Rethinking the Data-Driven Discovery of Rare Phenomena (DARE)

Due to its interest in rare phenomena, scientific discovery typically resembles the proverbial hunt for the needle in a haystack. Materials science seeks stable materials that have desired properties within huge combinatorial spaces. Neuroscience looks for accurate and specific explanations of critical neuro-electric phenomena like seizures or alpha-rhythms in infinite parameter spaces of dynamical brain models. Through this project, we enable the data-driven discovery of such important rare phenomena from high-dimensional observational data collections.

Funding

Agency: Australian Research Council
Grant: DP210100045
Period: 2021–2025

Investigators

Chief Investigators

• A/Prof. Mario Boley (Principal Chief Investigator), University of Haifa and Monash University
• A/Prof. Levin Kuhlmann, Monash University
• A/Prof. Daniel Schmidt, Monash University

Post-docs and PhD Students

• Dr. Phuc (Felix) Luong, Monash University
• Simon Teshuva, Monash University
• Ziyi (Neil) Liu, Monash University

Partner Investigators and Key Collaborators

• Dr. Lewis Blackman, CSIRO Biomedical Manufacturing
• Prof. Wray Buntine, Vin University
• Prof. Matthias Scheffler, Novel-Materials Discovery Laboratory at the FHI Molecular Physics Department of the Max Planck Society
• Prof. Omar Yaghi, University of California, Berkeley

Publications

1. 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.

2. SY Tew, M Boley, DF Schmidt. (2023). "Bayes beats cross validation: fast and accurate ridge regression via expectation maximization." NeurIPS. 36

3. 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.

4. F Yang, P Le Bodic, M Kamp, M Boley. (2024). "Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles." AISTATS.

5. Yang, Fan, Pierre Le Bodic, and Mario Boley. "Gradient Boosting Versus Mixed Integer Programming for Sparse Additive Modeling." In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 453-470. Cham: Springer Nature Switzerland, 2025.