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. He currently acts as Head of Research in the Department of Computing and Informatics.

Research

My research interests lie in a broadly understood area of machine learning and data science, with a particular focus on practical applications. Throughout my career, I was involved in a number of research projects with industry, with a particular focus on tangible impact and societally useful innovation.

Journal Articles

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.
  • 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., 2013. Understanding the Other Side - The Inside Story of the INFER Project. 1-9.
  • 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

  • Wahid-Ul-Ashraf, A., Budka, M. and Musial-Gabrys, K., 2018. Newton’s gravitational law for link prediction in social networks. 93-104.
  • Salvador, Budka, M. and Quay, T., 2017. Automatic Transport Network Matching Using Deep Learning. In: European Transport Conference 2017 4-6 October 2017 Barcelona, Spain.
  • Budka, D., Bennett, M.R., Budka, M. and Bakirov, R., 2017. COMPARING FOOTWEAR EVIDENCE AND THE POWER OF THE 3RD DIMENSION. 162-163.
  • Budka, D., Budka, M., Bennett, M.R. and Bakirov, R., 2017. CAPTURE AND ANALYSIS OF 3D FOOTWEAR EVIDENCE: NEW HORIZONS AND OPPORTUNITIES. 172.
  • Salvador, Budka, M. and Gabrys, B., 2017. Modelling Multi-Component Predictive Systems as Petri Nets. In: Kacprzyk, J. and Owsinski, J., eds. 15th Annual Industrial Simulation Conference 31 May-2 June 2017 Warsaw, Poland. Eurosis-ETI, 17-23.
  • Salvador, M, Budka, M., Quay, T. and Carver-Smith, A., 2016. Improving transport timetables usability for mobile devices: a case study. In: 11th International Conference on the Practice and Theory of Automated Timetabling 23-26 August 2016 Udine, Italy.
  • 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).
  • Salvador, Budka, M. and Gabrys, B., 2016. Towards automatic composition of multicomponent predictive systems. In: 11th International Conference on Hybrid Artificial Intelligence Systems 18-20 April 2016 Seville, Spain.
  • Bennett, M., Morse, S. and Budka, M., 2015. Tracks and sediments: evolutionary stasis in foot function?
  • 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. Springer, 49-60.
  • 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.
  • Balaguer-Ballester, E., Tabas-Diaz, A. and Budka, M., 2014. Empirical identification of non-stationary dynamics in time series of recordings. 142-151.
  • Król, D., Budka, M. and Musial, K., 2014. Simulating the information diffusion process in complex networks using push and pull strategies. 1-8.
  • 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. 581-586.
  • Juszczyszyn, K., Musial, K. and Budka, M., 2011. On analysis of complex network dynamics – changes in local topology. 61-70.
  • Juszczyszyn, K., Musial, K. and Budka, M., 2011. Link prediction based on Subgraph evolution in dynamic social networks. 27-34.
  • 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. Lecture Notes in Computer Science, 2011, Volume 6804: Springer-Verlag, 725-733.
  • Juszczyszyn, K., Budka, M. and Musial, K., 2011. The dynamic structural patterns of social networks based on triad transitions. 581-586.
  • 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. IEEE, 1-8.
  • Budka, M. and Gabrys, B., 2009. Electrostatic Field Classifier for Deficient Data. Heidelberg: Springer, 311-318.

Reports

  • 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

  • Schierz, A.C., Budka, M. and Apeh, E., 2011. Winners' notes. Using Multi-Resolution Clustering for Music Genre Identification. TunedIT.
  • Juszczyszyn, K., Budka, M. and Musial, K., 2011. Complex Network Model - a New Perspective. The International School and Conference on Network Science (NetSci2011).

Grants

  • KTP - We Are Base Limited (Innovate UK, 26 Oct 2015). In Progress

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 04:06 on August 20, 2018.