Apply to the BIGSKYEARTH Training School

Apply to the BIGSKYEARTH Training School

The 1st BigSkyEarth Training School will take place at the German Aerospace Center facilities in Oberpfaffenhofen, Germany, 4-9 April, 2016.

It will gather around 30 researchers for hands-on training on Big Data analytics in astronomy and Earth observation with six expert instructors from the EU and the USA.

The COST Action BigSkyEarth provides grants to 18 trainees from BigSkyEarth member countries for travel and costs of the school attendance.

Eligibility Rules

Applicants can be PhD students or PhD holders from any country.

However, only applicants from BigSkyEarth countries and institutions are eligible for the financial support provided by BigSkyEarth (see the list of countries and institutions below).
There will be special considerations with respect to supporting COST policies on promoting gender balance, enabling Early Career Investigators (ECI) and broadening geographical inclusiveness.

Costs and Fees, Financial Support

There is no registration fee, but participants should anticipate costs of travel, accommodation and meals. The estimated costs, besides travel, are 50EUR/night for accommodation and 20EUR/day for meals. The organizers can help with finding accommodation close to the venue.

The BigSkyEarth COST Action will provide 18 grants for selected participants who are coming from institutions in: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, fYR Macedonia, Germany, Greece, Hungary, Ireland, Israel, Italy, Lithuania, Malta, Netherlands, Poland, Portugal, Romania, Serbia, Slovenia, Spain, United Kingdom.
Members of the Byurakan Astrophysical Observatory from Armenia are also eligible for grants.

Grants do not necessarily cover all expenses related to attending the Training School, but it should be enough to cover the majority of the overall costs of travel, accommodation and meal expenses.


The German Aerospace Center at Oberpfaffenhofen, Germany is one of Germany’s largest research centers. Located near the A96 motorway between Munich and Lindau, the site is home to eight scientific institutes and currently employs approximately 1700 people. The research center’s main fields of activity include participating in space missions, climate research, research and development in the field of earth observation, developing navigation systems and advanced robotics development.

List of important dates

  • Deadline for applications: February 19, 2016
  • Candidate selection: March 1, 2016
  • Training School: April 4-9, 2016

Selection Criteria

Candidates will be evaluated based on their proposed research project and their motivation letter. However, there will be special considerations with respect to supporting COST policies on promoting gender balance, enabling Early Career Investigators (ECI) and broadening geographical inclusiveness.

How to Apply and Acceptance Process

  • Applicants should prepare:
    • A short CV
    • A list of publications
    • A short (maximum one page) description of the applicant’s research project that would benefit from this Training School
    • A short (maximum one page) description of the background knowledge of topics covered by this Training School and motivation for participating at the school.
  • If applying for a grant support from BigSkyEarth then specify the travel route and an estimate of the travel costs.
  • Applicants will be evaluated based on their own project proposals.
  • Organizers will cluster, adapt and merge student proposals into manageable units.
  • Students are expected to extensively study online material prior to attending.

The application documents should be sent to Marco Quartulli at <>.

Short description of objectives

Challenges are similar in astronomy and Earth Observation, with signal processing, statistics, machine learning, and computer science as the common denominator. The Training School aims at boosting the communication within and between disciplines and applications areas by propagating and advancing relevant common solutions developed within Big Data analysis and management research and industrial environments.

The goal is to contribute with a valuable know-how encompassing from sensor and data modelling, features extraction and metadata, information representation, data structures, pattern recognition, statistical/machine learning, data analytics, advanced visualisation, to data mining and KDD. Besides, specific computer science topics will be addressed as particular programming techniques, data structures in large databases, cloud computing, and related topics. The key aspect will be addressing all these topics in synergy to set in a logical interdisciplinary framework building bridges between diverse areas.


(Details about the instructors are below)
1. Introduction and fundamentals
• Introduction to signal processing and Machine Learning for Big Data (Mahabal, McNamee, Brescia)
• Big Data and Cloud Computing architecture fundamentals (Brescia)
• Open Source resources: an overview (Quartulli)

2. Sensor Signal Processing
• Sensors in astronomy (Brescia, Mahabal)
• Earth Observation sensors (Datcu)
• Descriptors and metadata (Mahabal)
• Sensor signal models and feature extraction (Datcu, Quartulli)
• Feature extraction from time-series data (Mahabal)
3. Machine and statistical Learning
• Statistical inference and learning (McNamee, Brescia)
• Kernel methods (McNamee, Brescia)
• Deep learning (McNamee)
• Combining models and datasets (Mahabal)
4. Classification, clustering and indexing
• Data structures and representation (Quartulli)
• Data Base Management Systems (Quartulli)
• Classification and clustering algorithms (McNamee, Mahabal)
• Semantic representations (Datcu)
5. Optimisation and Visualisation
• Introduction to optimisation methods and algorithms (Datcu)
• Distributed optimisation (Quartulli)
• Dimensionality reduction (Brescia)
• Visual analytics (McNamee, Mahabal)
• Visual data mining (Mahabal)
• Immersive environments (Datcu)
6. Heterogeneous mining and analysis
• Computer Science and Networks (Datcu)
• External data sources and Data Mining (Datcu)

Current list of instructors

datcu_smallMihai Datcu, German Aerospace Center DLR, Germany
Prof. Mihai Datcu holds a professorship in electronics and telecommunications with UPB since 1981. Since 1993 he is scientist with the German Aerospace Center (DLR), Oberpfaffenhofen. He is developing algorithms for model based information retrieval from high complexity signals and methods for scene understanding from synthetic aperture radar (SAR) and interferometric SAR data. He is engaged in research related to information theoretical aspects and semantic representations in advanced communication systems.

ashish_smallAshish Mahabal, Caltech, USA
Dr. Ashish Mahabal is a Senior Research Scientist in Astronomy at Caltech. He is interested in astronomical transients and has worked on several sky surveys and is the co-chair of the LSST Transients and Variable Stars group. He works on Big Data, data fusion, machine learning and real-time classification of anomalies.

MacNamee_smallBrian Mac Namee, School of Computer Science at University College Dublin, Ireland
Dr. Brian Mac Namee, a lecturer at the School of Computer Science at University College Dublin, Ireland. His research focuses primarily on machine learning and artificial intelligence. However, he is also interested in novel applications to which AI and ML techniques can be applied. Examples include robotics, machine vision and image processing, games and augmented/virtual reality. Brian recently co-authored the MIT Press textbook “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies” ( which provides an introduction to the application of machine learning to predictive analytics problems across a range of industries and applications.

Brescia_smallMassimo Brescia, Osservatorio Astronomico di Capodimonte, Italy
Massimo Brescia is a senior astronomer with an educational experience on Computer Science and Artificial Intelligence. He has a direct experience as designer and developer of machine learning, data mining and web 2.0 infrastructures, together with past specialization in astronomical technologies and control systems engineering, being involved, with different responsibility roles, in various telescopes and instruments (VST, TNG, VIMOS, NEMO), as well as in more recent appointments in scientific data management (Euclid Mission Science Ground Segment, large multi-wavelength survey programs and International Virtual Observatory Alliance).

quartulli_smallMarco Quartulli, Vicomtech-IK4, Spain
Dr. Marco Quartulli has worked at Advanced Computer Systems, Italy from 1997 to 2010 on remote sensing ground segment engineering, image analysis and archive mining for ESA and national space agencies. From 2000 to 2003, he was with the Image Analysis Group at the Remote Sensing Technology Institute of the German Aerospace Center (DLR) in Germany, working on metric resolution synthetic aperture radar image understanding in urban environments and content-based image retrieval. Since 2010, he has joined the TV & Media department of Vicomtech-IK4 in Spain. He is the co–chair of the Big Sky Earth EU COST Action dedicated to Big Data management and analytics methodologies in remote sensing and astronomy.


Eric D. Feigelson, PennState, USA
Feigelson completed a dissertation at Harvard under Riccardo Giacconi, and took a post-doctoral position at MIT with Claude Canizares. In 2003, he started a faculty position at Penn State where he has remained. Here he teaches all levels of astronomy from Astro 001 to graduate level classes. He conducts research in two areas — X-ray studies of star formation and the cross-disciplinary astrostatistics — where he wrote seminal papers in the foundation of the subfields and continues to play a leading role.

Scientific Organizing Committee

Chair: Marco Quartulli, Vicomtech, Spain
Nicholas Walton, University of Cambridge, UK
Zeljko Ivezic, University of Washington, USA
Dejan Vinkovic, Science and Society Synergy institute, Croatia
Victor Debattista, University of Central Lancashire, UK
Blagoj Delipetrev, University Goce Delcev, fYR Macedonia
Robert Ross, Dublin Institute of Technology, Ireland
Brian Mac Namee, University College Dublin, Ireland
Gottfried Schwarz, German Aerospace Center DLR, Germany
Mihai Datcu, German Aerospace Center DLR, Germany
Darko Jevremovic, Astronomical Observatory Belgrade, Serbia
Giuseppe Longo, Università Federico II in Napoli, Italiy
Massimo Brescia, Osservatorio Astronomico di Capodimonte, Italy
André Moitinho de Almeida, Universidade de Lisboa, Portugal
Sven Loncaric, University of Zagreb, Croatia
Uroš Kostic, Aalta Lab, Slovenia
Olga Kurasova, Vilnius University, Lithuania
Christian Muller, BUSOC, Belgium
Andrea Marinoni, University of Pavia, Italy
Mattia Vaccari, University of the Western Cape, South Africa

Scientific Advisory Committee

Prof Javad ZARBAKHSH, Carinthia University of Applied Sciences, Austria
Dr. Andre Fuzfa, University of Namur, Belgium
Prof Ognyan KOUNCHEV, Institute of mathematics and informatics, Bulgaria
Prof Nikolay KIROV, New Bulgarian University, Bulgaria
Prof Milcho TSVETKOV, Institute of Mathematics and Informatics, Bulgaria
Dr Marko SUBASIC, University of Zagreb, Croatia
Dr Petr SKODA, Astronomical Institute of the Academy of Sciences, Czech Republic
Prof Pavel SMRZ, Brno University of Technology, Czech Republic
Dr Kim S. PEDERSEN, University of Copenhagen, Denmark
Prof Tiit KUTSER, Estonian Marine Institute, Estonia
Dr Kaupo VOORMANSIK, Tartu Observatory, Estonia
Dr Jouni PELTONIEMI, University of Helsinki, Finland
Dr Maria GRITSEVICH, Finnish Geospatial Research Institute, Finland
Dr Sanna MNKL, University of Jyvsky, Finland
Prof Emmanuel GANGLER, Clermont University, France
Prof Karine ZEITOUNI, University of Versailles-St-Quentin, France
Dr Sofian MAABOUT, University of Bordeaux, France
Dr Frdric ARENOU, Observatoire de Paris, France
Dr Vassilis TSIAFAKIS, University of Peloponnese, Greece
Prof Manolis KOUBARAKIS, National and Kapodistrian University of Athens, Greece
Dr Gyula SZABO, Gothard Astrophysical Observatory, Hungary
Prof Laszlo KISS, Hungarian Academy of Sciences, Hungary
Dr Bianca SCHOEN-PHELAN, Dublin Institute of Technology, Ireland
Dr Doron CHELOUCHE, University of Haifa, Israel
Prof Paolo GAMBA, Universit degli studi di Pavia, Italy
Dr Giovanni NICO, Consiglio Nazionale delle Ricerche, Italy
Dr Viktor MEDVEDEV, Vilnius University, Lithuania
Dr Alessio MAGRO, University of Malta, Malta
Dr Andrea DEMARCO, University of Malta, Malta
Dr Brown ANTHONY, Leiden University, Netherlands
Prof Bozena CZERNY, Copernicus Astronomical Center, Poland
Dr Michal POSYNIAK, Institute of Geophysics – Polish Academy of Sciences, Poland
Prof Rita RIBEIRO, UNINOVA, Portugal
Ms Paula ANCA, National Institute for Aerospace Research, Romania
Dr Ioan PLOTOG, Politehnica University of Bucharest, Romania
Ms Andreea BOSCORNEA, The National Institute of Aerospace Research “ELIE CARAFOLI” – INCAS, Romaina
Prof Boris ANTIC, BioSense Center, Serbia
Mr Predrag LUGONJA, University of Novi Sad, Serbia
Ms Luca DAZ-VILARIO, University of Vigo, Spain
Mr Laurent NOEL, University of Central Lancashire, United Kingdom
Dr Eduardo GONZALES-SOLARES, University of Cambridge, United Kingdom
Dr Miroslav MIRCHEV, Ss. Cyril and Methodius University in Skopje, fYR Macedonia
Prof Andreja NAUMOSKI, Ss. Cyril and Methodius University in Skopje, fYR Macedonia



With the current emergence of Terabyte(TB)-scale astronomical and Earth observation systems, the traditional approach to basic functions such as data searching, analytics or visualization are becoming increasingly difficult to handle. Simple database queries can result now in data subsets so large that they are incomprehensible, slow (or even impossible) to handle, and impossible to visualize with commodity visualization tools. Astronomy and remote sensing complement each other, as they are on the quest for new Big Data interpretation capabilities: both disciplines have peculiar data, typical data processing and analysis chains, and specific models to be fed with data. However, both disciplines lack the capabilities for easily accessible semantics-oriented browsing (usage of higher level descriptive expressions) in large data archives. Therefore, joint efforts to design and develop innovative Big Data tools should help users in many different fields and set new standards for many communities. This has identified several broad challenges to this line of reasoning that need multidisciplinary approach through international networking of experts and professionals. These challenges are then channelled into Action Objectives:
Challenge A: Digital curation and data access
Challenge B: New frontiers in visualization
Challenge C: Adaptation to new high performance computing (HPC) technologies
Challenge D: New generation of scientists in the age of interdisciplinarity
For more detail see the description of the Action in Memorandum of Understanding.