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How AutoML enables astrophysics research in Chile

The Institute for Astrophysics at PUC in Chile is using Modulos AutoML to support their research. Chilean scientists are at the forefront of astrophysics research as the mountains in the country are amongst the best sites for telescopes. Students and postdoctoral researchers at the institute have access to Modulos AutoML running on the institute’s supercomputer, Geryon. Kevin Schawinski spoke with Professor Ezequiel Treister about how Automated Machine Learning is enabling cutting edge astronomy research and training students in using machine learning.

Prof. Treister speaking at the American Astronomical Society (credit: E. Treister)
Prof. Treister speaking at the American Astronomical Society (credit: E. Treister)

What kinds of questions are you pursuing the answers to in your astrophysicist studies?

Ezequiel Treister: There are many problems in astrophysics that are based on a combination of big data and proper machine learning. The overwhelming size and the dimensionality of some problems cannot be analyzed by the human brain; it’s impossible. As a result, it’s very natural to try to combine machine learning techniques and astrophysical data to overcome this deficiency.

Machine learning can be applied to remedy a whole range of problems, if not every problem. Specifically in our team’s case, we are focusing on galaxy morphologies, morphological classifications, and in particular a program that, well yourself started a long time ago, is the identification of major galaxy mergers.

NGC 6240 is a merger between two massive galaxies. Prof. Treister’s team studies the two supermassive black holes which are about to merge (Credit NRAO).

Machine learning is, of course, entering all sorts of research areas. What do you think are the barriers to entry for scientists seeking to use machine learning? Why is it difficult to adopt such technologies?

Ezequiel Treister:  It is mostly to do with training. Basically, those belonging to the older generations – like myself – were not trained to use these techniques. Most of us do not have the skills to easily learn or start practicing the techniques. Also, more fundamentally, we don’t have the necessary skills to train those belonging to newer generations either.

It may be easier for the new generation of students and postdocs to start learning and using these techniques, but there is still a learning gap they need to cross which is difficult to overcome. Basically there’s a whole language that needs to be learned; there’s knowledge to acquire on algorithms, technicalities, and hard programming skills and languages need to be learned. Whilst these skills can be learned, it certainly takes time and not many people are willing to pay that price.

That’s a beautiful segue to my next questions because at Modulos we are working on Automated Machine Learning:

·  What are the benefits of applying Automated Machine Learning to science projects like the project your team is working on?

·  Is it easier to get started on the project by simply using the applications without dealing with the technology on a fundamental level?

Ezequiel Treister: The main benefit of using Modulos, and it makes me very excited about using it more widely, is that it allows us to naturally solve the problems associated with the kind of projects we have in mind. You tend to find these problems in early projects; so typically with undergraduate but even graduate students too.

You tell them about machine learning and the scientific projects we want them to pursue, and immediately they start to spend a lot of time (days, weeks, and even months) focused on the implementation problems. For example, they have problems with libraries; the code doesn’t do what they want; they cannot code what they need to; making use of extensive feedback. So at the beginning you don’t trust the results which means a lot of testing is needed.

This soon adds up to a significant time requirement. The students become frustrated too because they are interested in doing an astrophysical project, but instead they are worrying about a library that does not have something installed or is not doing the right thing.

To give you an example, Modulos allowed us to cross that bridge very easily because you can isolate the programming, the implementation of the code from the scientific problem, and it still allows the students to understand what they’re doing at a conceptual level. They can jump directly into the scientific part and in terms of motivation this is very important. In many ways it makes it much more straightforward for a student as they can directly jump into the scientific problem without having to deal with the implementation aspect.

The Geryon cluster at PUC (credit: E. Treister)

Is it important for astrophysicist and science students that you are training to gain machine learning skills? Is it important for them to have these skills if they want to start their own companies, or want to work in Chilean companies, for example?

Ezequiel Treister: Absolutely. One of the things that is keeping me very excited is that this summer we are going to offer, so in a couple of months, to undergraduate students in our university, who come from astronomy but also from engineering and other schools, to do research in-person with us. It will be for a relatively short period of time; so you can imagine if they have typically a month to do a scientific project and they need to start machine learning from scratch, they will most likely not achieve anything.

However, using Modulos allows them to directly jump into the problem; to actually start working on the problem; to see the results; to analyze the results in a very short time span. That is fundamental because what they learned, and in our case it will obviously be about astrophysical problem, can basically be translated to everything else they do. For example, if they come from an engineering background, they can still apply in their satellite applications. There is of course basic research involved as well, but in different areas. The idea is that these particular internships become a great experience that they can replicate in the future.

You can find Ezequiel Treister on Twitter. He also regularly writes pieces for El Mercurio, Chile’s newspaper of record.

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Evangelia Mitsopoulou

Senior Frontend Engineer

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She is g(r)eek frontend advocate. Evangelia holds a M.Sc on ICT (2008) from Aristotle University of Thesslaoniki and a B.Sc on Applied Computer Science (2006) from Univesity of Macedonia in Thessaloniki, Greece. She has worked as a semantic web researcher on EC-funded projects while living in London. The last 8 years she loves mastering the frontend world.

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Ce Zhang


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He believes that by making data—along with the processing of data—easily accessible to non-computer scientists, we have the potential to make the world a better place. His current research focuses on building data systems to support machine learning and help facilitate other sciences. Before joining ETH, Ce was advised by Christopher Ré. He finished his PhD round-tripping between the University of Wisconsin-Madison and Stanford University, and spent another year as a postdoctoral researcher at Stanford. His PhD work produced DeepDive, a trained data system for automatic knowledge-base construction. He participated in the research efforts that won the SIGMOD Best Paper Award (2014) and SIGMOD Research Highlight Award (2015), and was featured in special issues including the Science magazine (2017), the Communications of the ACM (2017), “Best of VLDB” (2015), and the Nature magazine (2015).

Alexandra Arvaniti

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During the last twenty years, she worked in different roles, setting up and running PMOs, supporting the Executive Management Team or as Operations Manager for the DACH region. She loves all organizational challenges, which she can use well at Modulos, like set up and establish administrative business processes.

Rudolf Bär

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After initially working for Dow Corning International in Zurich and Brussels (1964 to 1969), he held various management functions in the Private Banking Group Julius Baer, Zurich, lastly as CEO from 1993 to 2000 and retired from its Board of Directors in 2005. Since 2014 he has been studying at the Institute for Particle Physics and Astrophysics at the ETH, Zurich.

Marianne Chiesi


Marianne has worked in administration of various companies and the ETH.

Marianne Chiesi worked in the administration of various companies before taking time off to raise her children. She translated text books and literary works into Braille and joined the ETH Zurich as an administrative assistant. At ETH, she worked with professorships and researchers in many areas, including astrophysicists, particle physicists and biochemists. She now runs the administration at Modulos.

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Dominic Stark studied physics at ETH Zürich. The transition of his career path to Data Science began when he was analyzing UV images of galaxies. Together with Kevin Schawinski an Ce Zhang, he worked on applying the latest advances of deep learning research to his problem. It turned out that the method itself was at least as interesting as the problem they designed it for. After publishing the results, his research project was about using Reinforcement Learning to develop novel ideas for data acquisition in astronomy. As a Data Scientist at Modulos, he keeps on solving problems, that require new ideas and technologies.

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Michael Röthlisberger

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He started to take an interest in Data Science and Software Development during his master’s degree. For his master thesis he worked on the image reconstruction software for a new PET detector. Michael gained some first experience in an internship for Sensirion AG. There he was part of the R&D team, which was developing a new gas sensor. The participation of a machine learning hackathon was sparking the interest of Michael in ML and he decided to pursue a career in this field. He is now exited to face new challenges with modulos and experience working in a rising start-up.

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Prior to his work at Modulos he studied physics at ETH Zurich. During his Master studies he worked together with Kevin Schawinski and Ce Zhang on exploring machine learning related topics in astronomy. In these one and a half years they published three scientific papers together. Dennis Turp is currently employed as a Data Scientist. His main expertise lies in the fields of generative modeling and anomaly detection.

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After acquiring Bachelor and Master degrees in Physics, Anna completed her PhD in Astrophysics in Kevin Schawinski’s group at ETH. Her work on the relationship between supermassive black holes and their host galaxies is summarized in five first-author papers. After exploring the depths of our Universe, Anna joined Modulos as the Head of Data Science. She has since been appointed the role of CTO and is now leading the overall technology development.

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Christoph Golombek

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Happy customers, happy Christoph – or is it the other way around?

After finishing his master studies in Energy Technology at RWTH in Germany, Christoph started his professional career as an expert and Sales Support Engineer for wind turbines in cold climates in Canada. There he started seeing the benefits of machine help in tackling data-driven challenges. Having explored the great North, his passion for cutting edge technology drove him into the machine vision sector in Switzerland, where he has worked as a fusion of Sales Engineer and Tech Support, while also acting as a Team Leader of a team of four. At Modulos, he can now focus again on bringing state-of-the-art technology to happy customers.

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We believe he is the perfect fit to bring across the Modulos value proposition to our customers. Do not hesitate to reach out to Florian to engage in a discussion about Modulos AutoML.