MMLA 2019 : International Conference on Modeling, Machine Learning and Astronomy
Call For Papers
Department of Computer Science and Engineering, Center for AstroInformatics, Modeling and Simulation, PES University and International Astrostatistics Association are proud to present a unique conference on Modeling, Machine Learning and Astronomy.
Theory of machine learning, deep learning in particular has been witnessing an implosion lately in deciphering the “black-box approaches”. Optimizing deep neural networks is largely thought to be an empirical process, requiring manual tuning of several parameters. Drawing insights in to these parameters gained much attention lately. The conference aims to focus on gaining theoretical insights in the computation and setting of these parameters and solicits original work reflecting the influence of such theoretical framework on experimental results on standard datasets and architectures. The conference aims to garner valuable talking points from optimization studies, another aspect of deep learning architectures and experiments. It is in this spirit, the organizers wish to bridge metaheuristic optimization methods with deep neural networks and solicit papers that focus on exploring alternatives to gradient descent/ascent types methods. Papers with theoretical insights and proofs are particularly sought after, with or without limited experimental validation. We would welcome cutting-edge research on aspects of deep learning theory used in the fields of artificial intelligence, statistics and data science, theoretical and numerical optimization.
Habitability outside the solar system is an intriguing topic and center of focused research for at least a decade now. Coupled with this, there are advances in the fields of Artificial Life and Complex Adaptive Systems aiming to understand and synthesize life-like systems. The conference brings together material understanding design diversity, complexity, and adaptability of life and their rapid influence in areas of engineering and the Sciences. We wish to solicit ideas from nature and their generalizations from life and their translations into engineering and science.
Data is at the heart of this. Astronomy is a fascinating case study as it had embraced big data embellished by 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 astronomy. The importance of data driven discovery in Astronomy has given birth to an exciting new field known as astroinformatics. The inter-disciplinary study brings together machine learning theorists, astronomers, mathematicians and computer scientists underpinning the importance of machine learning algorithms and data analytic techniques.
The Conference aims to set a unique ground as an amalgamation of the diverse ideas and techniques while staying true to the baseline. We expect to discuss new developments in modeling, machine learning, design of complex computer experiments and data analytic techniques which can be used in areas beyond astronomical data analysis. Given the horizontal nature of MMLA, we hope to disseminate methods that are area-agnostic but currently of interest to the broad community of science and engineering.
Topics of interest include, but are not limited to:
• Exoplanets (discovery, machine classification etc.)
• Unsupervised, semi-supervised, and supervised representation learning
• Representation learning for reinforcement learning
• Metric learning and kernel 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
• Optimization methods
• Swarm intelligence
• Multi-objective optimization
• Dynamical Systems and Complexity
• Information-Theoretic Methods in Life-like Systems
• Predictive Methods for Complex Adaptive Systems and Life-like Systems
Note to Authors: Proof of concept papers, applied on toy data sets are welcome as long as the theory and models are solid. Papers with applications in some area of Engineering or Science, without theoretical insight would be “desk-rejected”. Application in Astronomy is strongly encouraged but the lack of it would not be a reason for rejection. The submission and review process follow (strict) double blind protocol. Manuscripts attempting to reveal author identity in direct or indirect manner will be summarily rejected. At least one of the authors of accepted manuscript needs to register and present during the conference.
Formatting Instructions: IEEE 2-column format.
Page length and Tracks:
A)Track I: Applied Machine Learning B) Track II: Machine Learning in Astronomy
8 pages (Full length original manuscript)
10 Pages (Survey articles on recent advances in Astroinformatics)
4 pages (theoretically insightful papers in Machine learning/Deep learning/Computational Intelligence; application area/ data sets need not be mentioned)
1. Ashish Mahabal, Caltech and Jet Propulsion Labs
2. Oleg Malkov, Russian Academy of Sciences--Parameterization of stars and determination of interstellar extinction from multicolor photometry
Invited Talk: Tarun Deep Saini, IISc
Kartik GuruMurthy, Amazon
Ajith Parmeswaran, ICTS
Anand Sengupta (IIT Gandhinagar)
Suresh Sundaram, IISc
Sriparna Saha, IIT Patna--"Multiobjective Clustering Techniques: Theory
Najam Hasan, Moulana Azad National Urdu University
Pre-conference Tutorial on Machine Learning/Deep Neural Nets:
Archana Mathur, Indian Statistical Institute, Bangalore-“AutoEncoders and ANN”
Sheelu Abraham, IUCAA - “Convolutional Neural Network as a tool for galaxy feature identifier"
Kaushal Sharma, IUCAA—“Hands on introduction to spectral classification”
Sriparna Saha, Maulana Abul Kalam Azad University of Technology—"Fuzzy Systems”
Look forward to your scholarly contribution and active participation!
Snehanshu Saha, PES University
Jayant Murthy, Indian Institute of Astrophysics