Serokell Labs

Innovative solutions with
solid foundations
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The research
division of Serokell

Our work lies at the intersection of all the important fields of modern technology
Programming
language theory (PLT)
Distributed
systems
Mathematics
Artificial
Intelligence
Our approach combines innovations from multiple fields, making it easy to create bridges between disciplines.
With our experience in machine learning, blockchain, and programming language theory, we build novel solutions to real problems plaguing the industry.
Careful research, mathematical modeling, and interdisciplinary work are at the foundation of everything that Serokell Labs stands for.

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At Serokell Labs, our goal is to change industry through science

We create a feedback loop between academia and industry with three practices:
  • Creating a platform for promising scientists to do original research.
  • Validating state-of-the-art industry solutions within frameworks of thought already used in academia. (papers, theories, mathematical models)
  • Building new solutions that take in mind the research done before in the field.
Doing original
research
Building new
solutions
Validating
state-of-the-art

At Serokell Labs, our goal is to change industry through science

We create a feedback loop between academia and industry with three practices:
Doing original
research
Building new
solutions
Validating
state-of-the-art
  • Creating a platform for promising scientists to do original research.
  • Validating state-of-the-art industry solutions within frameworks of thought already used in academia. (papers, theories, mathematical models)
  • Building new solutions that take in mind the research done before in the field.

Path for innovators

We run our research laboratory together with ITMO, the top university for competitive programmers*
* according to HackerRank.
Collaboration between Serokell developers and scientists from the leading world universities enables us to translate the vision of pioneers in optical design, computer vision, and engineering into working solutions for real business problems.

Path for innovators

We run our research laboratory together with ITMO, the top university for competitive programmers*
* according to HackerRank.
Collaboration between Serokell developers and scientists from the leading world universities enables us to translate the vision of pioneers in optical design, computer vision, and engineering into working solutions for real business problems.

Our research

Applied research for GHC

Author: Vladislav Zavialov, Artem Kuznetsov

Applied research for GHC

Author: Vladislav Zavialov, Artem Kuznetsov
Field: PLT
We refactor and improve parts of GHC to move Haskell closer to practical dependent types, implementing features such as standalone kind signatures.

Research on non-classical logics

Author: Danya Rogozin

Research on non-classical logics

Author: Danya Rogozin
Field: Mathematical logic
We investigate topological and algebraic aspects of non-classical logics and structures that matter from a logical perspective.

Research on unidirectional coercibles

Author: Sasha Pakulev

Research on unidirectional coercibles

Author: Sasha Pakulev
Field: PLT
Coerce is a Haskell feature that allows for zero-cost conversion between generative type abstractions and their base type. We want to create the possibility for safe unidirectional coerce for newtypes with a guaranteed invariant.

ML Lab

We build applied machine learning solutions for business purposes while repeatedly innovating on mainstream ML tools.
Our fields of work include: applied discriminative and generative machine learning models, alternate implementations for ML algorithms with functional programming languages, coordinate-free linear algebra.
Recent projects from our portfolio:
Enhancing biology studies with ML
We are investigating the use of ML to artificially increase the resolution of commodity microscopes. For the same project, we are also creating an organelle classificator that uses the U-Net network architecture.
Exploring computer creativity
We are exploring music generation through both symbolic representation and raw audio. Some of the tools we use: Markov chains, hidden Markov models, deep learning with RNNs and Transformers.
Designing a Face Recognition System
We are building face recognition solutions with machine learning models that use Gaussian Processes for handling high-dimensional data.

Innovation is one click away

We would be happy to discuss your project and propose solutions.
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