Research Output per year
Personal profile
Research interests
I work on advancing tools for human-centred data analysis. In contrast to machine learning methods that aim for automation (e.g., self-driving cars), human-centred data analysis methods aim to empower human users. In order to be effective, such methods and their results must be simple and interpretable yet provide valid answers to critical analysis questions.
I am interested in all aspects of data analysis methods: learning-theoretic foundations, efficient algorithms, and concrete applications. For the latter, I particularly focus on supporting scientific discoveries. For example, I collaborate with materials science researchers to pursue the data-driven discovery of novel functional materials.
Keywords
- Data Science
- Data analysis
- Machine Learning
- Data mining
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Research Output 2009 2018
Discovering reliable dependencies from data: hardness and improved algorithms
Mandros, P., Boley, M. & Vreeken, J., 2018, Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDM 2018: 17–20 November 2018 Singapore. Tao, D. & Thuraisingham, B. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 317-326 10 p. 8594856Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Discovering reliable approximate functional dependencies
Mandros, P., Boley, M. & Vreeken, J., 2017, KDD'17 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: August 13-17, 2017 Halifax, NS, Canada. Rosales, R. (ed.). New York NY USA: Association for Computing Machinery (ACM), p. 355-363 9 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Effective parallelisation for machine learning
Kamp, M., Boley, M., Missura, O. & Gärtner, T., 2017, NIPS Proceedings: Advances in Neural Information Processing Systems (NIPS 2017). Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R. & Vishwanathan, S. (eds.). San Deigo CA USA: Neural Information Processing Systems Foundation Inc., 22 p. (Advances in Neural Information Processing Systems; no. 30).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Efficiently discovering locally exceptional yet globally representative subgroups
Kalofolias, J., Boley, M. & Vreeken, J., 2017, Proceedings - 17th IEEE International Conference on Data Mining, ICDM 2017: 18–21 November 2017 New Orleans, Louisiana. Raghavan, V., Alu, S., Karypis, G., Miele, L. & Wu, X. (eds.). Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, p. 197-206 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Identifying consistent statements about numerical data with dispersion-corrected subgroup discovery
Boley, M., Goldsmith, B. R., Ghiringhelli, L. M. & Vreeken, J., Sep 2017, In : Data Mining and Knowledge Discovery. 31, 5, p. 1391-1418 28 p.Research output: Contribution to journal › Article › Research › peer-review
Open Access
Prizes
Best-Paper-Award ICDM 2018
Panagiotis Mandros (Recipient), Mario Boley (Recipient) & Jilles Vreeken (Recipient), 2018
Prize: Prize (including medals and awards)
Best-Student-Paper-Award ICDM 2008
Mario Boley (Recipient) & Henrik Grosskreutz (Recipient), 2008
Prize: Prize (including medals and awards)