Computational Intelligence for Astroinformatics (CIAStro)
Data sets in Astronomy have been growing with the advent of many sky-surveys. The variety and complexity of the data sets at different wavelengths, cadences etc. imply that modeling, computational intelligence methods and machine learning need to be exploited to understand data-rich astronomy. Ranging from PB-sized archives to the recent example of the discovery of Gravitational Waves, the importance of data driven discovery in Astronomy has multiplied. That has resulted in the relatively new field of Astroinformatics: an interdisciplinary area of research where astronomers, mathematicians and computer scientists collaborate to solve problems in astronomy through the application of techniques developed in data science. Classical problems in astronomy now involve accumulation of large volumes of complex data with different formats and characteristic and cannot now be addressed using classical techniques. As a result, machine learning algorithms and data analytic techniques have exploded in importance, often without a mature understanding of the pitfalls in such studies.
The Conference aims to capture the baseline, set the tempo for future research in India and abroad and prepare a scholastic primer that would serve as a standard document for future research. We expect to discuss new developments in efficient models for complex computer experiments and data analytic techniques which can be used in astronomical data analysis in short term and various related branches in physical, statistical, computational sciences. The Conference aims to evolve and critique a set of fundamentally correct thumb rules and experiments, backed by solid mathematical theory and provide the marriage of astronomy and Machine Learning with stability and far reaching impact serving the context of specific science problems of interest to the audience.
Given the horizontal nature of SSCI, we hope to disseminate methods that are applicable to Astroinformatics but are not currently used, and also making CS practitioners aware of the interesting problems that complex astronomy data sets provide.
Topics of interest include, but are not limited to:
- Exoplanets (discovery, machine classification etc.)
- Classification of transients (Galactic and extragalactic)
- Multi-messenger astronomy aided by Machine learning
- Deep learning in astronomy
- MCMC on big data
- Statistical Machine Learning
- Bayesian Methods in Astronomy
- Meta-heuristic and Evolutionary Clustering methods and applications in Astronomy
- Theory of Metaheuristics and deep learning in Astronomy
- Astronomical time series analysis
- CI based interpolation methods for data fitting problems
TPC: Astronomy Track: Jayant Murthy, Ajit Kembhavi, Eric Feigelson, Jogesh Babu, Robert Mann, ZM Musielak, Ajith.P, Ashish Mahabal, Mousumi Das, Margarita Safonova, TD Saini, NInan Sajeeth, Abel Mendez
ML Track: BS DayaSagar, Sriparna Saha, Asif Ekbal,Gowri Srinivasa, Sivaji Bandopadhay, Bhabotosh Chanda, Saroj Meher, Sanghamitra Bandopadhay,
Modeling Track: Snehanshu Saha, James Peterson, Zachary David Voller
Paper Submissions: June 20, 2018
Acceptance Notification: September 10, 2018
Early Registration: September 15, 2018