Marcin Budka

Professor Marcin Budka

  • Professor Of Data Science
  • Poole House P256b, Talbot Campus, Fern Barrow, Poole, BH12 5BB
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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. and Budka, M., 2014. Empirical identification of non-stationary dynamics in time series of recordings. Springer Verlag, 142-151.
  • Musial, K., Budka, M. and Blysz, W., 2013. Understanding the Other Side - The Inside Story of the INFER Project. 1-9.
  • Budka, M., 2013. Clustering as an example of optimizing arbitrarily chosen objective functions. In: Nguyen, N., Trawinski, B., Katarzyniak, R. and Geun-Sik, J., eds. Advanced Methods for Computational Collective Intelligence. Springer Berlin / Heidelberg, 177-186.
  • Musial, K., Budka, M. and Blysz, W., 2012. Understanding the Other Side - the Inside Story of the INFER Project. In: Howlett, R.J. and Jain, L.C., eds. Innovation through Knowledge Transfer 2012. Springer Berlin Heidelberg, (in press).
  • Schierz, A.C. and Budka, M., 2011. High-Performance Music Information Retrieval System for Song Genre Classification. In: Kryszkiewicz, M., Rybinski, H., Skowron, A. and Ras, Z.W., eds. Proceedings of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS'11). Springer-Verlag.

Conferences

  • Reich, T., Budka, M. and Hulbert, D., 2020. Impact of Data Quality and Target Representation on Predictions for Urban Bus Networks. 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2843-2852.
  • Ali, A.R., Budka, M. and 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. and 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. and 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. and Quay, T., 2018. Automatic Transport Network Matching Using Deep Learning. Transportation Research Procedia, 31, 67-73.
  • Ali, A.R., Gabrys, B. and Budka, M., 2018. Cross-domain Meta-learning for Time-series Forecasting. Procedia Computer Science, 126, 9-18.
  • Wahid-Ul-Ashraf, A., Budka, M. and Musial, K., 2018. NetSim - The framework for complex network generator. KES, 126, 547-556 Elsevier.
  • Budka, D., Bennett, M.R., Budka, M. and 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. and Bakirov, R., 2017. CAPTURE AND ANALYSIS OF 3D FOOTWEAR EVIDENCE: NEW HORIZONS AND OPPORTUNITIES. FORENSIC SCIENCE INTERNATIONAL, 277, 172.
  • Salvador, M.M., Budka, M. and Gabrys, B., 2017. Modelling multi-component predictive systems as petri nets. 15th International Industrial Simulation Conference 2017, ISC 2017, 17-23.
  • Salvador, Budka, M. and Gabrys, B., 2016. Adapting Multicomponent Predictive Systems using Hybrid Adaptation Strategies with Auto-WEKA in Process Industry. In: AutoML 2016 @ ICML 20-24 June 2016 New York (USA).
  • Martin Salvador, M., Budka, M. and 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. and 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. and Budka, M., 2015. Tracks and sediments: evolutionary stasis in foot function? 31st IAS Meeting of Sedimentology, 22-25 June 2015, Krakow, Poland.
  • Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Martin Salvador, M., Schwan, S., Tsakonas, A. and 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 October-1 November 2014 Leuven, Belgium. Advances in Intelligent Data Analysis XIII, 8819, 49-60 Springer.
  • Balaguer-Ballester, E., Tabas, A. and Budka, M., 2014. Attracting Dynamics, non-stationarity and trial-to-trial variability in frontal ensemble recordings. In: Bernstein Computational Neuroscience Conference 2014 2-5 September 2014 Tubingen.
  • Król, D., Budka, M. and 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.
  • Balaguer-Ballester, E., Tabas-Diaz, A. and 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, 142-151.
  • Budka, M., Musial, K. and 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. and Budka, M., 2012. A probabilistic approach to structural change prediction in evolving social networks. , 996-1001.
  • Juszczyszyn, K., Budka, M. and Musial, K., 2011. The Dynamic Structural Patterns of Social Networks Based on Triad Transitions. Advances in Social Networks Analysis and Mining (ASONAM), 2011 International Conference on, 581-586.
  • Schierz, A.C. and Budka, M., 2011. High--Performance music information retrieval system for song genre classification. In: Kryszkiewicz, M., Rybinski, H., Skowron, A. and Ras, Z.W., eds. Proc. of the 19th International Symposium on Methodologies for Intelligent Systems (ISMIS'11) June 2011 Warsaw, Poland. , 725-733 Lecture Notes in Computer Science, 2011, Volume 6804: Springer-Verlag.
  • Juszczyszyn, K., Budka, M. and Musial, K., 2011. The dynamic structural patterns of social networks based on triad transitions. , 581-586.
  • Juszczyszyn, K., Musial, K. and Budka, M., 2011. Link prediction based on Subgraph evolution in dynamic social networks. , 27-34.
  • Juszczyszyn, K., Musial, K. and Budka, M., 2011. 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, 61-70.
  • Budka, M. and Gabrys, B., 2010. Correntropy–based density–preserving data sampling as an alternative to standard cross–validation. In: World Congress on Computational Intelligence (WCCI 2010) 18-23 July 2010 Barcelona, Spain. , 1-8 IEEE.
  • Budka, M. and Gabrys, B., 2009. Electrostatic Field Classifier for Deficient Data. , 311-318 Heidelberg: Springer.

Reports

  • Abbas, A., Budka, M. and Gabrys, B., 2015. A Review of Meta-level Learning in the Context of Multi-component, Multi-level Evolving Prediction Systems.. Bournemouth University.
  • Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Schwan, S., Tsakonas, A. and Žliobaitė, I., 2011. From sensor readings to predictions: on the process of building soft sensors.
  • Budka, M., 2010. Coolant temperature forecasting for transformers data.

Theses

Others

  • Juszczyszyn, K., Budka, M. and Musial, K., 2011. Complex Network Model - a New Perspective. The International School and Conference on Network Science (NetSci2011).
  • Schierz, A.C., Budka, M. and Apeh, E., 2011. 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 - Bluestar Software Ltd. (InnovateUK, 01 Oct 2019). In Progress
  • KTP - We Are Base Limited (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] Dinosaurs to forensics
  • [Royal Society Summer Science Exhibition 2017] Dinosaurs to Forensics. Using ichnology to understand fossil trackways and track evidence at crime scenes

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