Algorithmic Iterative Reticular Synthesis of Zeolitic Imidazolate Framework Crystals
Rong, Z., Chen, Z., Luong, F. et al. (2025) "Algorithmic iterative reticular synthesis of zeolitic imidazolate framework crystals" Nat. Synth.
CV
A full CV with publications is available in pdf format.
Education
- Ph.D in Computer Science, University of Bonn, 2011
- Diploma in Computer Science, University of Bonn, 2006
Affiliation History
- 2024 - today: Associate professor at the Department of Information Systems at Haifa University, Israel
- 2018 - today: Senior lecturer at the Department of Data Science and AI at Monash University, Australia (tenured until August 2024, currently adjunct)
- 2017 - 2018: Post-doctoral Fellow at Max Planck Institute for Informatics, Saarbrücken, Germany
- 2015 - 2017: Post-doctoral Fellow at Fritz Haber Institute of the Max Planck Society (Materials Science), Berlin, Germany
- 2011 - 2015: Post-doctoral Fellow at University of Bonn, Department of Information Systems and AI
- 2007 - 2014: Research Scientist at Fraunhofer Institute for Intelligent Analysis and Information Systems, St. Augustin
Publications
Cortical connectivity, local dynamics and stability correlates of global conscious states
Zhao, Y., Tsuchiya, N., Boley, M. et al. "Cortical connectivity, local dynamics and stability correlates of global conscious states". Commun Biol 8 (2025): 1391
Gradient Boosting versus Mixed Integer Programming for Sparse Additive Modeling
Yang, Fan, Pierre Le Bodic, and Mario Boley. "Gradient Boosting Versus Mixed Integer Programming for Sparse Additive Modeling." Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECMLPKDD 2025): 453-470.
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles
F Yang, P Le Bodic, M Kamp, M Boley. (2024). "Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles." AISTATS.
Bayes beats cross validation: fast and accurate ridge regression via expectation maximization
SY Tew, M Boley, DF Schmidt. (2023). "Bayes beats cross validation: fast and accurate ridge regression via expectation maximization." NeurIPS. 36
Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly
Y Lu, D Yalcin, PJ Pigram, LD Blackman, M Boley. "Interpretable machine learning models for phase prediction in polymerization-induced self-assembly." Journal of Chemical Information and Modeling 63, no. 11 (2023): 3288-3306.
Relative Flatness and Generalization
Petzka, Henning, Michael Kamp, Linara Adilova, Cristian Sminchisescu, and Mario Boley. "Relative flatness and generalization." Advances in neural information processing systems 34 (NeurIPS 2021): 18420-18432.
Better Short than Greedy: Interpretable Models through Optimal Rule Boosting
M Boley, S Teshuva, P Le Bodic, G Webb. (2021). "Better Short than Greedy: Interpretable Models through Optimal Rule Boosting." SDM.
Identifying domains of applicability of machine learning models for materials science
C Sutton, M Boley, LM Ghringhelli, M Rupp, J Vreeken, M Scheffler. (2020). "Identifying domains of applicability of machine learning models for materials science." Nature Communications. 11(1),4428.