Upcoming Seminar - Machine learning the Universe: Opening the Pandora Box
We have arrived at the penultimate seminar in the LIV.DAT Virtual Seminar Series–Spring 2021. On Tuesday 25 May at 15:00 (Europe/London) we have the pleasure to welcome Professor Shirley Ho who is based at the Flatiron Institute and Princeton University (USA). She will present a talk titled Machine Learning the Universe: Opening the Pandora box.
This and other seminars in the series cover R&D outside of our center’s core research areas and give an insight into cutting edge research in this ready. Students, staff and anyone else who is interested are cordially invited to connect to the virtual seminar.
About the talk
Scientists have always attempted to identify and document analytic laws that underlie physical phenomena in nature. The process of finding natural laws has always been a challenge that requires not only experimental data, but also theoretical intuition. Often times, these fundamental physical laws are derived from many years of hard work over many generations of scientists. Automated techniques for generating, collecting, and storing data have become increasingly precise and powerful, but automated discovery of natural laws in the form of analytical laws or mathematical symmetries have so far been elusive. Over the past few years, the application of deep learning to domain sciences – from biology to chemistry and physics is raising the exciting possibility of a data-driven approach to automated science, that makes laborious hand-coding of semantics and instructions that is still necessary in most disciplines seemingly irrelevant. The opaque nature of deep models, however, poses a major challenge. For instance, while several recent works have successfully designed deep models of physical phenomena, the models do not give any insight into the underlying physical laws. This requirement for interpretability across a variety of domains, has received diverse responses. In this talk, the group’s analysis is presented which suggests a surprising alignment between the representation in the scientific model and the one learned by the deep model.
About the speaker
Shirley Ho joined the Flatiron Institute at in 2018 as leader of the Cosmology X Data Science group at the Center for Computational Astrophysics (CCA). Her research interests have ranged from fundamental cosmological measurements to exoplanet statistics to using machine learning to estimate how much dark matter is in the universe. Ho has broad expertise in theory, observation and data science. Ho’s recent interest has been on understanding and developing novel tools in statistics and machine learning techniques, and applying them to astrophysical challenges. Her goal is to understand the universe’s beginning, evolution and its ultimate fate. In her bidding to understand our Universe, Ho plans, builds and analyses data from a number of astronomical surveys such as Actacama Cosmology Telescope, Euclid, the Large Synoptic Survey Telescope, Simons Observatory, Sloan Digital Sky Survey and the Wide Field Infrared Survey Telescope.
For more information and how to register please follow this link.
Professor Shirley Ho
Cosmology X Data Science Group, Flatiron Institute, New York (USA) &
Department of Astrophysical Sciences, Princeton University (USA)
“Machine learning the Universe: Opening the Pandora Box”
Tuesday 25 May 2021 | 15:00 (Europe/London)
Dr Myriam Neaimeh
School of Engineering, Newcastle University & Data-Centric Engineering Group, Turing Institute
"Applying data science methods to modernise transport and electricity infrastructures"
Tuesday 15 June 2021 | 13:00 (Europe/London)