Search for the best AI solution to your data science challenge
What data do you want to base your AI solution on? Upload your training data and the system automatically analyses your dataset.
What quantity do you want the AI solution to predict? Specify your label and what information to ignore.
How do you want to find and measure the success of your solution? Select feature extractors, models, the optimizer and the objective or use the default settings.
Once you have specified your problem, the Modulos AutoML platform will find the best AI solution for you. The system provides insights which allow you to monitor the process.
You are now ready to use your AI solution! Download the best model and feature extractor pipeline with a simple click and make predictions on a new dataset.
Modulos auto-ml can deal with a wider range of data types than anyone else does.
Simply click go to start training models, or use your expertise to refine your search.
The Modulos Team is constantly adapting the latest AI models from research.
No longer spend months experimenting with different models and frameworks.
After acquiring Bachelor and Master degrees in Physics and gaining research experience in Australia, Anna completed her PhD in Astrophysics in Kevin Schawinski’s group at ETH. During her PhD she developed an innovative approach, took advantage of large data sets, and used advanced statistical methods to study the relationship between galaxies and their supermassive black holes. Her work is summarized in five first-author papers. After exploring the depths of our Universe, Anna is now excited to tackle challenges that we face in the business world by developing and exploiting cutting-edge machine learning methods. Anna is currently employed as Modulos’ Head of Data Science.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
“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.
Random is best.
He believes that by making data—along with the processing of data—easily accessible to non-CS users, 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).
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.
