Apply to the BigSkyEarth Training School: Big Data in Simulations and Observations

Apply to the BigSkyEarth Training School: Big Data in Simulations and Observations

BigSkyEarth is organizing its final Training School, focused on Big Data in simulations and observations. It will take place at the Tuorla observatory, the University of Turku, on Nov. 26 – Dec.1, 2018. Key practitioners from the astronomy, the Earth observation and the computer science domains will be participating in the School contributing the perspectives of both the academic and the industrial sector. Grants will be made available by the Action for a number of participants. The procedure for applying and for the selection of participants is described below.

Past editions of the Training School

The 1st edition of the BigSkyEarth Training School took place at the German Aerospace Center facilities in Oberpfaffenhofen, Germany, 4-9 April, 2016. It gathered 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 second edition took place in Preston at the University of Central Lancanshire, UK, on April 3-8 2017 and focused on the creation of effective visualisations of big data bases. The third edition was held at the Vicomtech Research Center, Spain, in April 3-9, 2018.

Eligibility Rules

Applicants can be PhD students or PhD holders from any country. However, only applicants from BigSkyEarth COST 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.


(For travel instructions, see below how to reach Tuorla.) The Science Centre Tuorla has been established to the premises of the Tuorla observatory in 2018. It is the place where the schools and public meet the scientists and it provides teaching to schools and to the teachers. The Science Centre is also an excellent place for small meeting and research schools. The site has several telescopes located around its main buildings and the one meter Dall-Kirkham reflector is the largest optical telescope in Finland. Since 2008, the Tuorla Planetarium has been operating next to the observatory. It is the departing point for the TimeTrek route (13.7 km) which portrays the whole history of the Universe, the Earth and the biosphere.

List of important dates

Dates to be noted include:
– Submit your application no later than: October 31, 2018 (but submit as soon as possible, since the candidate selection is performed until the quota is filled)
– Candidate selection: within a week after submission (until the quota is filled)
– Training School: Nov.26-Dec.1, 2018

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.

Costs and Fees, Financial Support

There is no registration fee, but participants should anticipate costs of travel, accommodation and meals. The organizers can help with finding accommodation close to the venue. The BigSkyEarth COST Action will provide grants for selected participants who are coming from institutions in: Austria, Belgium, Bulgaria, Bosnia and Herzegovina, Croatia, Czech Republic, Denmark, Estonia, Finland, France, fYR Macedonia, Germany, Greece, Hungary, Ireland, Israel, Italy, Lithuania, Malta, Netherlands, Poland, Portugal, Romania, Serbia, Slovakia, 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.

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. Organisers 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 <[email protected]>.

Challenges and general goals of the Training School

Challenges are similar in astronomy and Earth Observation, with signal processing, statistics, machine learning, and computer science as the common denominator. BigSkyEarth Training Schools aim 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.
Their 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. Their key aspect is addressing all these topics in synergy to set in a logical interdisciplinary framework building bridges between diverse areas.

Topics and general programme

The school will cover a wide range of topics:

  • Machine learning applied on topics such as finding bars in big simulations (e.g. Illustris)
  • Classical Machine Learning vs. Deep Learning
    • Representation Learning
    • Convolutional Networks
  • Supervised, Unsupervised, Reinforcement Learning, …
    • Classification
    • Regression
  • Astronomical Application Examples
    • Star/Galaxy Classification
    • Transient Classification
    • Transient Detection
    • De-blending
  • What is the network learning?
  • The principal components analysis and its application to spectra
  • Bayesian filtering and smoothing

Traveling to Tuorla

The Science Centre Tuorla is located on the south-western coast of Finland, 150 km west of Helsinki. It is located close to the city of Turku which one can reach by air, sea, road and rail.

  • For detailed instructions about the roads to the observatory (and how to reach it by car or bus form Turku), see HERE
  • By plane: take a flight to Turku Airport, and then take the bus number 1 to the marketplace and change the bus to 704 or 706 towards Piikkiö (see HERE for bus instructions)
  • More information on how to reach Turku – HERE

Trainers and Speakers


Victor Debattista is a computational astrophysicist working on the formation, evolution and dynamics of galaxies. He is particularly interested in the Milky Way with the recent second data release of the Gaia satellite. He is the UK Point of Contact for LSST:Bulge Science. He is the group leader of the Galaxy formation and dynamics group at the University of Central Lancashire where he has been since 2007. He has held posts in the University of Washington, ETH Zurich and the University of Basel. He earned his PhD at Rutgers University.

Nima Sedaghat is a junior research scientist in Deep Learning and Computer Vision at University of Freiburg, Germany. Due to his passion in astronomy, his recent research has been focused on applications of deep learning in astronomy. His recent projects in this cross-disciplinary area have been focused on real-time transient detection and galaxy de-blending. His current research interest is in Domain Adaptation, to facilitate the use of astronomical deep learning solutions in the real universe!

Antti Penttilä holds a position of a University researcher at the Department of Physics, University of Helsinki. He is an expert on numerical analysis and interpretation of light scattering in complex media.



Julien Tréguer graduated as an engineer from Telecom ParisTech school in Paris in 1995 and obtained an MBA from the Collège des Ingénieurs in Paris in 1996. After many years working in management consulting, asset management and investment banking for firms such as AT Kearney, Société Générale and BNP Paribas and a stint in entrepreneurship starting a financial software company, he earned a Master of Science in Astrophysics from Paris Observatory in Paris in 2012. He then enrolled in a PhD in the Planck collaboration in observational cosmology at the Astroparticle and Cosmology laboratory in Diderot university in Paris until 2016. He joined the Energy and Industrial Process / Data Intelligence department at Vicomtech in 2018 focusing mainly on image and video analysis.

Simo Särkkä is an Associate Professor with Aalto University, Technical Advisor of IndoorAtlas Ltd., and an Adjunct Professor with Tampere University of Technology and Lappeenranta University of Technology. In 2013 he was a Visiting Professor with the Department of Statistics of Oxford University and in 2011 he was a Visiting Scholar with the Department of Engineering at the University of Cambridge, UK. His research interests are in multi-sensor data processing systems with applications in location sensing, health technology, machine learning, inverse problems, and brain imaging. He has authored or coauthored ~100 peer-reviewed scientific articles and his book “Bayesian Filtering and Smoothing” along with its Chinese translation were recently published via the Cambridge University Press. His latest book “Applied Stochastic Differential Equations” is published via the Cambridge University Press in 2018. He is a Senior Member of IEEE, serving as an Associate Editor of IEEE Signal Processing Letters, and is a member of IEEE Machine Learning for Signal Processing Technical Committee.

Maria Gritsevich is a senior scientist, docent in Planetary Sciences at the Department of Physics, University of Helsinki (UH). Her primary scientific interests include meteors, light scattering experiments, and laboratory researches on extraterrestrial materials. Prior to coming to UH, she was appointed as a senior scientist at the Lomonosov Moscow State University, at the Ural Federal University and at the Russian Academy of Sciences, where she developed novel idea to interpret meteor observations using the analytical solution of the equations of Meteor Physics (this by-passes established then brute force modeling approach requiring number of artificially set assumptions). She has also worked as a research fellow at the European Space Agency (ESA/ESTEC), and as a Specialist Research Scientist at the Finnish Geospatial Research Institute.

Janne Sievinen has educational background in materials physics at the Department of Physics in University of Helsinki (1988 – 1995). He has had career at Varian Medical Systems Finland Oy (1995 – 2012) that develops software for treating cancer and other medical conditions with radiotherapy, proton therapy and brachytherapy, holding management positions and being also directly involved in the implementation and maintenance of photon dose calculation algorithms. He is now providing private consultation for medical devices industry. As an active member of the Fireball Working Group in the Finnish Astronomical Association Ursa, his primary interests include analyses of atmospheric trajectories of meteors, and the development of practical software tools to assist these activities. Currently he is focused on the development of MeTra Meteor Trajectory Analyzer that is a comprehensive software package intended for fast and intuitive reconstruction of the atmospheric trajectories of meteoroids based on photographic data.

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.

Local Organizing Committee

Science Centre Tuorla of the University of Turku
University of Helsinki, Planetary System Research Group

  • Pasi Nurmi (University of Turku)
  • Kirsi Lehto (University of Turku)
  • Maria Gritsevich (University of Helsinki)

What is BigSkyEarth?

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 visualisation
– 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 the Memorandum of Understanding.