We solve difficult problems with machine learning by connecting businesses to a talented developer community.

Tivadar Danka, PhD

Tivadar is a lead expert in machine learning. After finishing his PhD in mathematics in 2016, he started on a journey to democratize AI and find solutions to even the hardest problems. He also developed modAL, the leading active learning framework for Python.

Krisztián Koós, PhD

Krisztián is the core developer of our competition platform, responsible for the magic behind. During his PhD, he developed NucleAIzer, the first fully open and free deep learning based tool for nuclei segmentation in cells.

Dr. Anetta Leticia Vajda

As a seasoned professional, Anetta is bringing her extensive experience from the corporate scene as a lawyer and economist in finance. Her strong focus on business enables telesto.ai to strengthen its position in the AI and machine learning industry on a global scale.

Nándor Magyar

Nándor is a software generalist, with focus on frontend development and DevOps. He is in charge of building the Angular application for our competition platform, as well as managing the Kubernetes cluster.

Vitaly Kovalev

Vitaly is a machine learning engineer, with years of experience in developing data-intensive applications. With immense knowledge under his belt, he is responsible for turning proof of concept solutions into production ready methods.

Scientific advisors

Peter Horvath, PhD

Péter is a Finnish Distinguished Professor Fellow at FIMM Helsinki and the Director of Institute of Biochemistry at the Biological Research Centre in Szeged, Hungary. He is a distinguished scientist, with a drive to revolutionize machine learning with crowdsourcing.

Our goal is to

  1. Connect the amazing AI talent with the industry through exciting problems. We want to empower everyone to work in AI and enable every community to leverage state of the art algorithms.
  2. Create the ultimate competitive crowdsourcing AI platform. At telesto.ai, competitors are rewarded frequently and organizers are guaranteed to have an algorithm that is constantly improving.
  3. Aim for production, not proof of concept. Frequent faults during deployment? Surprising edge cases? Our competitions are dynamic: competitors are able to address these issues as soon as they surface.
  4. Support the AI community and open source software. We think that the current technology boom in AI is made available by amazing people such as researchers and open source software contributors. We would like to give back to them.