MG_4302Q_small.jpg Email: mirko.bunse At sign cs.tu-dortmund.de
Phone: 0231/755-6487
Room-No.: JvF25 R103


Mirko finished his Ph.D. in 2022 at TU Dortmund University. There, he also received his M.Sc. degree in Computer Science in 2018, with honors and a specialization in Data Science. His involvement with machine learning ranges back to 2016, when he joined our AI group as a student assistant. Before, he was working as a software developer for geo-information systems and as a student assistant at Paderborn University, where he completed a B.Sc. in 2014.


Mirko studies the application of machine learning in astro-particle physics. This application field is characterized by extreme class imbalances, by a domain-specific post-processing of predictions, and by the fact that all training data is simulated while the learned models must be valid in practice. Mirko's current focuses are the aggregation of predictions in terms of ordinal quantification (a.k.a. unfolding), learning under class-conditional label noise, and the smart control of simulations through active class selection.

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Bunse/2022b Bunse, Mirko. Unification of Algorithms for Quantification and Unfolding. In Worksh. on Mach. Learn. for Astropart. Phys. and Astron., pages 459--468, Gesellschaft für Informatik e.V., 2022.
Bunse/2022c Mirko Bunse. On Multi-Class Extensions of Adjusted Classify and Count. In Int. Worksh. on Learn. to Quantify: Meth. and Appl., pages 43--50, 2022. Arrow Symbol
Bunse/2022d Bunse, Mirko. Machine Learning for Acquiring Knowledge in Astro-Particle Physics. TU Dortmund University, 2022. Arrow Symbol
Bunse/etal/2022b Mirko Bunse and Alejandro Moreo and Fabrizio Sebastiani and Martin Senz. Ordinal Quantification through Regularization. In Europ. Conf. on Mach. Learn. and Knowl. Discov. in Da\-ta\-ba\-ses, Springer, 2022.
Senz/Bunse/2022a Senz, Martin and Bunse, Mirko. DortmundAI at LeQua 2022: Regularized SLD. In Conf. and Labs of the Eval. Forum, Vol. 3180, pages 1911--1915, 2022. Arrow Symbol
Senz/etal/2022a Martin Senz and Mirko Bunse and Katharina Morik. Certifiable Active Class Selection in Multi-Class Classification. In Worksh. on Interact. Adapt. Learn., pages 68--76, CEUR Worksh. Proc., 2022.
Bunse/2021a Bunse, Mirko and Morik, Katharina. Certification of Model Robustness in Active Class Selection. In Europ. Conf. on Mach. Learn. and Knowledge Discovery in Databases (ECML-PKDD), Springer, 2021. Arrow Symbol
Bunse/Morik/2021a Bunse, Mirko and Morik, Katharina. Active Class Selection with Uncertain Deployment Class Proportions. In Workshop on Interactive Adaptive Learning, CEUR Workshop Proceedings, 2021.
Pfahler/etal/2021a Pfahler, Lukas and Bunse, Mirko and Morik, Katharina. Noisy Labels for Weakly Supervised Gamma Hadron Classification. 2021. Arrow Symbol
Bunse/etal/2020a Bunse, Mirko and Weichert, Dorina and Kister, Alexander and Morik, Katharina. Optimal Probabilistic Classification in Active Class Selection. In International Conference on Data Mining (ICDM), pages 942--947, IEEE, 2020. Arrow Symbol
Heppe/etal/2021a Nikolaos Nikolaou and Ingo P. Waldmann and Angelos Tsiaras and Mario Morvan and Billy Edwards and Kai Hou Yip and Giovanna Tinetti and Subhajit Sarkar and James M. Dawson and Vadim Borisov and Gjergji Kasneci and Matej Petkovic and Tomaz Stepisnik and Tarek Al-Ubaidi and Rachel Louise Bailey and Michael Granitzer and Sahib Julka and Roman Kern and Patrick Ofner and Stefan Wagner and Lukas Heppe and Mirko Bunse and Katharina Morik. Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots. In The Astronomical Journal (under review), 2020. Arrow Symbol
Bunse/etal/2017a Bunse, M. and Bockermann, C. and Buss, J. and Morik, K. and Rhode, W. and Ruhe, T.. Smart Control of Monte Carlo Simulations for Astroparticle Physics. In Pascal Ballester and Jorge Ibsen and Mauricio Solar and Keith Shortridge (editors), Astronomical Data Analysis Software and Systems (ADASS XXVII), Vol. 522, pages 417--420, Astronomical Society of the Pacific, 2019.
Bunse/etal/2019a Bunse, Mirko and Saadallah, Amal and Morik, Katharina. Towards Active Simulation Data Mining. In Kottke, Daniel and Lemaire, Vincent and Calma, Adrian and Krempl, Georg and Holzinger, Andreas (editors), Proc. of the 3rd Int. Tutorial and Workshop on Interactive Adaptive Learning at ECML-PKDD 2019, Vol. 2444, pages 104--107, CEUR Workshop Proceedings, 2019. Arrow Symbol
Bunse/Morik/2019a Bunse, Mirko and Morik, Katharina. What Can We Expect from Active Class Selection?. In Jäschke, Robert and Weidlich, Matthias (editors), Lernen, Wissen, Daten, Analysen (LWDA) conference proceedings, Vol. 2454, pages 79--83, 2019. Arrow Symbol
Bunse/2018a Bunse, Mirko. DSEA Rock-Solid -- Regularization and Comparison with other Deconvolution Algorithms. TU Dortmund, 44221 Dortmund, Germany, 2018.
Bunse/etal/2018b Bunse, Mirko and Piatkowski, Nico and Ruhe, Tim and Rhode, Wolfgang and Morik, Katharina. Unification of Deconvolution Algorithms for Cherenkov Astronomy. In 5th IEEE DSAA, 2018. Arrow Symbol
Bunse/Piatkowski/2018a Bunse, Mirko and Piatkowski, Nico and Morik, Katharina. Towards a Unifying View on Deconvolution in Cherenkov Astronomy. In Gemulla, Rainer and Ponzetto, Simone Paolo and Bizer, Christian and Keuper, Margret and Stuckenschmidt, Heiner (editors), Lernen, Wissen, Daten, Analysen (LWDA) conference proceedings, Vol. 2191, pages 73--77, 2018. Arrow Symbol
PG594/2016a Mohamed Asmi and Alexander Bainczyk and Mirko Bunse and Dennis Gaidel and Michael May and Christian Pfeiffer and Alexander Schieweck and Lea Schönberger and Karl Stelzner and David Sturm and Carolin Wiethoff and Lili Xu. PG594 -- Big Data. No. 5, TU Dortmund, 2016.


Supervised Theses