Marcin Budka

Professor Marcin Budka

  • u_mbudka at bournemouth dot ac dot uk
  • Professor of Machine Learning and AI
Back to top

Biography

Marcin Budka received his dual MSc/BSc degree in Finance and Banking from the Katowice University of Economics (Poland, 2003), BSc in Computer Science from the University of Silesia (Poland, 2005) and PhD in Computational Intelligence from Bournemouth University (UK, 2010). Between 2003 and 2007 he was working as an engineer, project manager and team leader in a smart-metering start-up company, before pursuing an academic career. In the years 2011-2012 he was appointed as a Visiting Research Fellow at the Wroclaw University of Technology, Poland. Between 2015 and 2017 he was the Head of Research in the Department of Computing and Informatics at Bournemouth University.

Research

His research interests lie in a broadly understood area of AI, machine learning and data science, with a particular focus on practical applications. Throughout his career, I was involved in a number of consultancy and research projects with industry, ranging from EU-funded international research programmes to multiple Knowledge Transfer Partnerships, always with a particular focus on tangible impact and societally useful innovation.

Journal Articles

Books

Chapters

  • Balaguer-Ballester, E., Tabas-Diaz, A., Budka, M., 2014. Empirical identification of non-stationary dynamics in time series of recordings. Springer Verlag, 142-151.
  • Musial, K., Budka, M., Blysz, W.. Understanding the Other Side - The Inside Story of the INFER Project.
  • Budka, M., 2013. Clustering as an example of optimizing arbitrarily chosen objective functions. In: Nguyen, N., Trawinski, B., Katarzyniak, R., Geun-Sik, J., eds. Advanced Methods for Computational Collective Intelligence. Springer Berlin / Heidelberg, 177-186.
  • Musial, K., Budka, M., Blysz, W.. Understanding the Other Side - the Inside Story of the INFER Project. In: Howlett, R.J., Howlett, R.J., Jain, L.C., eds. Innovation through Knowledge Transfer 2012. Springer Berlin Heidelberg, (in press).
  • Schierz, A.C., Budka, M., 2011. High-Performance Music Information Retrieval System for Song Genre Classification. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Ras, Z.W., eds. Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS'11). Springer-Verlag.

Conferences

  • Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., Dogan, H., Aksu, G., 2025. Tab-AML: A Transformer Based Transaction Monitoring Model for Anti-Money Laundering. Proceedings 2025 IEEE Conference on Artificial Intelligence Cai 2025, 161-167.
  • Awe, M., Malhi, A., Mavengere, N., Budka, M., Dave, B., 2025. Towards the Automation of Last Planner System ®. Proceedings of the International Symposium on Automation and Robotics in Construction, 1143-1152.
  • Samaratunga, R., Budka, M., Breen, A., Breen, A., 2025. Automatic detection of the lumbar spine using transfer learning for Quantitative Fluoroscopy. Procedia Computer Science, 270, 2291-2300.
  • Taati, E., Budka, M., Neville, S., Canniffe, J., 2024. Optimizing Neural Topic Modeling Pipelines for Low-Quality Speech Transcriptions. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 14795 LNAI, 184-197.
  • Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., Dogan, H., Aksu, G., 2023. Enhancing Anti-Money Laundering: Development of a Synthetic Transaction Monitoring Dataset. Proceedings 2023 IEEE International Conference on E Business Engineering Icebe 2023, 47-54.
  • Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., Dogan, H., Aksu, G., 2023. Perspectives from Experts on Developing Transaction Monitoring Methods for Anti-Money Laundering. Proceedings 2023 IEEE International Conference on E Business Engineering Icebe 2023.
  • Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., 2022. Enhancing Transaction Monitoring Controls to Detect Money Laundering Using Machine Learning. In: 18th IEEE International Conference on E-Business Engineering (ICEBE) 14/10/2022 Bournemouth, UK (best paper award).
  • Thurston, J., Polkinghorne, M., 2022. Students as Researchers. In: Fusion Learning Conference - Innovation and Excellence in the Pandemic 28/06/2021 Bournemouth University: UK. Poole, UK: Bournemouth University.
  • Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., 2022. Enhancing Transaction Monitoring Controls to Detect Money Laundering Using Machine Learning. Proceedings 2022 IEEE International Conference on E Business Engineering Icebe 2022.
  • Ali, A.R., Budka, M., 2021. An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease. IJCNN, 1-8 IEEE.
  • Reich, T., Budka, M., Hulbert, D., 2020. Impact of Data Quality and Target Representation on Predictions for Urban Bus Networks. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI) 01/12/2020 Canberra Australia. 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2843-2852.
  • Ali, A.R., Budka, M., Gabrys, B., 2019. Towards Meta-learning of Deep Architectures for Efficient Domain Adaptation. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 11671 LNAI, 66-79.
  • Ali, A.R., Budka, M., Gabrys, B., 2019. A Meta-Reinforcement Learning Approach to Optimize Parameters and Hyper-parameters Simultaneously. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 11671 LNAI, 93-106.
  • Wahid-Ul-Ashraf, A., Budka, M., Musial-Gabrys, K., 2018. Newton’s gravitational law for link prediction in social networks. Studies in Computational Intelligence, 689, 93-104.
  • Salvador, M.M., Budka, M., Quay, T., 2018. Automatic Transport Network Matching Using Deep Learning. Transportation Research Procedia, 31, 67-73.
  • Ali, A.R., Gabrys, B., Budka, M., 2018. Cross-domain Meta-learning for Time-series Forecasting. Procedia Computer Science, 126, 9-18.
  • Wahid-Ul-Ashraf, A., Budka, M., Musial, K.. NetSim - The framework for complex network generator. KES, 126 Elsevier.
  • Budka, D., Bennett, M.R., Budka, M., Bakirov, R., 2017. COMPARING FOOTWEAR EVIDENCE AND THE POWER OF THE 3RD DIMENSION. FORENSIC SCIENCE INTERNATIONAL, 277, 162-163.
  • Budka, D., Budka, M., Bennett, M.R., Bakirov, R., 2017. CAPTURE AND ANALYSIS OF 3D FOOTWEAR EVIDENCE: NEW HORIZONS AND OPPORTUNITIES. FORENSIC SCIENCE INTERNATIONAL, 277, 172.
  • Salvador, M.M., Budka, M., Gabrys, B., 2017. Modelling multi-component predictive systems as petri nets. 15th International Industrial Simulation Conference 2017 Isc 2017, 17-23.
  • Salvador, Budka, M., Gabrys, B., 2016. Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry. In: AutoML 2016 @ ICML 20/06/2016 New York (USA).
  • Martin Salvador, M., Budka, M., Gabrys, B., 2016. Towards automatic composition of multicomponent predictive systems. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 9648, 27-39.
  • Salvador, M.M., Budka, M., Quay, T., Carver-Smith, A., 2016. Improving transport timetables usability for mobile devices: A case study. Patat 2016 Proceedings of the 11th International Conference on the Practice and Theory of Automated Timetabling, 521-524.
  • Bennett, M., Morse, S., Budka, M.. Tracks and sediments: evolutionary stasis in foot function? 31st IAS Meeting of Sedimentology, 22-25 June 2015, Krakow, Poland.
  • Król, D., Budka, M., Musial, K., 2014. Simulating the information diffusion process in complex networks using push and pull strategies. Proceedings 2014 European Network Intelligence Conference Enic 2014, 1-8.
  • Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Martin Salvador, M., Schwan, S., Tsakonas, A., Žliobaitė, I., 2014. From Sensor Readings to Predictions: On the Process of Developing Practical Soft Sensors. In: The Thirteenth International Symposium on Intelligent Data Analysis (IDA 2014) 30/10/2014 Leuven, Belgium. Advances in Intelligent Data Analysis XIII, 8819 Springer.
  • Balaguer-Ballester, E., Tabas, A., Budka, M., 2014. Attracting Dynamics, non-stationarity and trial-to-trial variability in frontal ensemble recordings. In: Bernstein Computational Neuroscience Conference 2014 02/09/2014 Tubingen.
  • Balaguer-Ballester, E., Tabas-Diaz, A., Budka, M., 2014. Empirical identification of non-stationary dynamics in time series of recordings. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 8779 LNAI.
  • Budka, M., Musial, K., Juszczyszyn, K., 2012. Predicting the evolution of social networks: Optimal time window size for increased accuracy. , 21-30.
  • Juszczyszyn, K., Gonczarek, A., Tomczak, J.M., Musial, K., Budka, M., 2012. A probabilistic approach to structural change prediction in evolving social networks. , 996-1001.
  • Juszczyszyn, K., Budka, M., Musial, K.. The Dynamic Structural Patterns of Social Networks Based on Triad Transitions. Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on.
  • Schierz, A.C., Budka, M.. High--Performance music information retrieval system for song genre classification. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Ras, Z.W., eds. Proc. of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS'11) 06/2011 Warsaw, Poland. Lecture Notes in Computer Science, 2011, Volume 6804: Springer-Verlag.
  • Juszczyszyn, K., Budka, M., Musial, K., 2011. The dynamic structural patterns of social networks based on triad transitions. , 581-586.
  • Juszczyszyn, K., Musial, K., Budka, M., 2011. Link prediction based on Subgraph evolution in dynamic social networks. , 27-34.
  • Juszczyszyn, K., Musial, K., Budka, M.. On analysis of complex network dynamics – changes in local topology. The fifth SNAKDD Workshop 2011 on Social Network Mining and Analysis held in conjunction with SIGKDD conference.
  • Budka, M., Gabrys, B., 2010. Correntropy–based density–preserving data sampling as an alternative to standard cross–validation. In: World Congress on Computational Intelligence (WCCI 2010) 18/07/2010 Barcelona, Spain. , 1-8 IEEE.
  • Budka, M., Gabrys, B., 2009. Electrostatic Field Classifier for Deficient Data. , 311-318 Heidelberg: Springer.
  • Moffat, A.C., Osselton, M.D., Widdop, B., Clarke, E.G.C.. Clarke's analysis of drugs and poisons. Pharmaceutical Pr.

Reports

Theses

Posters

Preprints

Others

  • Juszczyszyn, K., Budka, M., Musial, K.. Complex Network Model - a New Perspective. The International School and Conference on Network Science (NetSci2011).
  • Schierz, A.C., Budka, M., Apeh, E.. Winners' notes. Using Multi-Resolution Clustering for Music Genre Identification. TunedIT.

Profile of Teaching PG

  • L7 (MSc) Computer Vision (2019-)
  • L7 (MSc) Business Intelligence (2011-2018)
  • L7 (MSc) Search and Optimisation (2020)

Profile of Teaching UG

  • L6 Data Mining (2013-)

Grants

  • KTP - Nourish Care Systems Ltd (Innovate UK, 01 Sep 2025). Awarded
  • KTP - Crowd Group Ltd (Innovate UK, 01 Jul 2024). In Progress
  • KTP - Bluestar Software Ltd (Innovate UK, 08 Apr 2024). In Progress
  • KTP - ImproveWell Ltd (Innovate UK, 01 Sep 2023). In Progress
  • AI for automatic vertebrae motion tracking in fluoroscopic images (Medical Research Council (MRC), 01 Aug 2022). Completed
  • KTP - Bluestar Software Ltd. (InnovateUK, 01 Oct 2019). Completed
  • KTP - We Are Base Ltd (Innovate UK, 26 Oct 2015). Completed
  • Integrated software system for the 3-dimensional capture and analysis of footwear evidence (Natural Environment Research Council [2006-2012], 01 May 2015). Completed

Public Engagement & Outreach Activities

  • New Scientist Live 2019 (06 Oct 2019-12 Oct 2019)
  • Public outreach exhibition - "Dinosaurs to Forensics" (04 Jul 2017-09 Jul 2017)

Qualifications

  • PhD in Computational Intelligence (Bournemouth University, UK, 2010)
  • BSc (Hons) in Ant Colony System for Cluster Analysis (University of Silesia, Poland, 2005)
  • MSc/BSc (dual) in E-banking: threats and safeguards (University of Economics in Katowice, Poland, 2003)

Memberships

The data on this page was last updated at 05:00 on February 17, 2026.