Yevgeniya Kovalchuk

Yevgeniya serves as a supervisor on a KTP project with Made in the Midlands and a Co-Investigator on the ERDF funded project "EcRoFt – Computational methods toward zero-carbon retrofit systems".

• Artificial intelligence and machine learning
• Graph analytics
• Sensors and wearables
• Mutli-agent systems
• Healthcare informatics
• Movement/art therapy

• PhD in Computer Science, University of Essex, UK
• MSc in Economic Cybernetics, Mathematical Methods and Models in Economics Department, National Technical University of Ukraine
• Level 5 Diploma in Management and Leadership, Chartered Management Institute, UK
• Postgraduate Certificate in Higher Education

• L6 Artificial Intelligence and Machine Learning
• MSc Advanced Data Science

Yevgeniya’s current research is concerned with the use of wearables, biological signal processing and mathematical modelling of brain-body interactions linked to emotions to enhance our understanding of the interconnection between physical and mental health/performance, and the role different factors (e.g. social, environmental and genetic) play in defining this interconnection. A particular focus is on how mental and emotional states are represented in movement coordination patterns, something that is heavily exploited in movement and art therapies.

Studying human behaviour and dynamics of the representation of mental and emotional states in bodily coordination patterns has a wide range of applications from finding better treatments for patients with mental and/or physical disorders, through designing usable technology, to developing effective security, gaming, virtual reality and rehabilitation solutions.

Prospective PhD students with interest in one or more of the following areas are welcome to get in touch to discuss project ideas:

• Artificial intelligence and machine learning
• Graph analytics
• Biological signal processing
• Human motion analysis
• Affective computing

• K. M. Hanga, Y. Kovalchuk and M. M. Gaber, A Graph-based Approach to Interpreting Recurrent Neural Networks in Process Mining, IEEE Access 8 (2020), 172923-172938.
• H. Ghomeshi, M. M. Gaber, and Y. Kovalchuk, A Non-Canonical Hybrid Metaheuristic Approach to Adaptive Data Stream Classification, Future Generation Computer Systems, Elsevier 102 (2020),127-139.
• A. S. Sambo, R. M. A. Azad and Y. Kovalchuk, V. P. Indramohan and H. Shah, Feature Engineering for Improving Robustness of Crossover in Symbolic Regression, Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO-2020), 249–250.
• A. S. Sambo, R. M. A. Azad, Y. Kovalchuk, V. P. Indramohan and H. Shah, Leveraging Asynchronous Parallel Computing to Produce Simple Genetic Programming Computational Models, Proceedings of the 35th Annual ACM Symposium on Applied Computing (SAC-2020), 521–528.
• A. S. Sambo, R. M. A. Azad, Y. Kovalchuk, V. P. Indramohan and H. Shah, Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds.), Genetic Programming, Lecture Notes in Computer Science 12101, Springer (2020), 195-210.
• H. Ghomeshi, M. M. Gaber, and Y. Kovalchuk, RED-GENE: An Evolutionary Game Theoretic Approach to Adaptive Data Stream Classification, IEEE Access 7, 1 (2019), 173944-173954.
• H. Ghomeshi, M. M. Gaber, and Y. Kovalchuk, EACD: Evolutionary Adaptation to Concept Drifts in Data Streams, Data Mining and Knowledge Discovery, Springer (2019), 1-32
• K. M. Hanga and Y. Kovalchuk, Machine Learning and Multi-Agent Systems in Oil and Gas Industry Applications: A Survey, Computer Science Review 34, Elsevier (2019).
• M. M. Gaber, A. Aneiba, S. Basurra, Batty, O., A. Elmisery, Y. Kovalchuk, M.H. Ur Rehman, Internet of Things and Data Mining: From Applications to Techniques and Systems, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Wiley (2018), e1292. ISSN 19424787.
• H. Ghomeshi, M. M. Gaber, and Y. Kovalchuk, Ensemble Dynamics in Non-stationary Data Stream Classification. In: Sayed-Mouchaweh M. (eds.), Learning from Data Streams in Evolving Environments. Studies in Big Data, 41, Springer (2018), 123-153.
• D. Haidar, M. M. Gaber, and Y. Kovalchuk, AnyThreat: An Opportunistic Knowledge Discovery Approach to Insider Threat Detection, arXiv (2018).
• Y. Kovalchuk, R. Stewart, M. Broadbent, T. Hubbard, R. Dobson, Analysis of Diagnoses Extracted from Electronic Health Records in a Large Mental Health Case Register, PLOS ONE (2017).
• Z. Ibrahim, S.J. Kiddle, H. Wu, M. Kerz, Y. Kovalchuk, S. Miles, R. Dobson, Health-record Embedded Comparative Effectiveness Trials: A Case for Employing Multi-agent Systems, BMC Medical Informatics and Decision  Making (under review).
• S. Crichton, B. Barrat, A. Spiridou, U. Hoang, S. F. Liang, Y. Kovalchuk, S. Beevers, F. Kelly, B. Delaney, and C. Wolfe, Associations between Exhaust and Non-exhaust Particulate Matter and Stroke Incidence by Stroke Subtype in South London, Science of the Total Environment Journal, Elsevier (2016).
• S.F. Liang, A. Taweel, S. Miles, Y. Kovalchuk, A. Spiridou, B. Barratt, U. Hoang, S. Crichton, B.C. Delaney, C. Wolfe, Semi Automated Transformation to OWL Formatted Files as an Approach to Data Integration: A Feasibility Study Using Environmental, Disease Register & Primary Care Clinical Data, Methods of Information in Medicine 53,4 (2015).
• Y. Kovalchuk, Y. Chen, S. Miles, S. F. Liang, A. Taweel, Provenance-aware Pervasive Computing in Clinical Applications, Proceedings of the 9th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob, 2013), 308-313.
• Y. Kovalchuk, Talking Things, Proc. of the 9th Int. Conf. on Intelligent Environments, IOS Press (2013), 284-293.
• Y. Kovalchuk, Folk’s wisdom on a chip: Back into the future or Symphony of the body (2012).
• Y. Kovalchuk, G. Howells, H. Hu, D. Gu, and K.D. McDonald-Maier, A Practical Proposal for Ensuring the Provenance of Hardware Devices and Their Safe Operation, Proceedings of the IET Conferences on System Safety and Cyber Security (2012).
• Y. Kovalchuk, G. Howells, H. Hu, D. Gu, and K.D. McDonald-Maier, ICmetrics for Low Resource Embedded Systems, Proceedings of the third International Conference on Emerging Security Technologies (2012).
• Y. Kovalchuk, D. Newman, G. Howells, S. Kelly, H. Hu, D. Gu, and K.D. McDonald-Maier, Investigation of Properties of ICmetrics Features, Proceedings of the third International Conference on Emerging Security Technologies (2012).
• Y. Kovalchuk, G. Howells, and K.D. McDonald-Maier, Overview of ICmetrics Technology – Security Infrastructure for Autonomous and Intelligent Healthcare System, International Journal of u- and e- Service, Science and Technology, 4 (3) (2011), 49-60.
• Y. Kovalchuk, The Ministry of Interfaces (Doors), Workshop Proceedings of the 7th International Conference on Intelligent Environments, IOS Press, (2011).
• Y. Kovalchuk, Scientific Theatre: Multidisciplinary Approach to Designing Intelligent Environments, Workshop Proceedings of the 7th International Conference on Intelligent Environments, IOS Press (2011), 585-594.
• Y. Kovalchuk and L. Mova, Movement as a Means of Communication, Workshop Proceedings of the 7th International Conference on Intelligent Environments, IOS Press (2011), 610.
• M. Fasli and Y. Kovalchuk Learning Approaches for Developing Successful Seller Strategies in Dynamic Supply Chain Management, Information Sciences 181 (2011), 3411-3426.
• Y. Kovalchuk and M. Fasli, A Demand-Driven Approach for a Multi-Agent System in Supply Chain Management, E. David et al. (Eds.): LNBIP 59, Springer-Verlag Berlin Heidelberg (2010), 88–101.
• Y. Kovalchuk and V. Callaghan, A Self-Organizing System for Online Maintenance of a Living Organism, Proceedings of the 6th International Conference on Intelligent Environments, IOS Press (2010), 283-288.
• Y. Kovalchuk, Knowing yourself, Intelligent Environments, Creative Science, IOS Press (2010), 271-280.
• Y. Kovalchuk, A Conceptual Model of the System for Online Maintenance of a Living Organism, Technical Report CES-501, University of Essex (2009).
• R. Tagiew and Y. Kovalchuk, Barter Double Auction as Model for Bilateral Social Co-operations, Technical Report CES-502, University of Essex (2009).
• Y. Kovalchuk and M. Fasli, Deploying Neural-Network-Based Models for Dynamic Pricing in SCM, Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation (2008), 680-685.
• Y. Kovalchuk, Seller’s Strategies for Predicting Winning Bid Prices in Online Auctions, Proceedings of the International Conference on Intelligent Agents, Web Technologies and Internet Commerce (2008), 1-6.
• Y. Kovalchuk and M. Fasli, Evaluating Adaptive Customer Strategies in TAC SCM, Proceedings of the Workshop on Trading Agent Design and Analysis (TADA), AAAI-2008, Chicago (2008), 57-61.
• Y. Kovalchuk and M. Fasli, Adaptive Strategies for Predicting Bidding Prices in Supply Chain Management, Proceedings of the tenth International Conference on Electronic Commerce (ICEC’2008).
• Y. Kovalchuk, Multi-Agent Decision Support System for Supply Chain Management, Proceedings of the Doctoral Consortium of the 10th International Conference on Electronic Commerce (ICEC’2008).
• A. Agapitos, M. Dyson, Y. Kovalchuk, and S. M. Lucas, On the Genetic Programming of Time-Series Predictors for Supply Chain Management, Proceedings of the 2008 Genetic and Evolutionary Computation Conference (GECCO'2008), 1163-1170.
• Y. Kovalchuk, Information System for Analysis and Prediction of Stock Rates, in Proceedings of the third Scientific Conference on Problems of Implementation of Information Systems and Technologies in Economics and Business, Trade Economic University of Ukraine, Kyiv, Ukraine (2002).
• Y. Kovalchuk, Usage of the Unified Modelling Language (UML) for Developing Information Systems, in Proc. of the Scientific Conference on Modern Technologies of Running Business in Ukraine, NTUU "KPI", Kyiv, Ukraine (2002).
• Y. Kovalchuk, Information System for Analysis and Prediction of Stock Rates, in Proceedings of the second Scientific Conference on World Theory and Practice in Ukrainian Business, NTUU "KPI", Kyiv, Ukraine (2001).