Machine Learning for Science
Faced with a fundamental shift in both complexity and volume of the data, scientists have been struggling in finding ways to automate both analyses, and possibly the interpretation of the datasets. Machine Learning is a rapidly evolving field that has proven to be extremely efficient in solving challenges in multiple domains including signal processing, vision, self-driving, gaming, health and robotics. One may ask whether we can leverage the advances in machine learning and statistics for science. Our group specializes in applying and developing novel methods in machine learning on astrophysical challenges.
A few selected publications are here:
From Dark Matter to Galaxies with Convolutional Networks: Zhang et al., submitted, Arxiv:1902.05965
Learning to Predict Cosmological Structure Formation: He et al., submitted, Arxiv:1811.06533
Analysis of Cosmic Microwave Background with Deep Learning, Siyu He, Siamak Ravanbakhsh, Shirley Ho, ICLR 2018
Detecting damped Ly α absorbers with Gaussian processes. Garnett et al. 2017, MNRAS 2017
Estimating Cosmological Parameters from the Dark Matter Distribution, Ravanbakhsh et al., Proceedings of The 33rd International Conference on Machine Learning, volume 48, of JMLR: W&CP, 2016.
Optimal Ridge Detection using Coverage Risk, Chen et al., 2015, In Advances in Neural Information Processing Systems, pp. 316-324. 2015
Fast function to function regression, Oliva et al., 2015, AIStats, 2015