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 workshop 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 workshop 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 ICACCI, 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
Authors should submit their papers online. We use EDAS system for managing submissions and review process. Unregistered authors should first create an account on EDAS to log on. Detailed usage instruction on EDAS can be found here. The manuscripts should be submitted in PDF format. Please compare all author names in EDAS with the author list in your paper. They must be identical and in the same order. Further guidelines for submission are posted at: http://icacci-conference.org/2018/content/paper-submission-guidelines
All papers that conform to submission guidelines will be peer reviewed and evaluated based on originality, technical and/or research content/depth, correctness, relevance to conference, contributions, and readability. Acceptance of papers will be communicated to authors by email. Accepted and presented papers will be published in the conference proceedings and submitted to IEEE Xplore as well as other Abstracting and Indexing (A&I) databases. To be published in the Proceedings, an author of an accepted paper is required to register for the conference at the full rate. All accepted papers MUST be presented at the conference by one of the authors, or, if none of the authors are able to attend, by a qualified surrogate.
Papers Due: June 30, 2018
Acceptance Notification: July 30, 2018
Final Paper Deadline: August 20, 2018
- Dr. Snehanshu Saha, PES University
- Dr. Jayant Murthy, Indian Institute of Astrophysics
- Dr. Margarita Safonova, Birla Institute of Fundamental Research
- Dr. Tarun Deep Saini, Indian Institute of Science
Dr. Snehanshu Saha holds Masters Degree in Mathematical Sciences at Clemson University and Ph.D. from the Department of Mathematics at the University of Texas at Arlington in 2008. He was the recipient of the prestigious Dean's Fellowship during PhD. After working briefly at his Alma matter, Snehanshu moved to the University of Texas El Paso as a regular full time faculty in the Department of Mathematical Sciences. He is a Professor of Computer Science and Engineering at PES University since 2011 and heads the Center for Applied Mathematical Modeling and Simulation. He is also a visiting Professor at the department of Statistics, University of Georgia, USA. He has published 60 peer-reviewed articles in International journals. Dr. Saha is an IEEE Senior member, ACM Senior Member, Vice Chair-International Astrostatistics Association and Chair-Elect, IEEE Computer Society Bangalore Chapter. Snehanshu’s current and future research interests lie in Data Science, Astronomy, Healthcare and Machine Learning.
Dr. Jayant Murthy obtained his PhD in Physics from the Johns Hopkins University in 1987 on a project to understand the gas that is very near the Sun. He worked for 2 years as a National Research Council Fellow at NASA's Goddard Space Flight Center after which he returned to Johns Hopkins as Research Scientist where he worked on a number of spacecraft (Voyager, Hubble Space Telescope, FUSE etc.). He joined the Indian Institute of Astrophysics in 1999 where he is now a Senior Professor. Murthy has published over 100 papers in the scientific literature. He has mentored several students and young scientists and is currently heading the balloon and space payload group at IIA. He takes active interest and authored high impact peer-reviewed publications in Machine Learning driven astronomy.
Dr. Margarita Safonova was born in Russia. Dr. Safonova received her M.Sc. in Physics from Moscow State University in 1991, and a Ph.D. in Astrophysics from University of Delhi, Dept. Physics & Astrophysics in 2002. She worked in Cambridge, UK; Tehran, Iran, and from 2006 till 2016 worked in Indian Institute of Astrophysics, Bangalore. She is currently associated with M.P.Birla Institute of Fundamental Research, Bangalore. Her research interests are application of gravitational lensing in astrophysics and cosmology; UV astronomy from space and near space; exoplanets and habitability and machine learning methods in understanding astrophysical data.
Dr. Tarun Deep Saini is an Assistant Professor and Academic Coordinator, Joint Astronomy Programme at Indian Institute of Science. He holds a PhD from IUCAA, University of Pune awarded in 2001 and subsequently held visiting position at Institute of Astronomy, Cambridge, UK between 2001 and 2004. Tarun is an accomplished researcher and highly cited in the areas of Cosmology including Dark energy, Gravitational lensing, Structure formation in the Universe and Large scale structure in the Universe. He has been using computational methods like Markov Chain Monte Carlo simulations with moderate success. Lately, he has developed interest in statsitical learning and applications in Astronomy.
Website on Astroinformatics: