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Why selecting and training AI models is a job for machines

The space of possible models, architectures and hyperparameters is large and challenging to explore.

This post is part of a series of the vision behind Modulos. They are written by the co-founders, Ce Zhang and Kevin Schawinski.

In the traditional data science process, a team of data scientists and machine learning engineers is required. They will spend days, weeks, or even months on iteratively choosing AI models, tuning them, and testing them. Often, this tedious process is guided by the team’s experience with models and techniques and is colored by their experience and education. 

As an effect, the experiments of such a team will always be constrained by what they know and what their tools enable, independent of the quality of their input data. This unsystematic process thus leads to biased models delivered to production.

What makes finding the best model and configuration however such a difficult task? The space which you need to search for the best model is enormous. First of all, the space of AI models is vast and the number of potential models is very large. For many models then, you have somewhere between zero and a very large number of possible architectures — infinite even. And finally, when you then train the model, you can additionally change somewhere between zero and a very large number of so-called hyperparameters. 

All this adds up to a very complex, high dimensional labyrinth. The number of possible models and configurations you could try rapidly approaches infinity. A space so large that it cannot be visualized, rendering it impossible to intuitively understand.

So how should we go about finding a good model? Human minds have created a way to systematically go exploring for good models in such a space: search algorithms. Even a search algorithm as basic as randomly guessing the next step performs already significantly better and faster at finding good solutions than a human would1.

This observation is central to how we at Modulos think about automated machine learning. To find the combination of model, architecture and hyperparameter that’s the best for our problem, we should conduct a systematic search for it. We designed Modulos AutoML from the ground up for this task. 

Once free from arduous tasks, which can be automated, data scientists and business experts can focus on the higher-level challenges of their domains. For these challenges, human intelligence is essential. Employing artificial intelligence smartly furthermore allows humans to maximize their impact.

Footnotes:1: J. Bergstra and Y. Bengio, “Random Search for Hyper-Parameter Optimization”, J. Mach. Learn. Res., vol. 13, pp. 281-305, 2012.

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Romain Lencou

Head of Engineering

Deleted code is debugged code. (Jeff Sickel)

Romain Lencou graduated from the Grenoble Institut National Polytechnique with M.Sc in Computer Science in 2008. Growing up in France in the 90’s, he developed an enthusiasm for pop culture, technology and food. Always eager for technological challenges, Romain worked for companies like VMware, Intel and Logitech, covering various topics including cryptography, virtualization and computer vision. Bitten by the machine learning bug, he is looking forward to apply his problem solving skills in Modulos.

Kevin Schawinski

CEO / Co-Founder

Running a startup is super relaxing, right?

While a Ph.D student, he co-founded the Galaxy Zoo citizen science project involving more than a million members of the public in scientific research because machines weren’t quite good enough yet to go map the cosmos and classify galaxies. He stayed in Oxford as the Henry Skynner Junior Research fellow at Balliol College before moving to Yale as a NASA Einstein Fellow. In 2012, he started the galaxy and black hole research group at ETH Zurich as an assistant professor and began a close collaboration with Ce Zhang from computer science to work on the space.ml project. He is now the CEO of Modulos.

Ce Zhang

Co-Founder

Random is best.

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

Operations Manager

“You miss 100% of the shots you don’t take.” – Wayne Gretzky

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

Chairman of the Advisory Board

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

Administration

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.

Bojan Karlaš

Software Engineer

Real engineers must be a little bit lazy.

After getting a bachelor’s degree in software engineering at the University of Belgrade, Serbia, Bojan spent 2 years working as a developer at Microsoft building distributed database solutions. He then went to Switzerland to pursue a computer science master’s degree at EPFL. He did his master thesis with Ce Zhang at ETH Zürich on the topic of time series forecasting, after which he joined Ce’s group as a PhD student. His industry experience also includes internships at Microsoft, Oracle and Logitech. His research interests revolve around systems and abstractions for making machine learning accessible to non-experts.

Nikolay Komarevskiy

Software Engineer

Software engineer in his prime

Passionate about nanophotonics and scientific research, he pursued his PhD degree in the Computational Optics group under the supervision of Prof. Christian Hafner at ETH Zurich. In addition to electromagnetics, Nikolay gained profound expertise in optimizations and in evolutionary optimizations in particular. Substantial part of his PhD work was conducted in collaboration with NASA Ames and was dedicated to the design and optimization of photonic reflectors. After a year of Postdoc, Nikolay moved to industry, where he joined an R&D team to employ his experience in electromagnetic/multiphysics simulations and stochastic optimizations. Fascinated by the recent advances in building smart software, Nikolay switched his gears to software engineering and eagerly faces new challenges.

Evangelia Mitsopoulou

Senior Frontend Engineer

Work? What is this? I only know the verb create.

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.

Florian Marty

Sales Manager

Putting Science into the Art of Sales.

As a Ph.D. in Molecular Biology from the University of Zurich, Florian Marty was, like most scientists, not a big fan of sales initially. But, over the years and with growing experience in different commercial roles, he learned that there is a lot of science in what makes good salespeople. Coupled with his open mindset to learn new things and a communicative personality, Florian is fascinated to explore and test new strategies, tactics, and expert moves in sales. As a Sales Manager, he will be joining the commercial team helping to grow the customer base and make Machine Learning accessible to everyone. Fun fact, as Florian has never written a single line of code in his life.

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.

Dominic Stark

Data Scientist

Code quality correlates with food quality.

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.

Michael Röthlisberger

Data Scientist

Data handling with structure

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.

Dennis Turp

Data Scientist

Dennis Turp is the first employee of Modulos.

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.

Andrei Văduva

Software Engineer

The trendsetter geek

He focused his attention on designing Architectures of Computer Systems. During university, he gained an excellent understanding of performance optimization and scalability on architectures such as distributed systems. Having a good experience in various Computer Science fields like big data analytics and Artificial Intelligence, he did his bachelor’s thesis designing a Machine Learning algorithm for social media platforms. After graduation, he joined the investment banking industry, in London, where he gained good experience in designing and building high-quality software. Andrei moved to Switzerland to explore new perspectives and found a great challenge in the startup world. Using his passion for technology and professional experience, he brings the best practices in software engineering to Modulos.

Anna Weigel

Chief Technology Officer

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.

Claudio Bruderer

Product Manager

Give me coffee to function.

After obtaining a BSc and a MSc degree in physics at ETH Zurich, Claudio decided to continue his studies of the Universe as a PhD student in Prof. Refregier’s Cosmology research group. He studied the gravitational lensing effect, whereby he measured the shapes of several billions of galaxy images (mostly synthetic ones). After acquiring his PhD, Claudio then joined the consulting company AWK Group AG and worked as a project manager and associate for IT and communications projects in the logistics and mobility sectors and for the federal government. Determined to create cutting-edge IT solutions, he decided to join Modulos as a product manager.

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Christoph Golombek

Sales Manager

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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