Dr Daniel Stamate

Staff details

Position Senior Lecturer in Data Science
Department Computing
Email d.stamate (@gold.ac.uk)
Phone +44 (0)20 7919 7864
Dr Daniel Stamate

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.

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.


Book Section

Walghamdi, Wajdi; 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: , ed. Volume 248: Health Informatics Meets eHealth. 248 IOS Press, pp. 9-16. ISBN 978-1-61499-857-0

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; 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

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

Ajnakina, Olesya; Lally, John; Di Forti, Marta; Stilo, Simona; Kolliakou, Anna; Gardner-Sood, Poonam; Dazzan, Paola; Pariante, Carmine; Marques, Tiago Reiss; Mondelli, Valeria; MacCabe, James; Gaughran, Fiona; David, Anthony S; Stamate, Daniel; Murray, Robin and Fisher, Helen L.. 2017. 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

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

Stamate, Daniel. 2008. Default Reasoning with Imperfect Information in Multivalued Logics. 38th International Symposium on Multiple Valued Logic, n/a, pp. 163-168. ISSN 0195-623X

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

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

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: Proceedings 11th International Conference on Computational Collective Intelligence (ICCCI), 2019. Hendaye, France 4-6 September 2019.

Marechal, Frederic; 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.

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.

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.

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.

Belgrave, Danielle; Cassidy, Rachel; 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.

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.

Logofătu, Doina; Sobol, Gil; 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.

Logofatu, Doina; Sobol, Gil and 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.

Alghamdi, Wajdi; 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.

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.

Olaniyan, Rapheal; 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.

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.

Logofatu, Doina and 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.

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.

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.

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.

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.