Previous Meetings and Events

Anomaly detection is a challenging problem which can greatly benefit from the use of machine learning (ML) methods: unsupervised as well as semi-supervised. ML algorithms must be able to process complex, massive data sets and search for anomalies under extreme conditions (very low signal-to-noise ratio, real-time data, etc). The range of applications for anomaly detection methods is vast, and advances made in one scientific field can frequently be transferred to other disciplines, to the benefit of both parties involved.

More details can be found here.

The 2023 conference on Machine Learning in astronomical surveys intend to critically review new techniques in the Machine Learning methods for astronomy.

In order to bring together the widest possible community, while limiting the carbon impact, this conference will be organized in hybrid mode and on two physical sites simultaneously, at the IAP in Paris and at the CCA/Flatiron Institute in New York.

More details can be found here.

The physical processes driving galaxy formation and evolution are key topics in both the fields of Cosmology and extragalactic astronomy. The interrelations between these mechanisms, at both individual level and in the larger context of the galaxy environment, are also essential factors in the study of the cosmological parameters.

The usage of machine learning techniques for the analysis of astronomical data has exploded in the last decade thanks to their potential to carry out a great variety of tasks with high accuracy and high processing speed, which allows to handle big data surveys efficiently. Their application to increasingly larger science cases in the field of extragalactic astronomy and Cosmology has brought significant advances in the area, but has also marked some challenges (interpretability, uncertainty estimation, etc) that need to be addressed for a robust utilization of these methodologies to galaxy evolution and their environment.

More details can be found here.

ADASS provides a forum for scientists and programmers concerned with algorithms, software, and software systems employed in the acquisition, reduction, analysis, and dissemination of astronomical and planetary science data. An important element of the program is to foster communication between developers and users with a range of expertise in the production and use of software and systems. The program consists of invited talks, contributed oral and poster papers, tutorials, user group meetings, and special interest group meetings (collectively “Birds of a Feather” meetings). ADASS is known for its many fruitful community discussions during coffee breaks and after hours.

More details can be found here.

What could be a bigger source of "big data" than the entire Universe? Researchers in statistics and computer science are developing new methods to analyse the "big data" that companies and governments want to understand. To test out these methods they need big datasets that everyone can share --- and new astronomical telescopes just happen to be gearing up to generate observations of billions of stars and galaxies that fit the bill. Insights from statistics will help astrophysicists to get the most out of these observations. The Astrostatistics 2023 workshop will bring together statisticians and astrophysicists to learn the technical languages of the two fields and identify how to work together to combine the excitement of space with big data.

Detailed information can be found here.

Frontiers in Machine Learning in Cosmology and Particle Physics 2023 is a workshop on the latest machine learning developments in the domains of cosmology and particle physics. It takes inspiration from the influential workshop series "Hammers & Nails", organised every other year by the Weizmann Institute of Science, Israel.

More details can be found here.

Astroinformatics 2023 aspires to continue the successful series of meetings over the last decade have attracted researchers engaged in the processing of astronomical data using modern computational methods. The scientific exchange between the astronomical and computational worlds is, as always, the main focus of the event.

Detailed information can be found here.

 The Statistical Challenges in Modern Astronomy (SCMA) conference is truly cross-disciplinary — presentations by invited scholars of astronomical and data sciences are mixed in a program that pursues both scientific and methodological goals.

Themes include statistical modeling of astronomical phenomena, discovering hidden astronomical signals, and enhancing the roles of machine learning for astrophysical insights. The SCMA VIII conference is being held at a critical moment in time, as astrostatistical efforts are thriving around the world with increasing impact on the broader research enterprise.

Detailed information can be found here.

This conference aims to explore the application of data-driven tools to learn about galaxy formation physics. It endeavours to maximize the gain from astrostatistics, data science, and machine learning for the galaxy formation field as a whole, by emphasizing the translation of data-driven results to physical understanding. This conference will focus on a sharing of expertise in data exploration and analysis tools, and an open discussion of how these may teach us about the physics of galaxy formation and evolution.

Detailed information can be found here.