalable Distributed Genetic Algorithm for Data Ordering Problem with Inversion Using MapReduce Stamate, Daniel . 2014. 'Scalable Distributed Genetic Algorithm for Data Ordering Problem with Inversion Using MapReduce'. In: AIAI 2014: 10th IFIP International Conference on Artificial Intelligence Applications and Innovations. Rhodes, Greece. Imperfect Information Fusion Using Rules with Bilattice Based Fixpoint Semantics Stamate, Daniel and Pu, Ida . 2012. 'Imperfect Information Fusion Using Rules with Bilattice Based Fixpoint Semantics'. In: 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012. Catania, Italy. Queries with Multivalued Logic-Based Semantics for Imperfect Information Fusion Stamate, Daniel . 2010. 'Queries with Multivalued Logic-Based Semantics for Imperfect Information Fusion'. In: 40th IEEE International Symposium on Multiple-Valued Logic (ISMVL '10). Barcelona, Spain 26-28 May 2010. Default Reasoning with Imperfect Information in Multivalued Logics Stamate, Daniel . 2008. 'Default Reasoning with Imperfect Information in Multivalued Logics'. In: 38th International Symposium on Multiple Valued Logic (ismvl 2008). Dallas TX, United States 22-24 May 2008. Reduction in Dimensions and Clustering using Risk and Return Model Stamate, Daniel and Qaiyumi, S.. 2007. 'Reduction in Dimensions and Clustering using Risk and Return Model'. In: IEEE International Symposium on Data Mining and Information Retrieval (IEEE DMIR-07) in conjunction with the IEEE 21 International Conference on Advanced Information Networking and Applications (IEEE AINA-07), Niagara Falls, Canada. UNDEFINED 5/1/2007. Assumption based Multi-Valued Semantics for Extended Logic Programs Stamate, Daniel . 2006. 'Assumption based Multi-Valued Semantics for Extended Logic Programs'. In: 36th IEEE International Symposium on Multiple-Valued Logics (IEEE ISMVL 2006). UNDEFINED 5/1/2006.
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.
