Artificial Intelligence and Applied Machine Learning Group

The Applied Artificial Intelligence and Machine Learning Group of iCoSys in Fribourg Switzerland is active in domains such as :

  • Artificial Intelligence,
  • Machine learning,
  • Big data analysis,
  • Signal processing,
  • Algorithms

Machine Learning for enterprise solutions

Our main focus is to work with economic partners on applied machine learning projects. We have ongoing project realisations in different domains such as biomedical, video surveillance, electricity consumption analysis, financial time series, massive text processing, Industry 4.0, etc. Partnerships are typically done through research mandates, CTI funding or EU framework projects.

Our team includes seniors in AI and Machine Learning, PhD-level and master-level computer scientists to make the best out of your projects. Contact Jean Hennebert for more information.


At iCoSys Fribourg, we ave set up a microservice cluster to deploy machine learning models scheduled by Kubernetes, including shared GPU support for deep learning.
In this project, we are developing geometric deep learning methods to support pathologists in the diagnostic process. Specifically, we are looking at tumor buds and lymphocytes in colorectal cancer and their spatial relationship.
In the context of Facility 4.0, we will study the optimization of buildings energy consumption hand in hand with our partners. This will allow us to focus on real world anomalies in the vast amount of data at our disposal; thanks to our BBData infrastructure.
The European project BIOSMART aims to develop active and smart bio-based and compostable packages that make possible: light weighting, reduced food residues, shelf life monitoring, longer shelf life and easier consumer waste handling.
In an international industrial research project we apply the latest technologies in machine learning to do handwriting recognition in tax forms.
The AINews project aims to provide intelligent conversational agent technologies that support the digital transformation of the press. These technologies will help make the information channel dynamic through dialogues, retain readers through content customization and optimize editorial directions through maps of interests on common topics.
ADVANCe is an Innosuisse funded project that focuses on the analysis of emotions and their congruence in video feeds. We partner in this project with IDIAP (Martigny) and CM Profiling (Bern).
BBDATA stands for Big Building Data and aims at developing a scalable cloud platform and tools for storing and processing smart building data. The services are targeting data access, processing and analysis, using open, robust, standardized and secured big data technologies.
DLL is a machine learning framework that aims to provide a C++ implementation of neural networks and convolutional neural networks. The implementation provides significant speedups on cpu in comparison to other frameworks such as TensorFlow or Torch. It is then dedicated to the use phase of such deep models where performance on cpu is requested, such as in industry 4.0 applications.
Optisoil is a project that leverages machine learning to speed up numerical simulations in the domain of civil engineering.
ETL is a header only library for C++ that provides vector and matrix classes with support for Expression Templates to perform very efficient operations on them.
DAPLAB aims at facilitating the access for enterprises and universities to emerging technologies in the area of big data and intelligent data analysis by managing an infrastructure composed of cluster of servers dedicated to storage and computation, to which universities and local companies will have access.
PhD Thesis by Christophe Gisler: time series represent a large part of the data supply worldwide and many data mining tasks, such as prediction and classification, are concerned with them. This thesis focused on the analysis and development of generic machine learning approaches to multivariate time series classification.
Tactical Ad-hoc networK Emulation aims to conceive, develop and implement algorithms to deploy reliable tactical applications over ad-hoc networks for Armasuisse.
SENSIMED AG has developed Triggerfish, a wearable non-invasive solution to monitor continuously intra-ocular pressure (IOP). The purpose of the Sensimed Diagnosis project is to analyze, design and implement signal processing, feature extraction and pattern recognition tools to exploit the signals monitored by the Triggerfish.
PhD thesis of Antonio Ridi: In this thesis we describe our work on the use of generative modeling for the classi cation of time series in the context of in-home monitoring. More precisely, we use generative approaches as Gaussian Mixture models (GMMs) and Hidden Markov models (HMMs).
VideoProtector is an innovative solution to manage cameras streams in a central place. This centralization offers also the possibility to run incident detection algorithms in a Software-as-a-Service (SaaS) mode.
PhD Thesis of Gérôme Bovet - The thesis is about the conception and implementation of a framework to develop dedicated smart buildings applications. By relying on Web technologies, we demonstrate the development of seamless services while reusing the available resources within the network. We also demonstrated in this thesis the exposure of machine learning services made accessible through Web interfaces and hiding the complexity of the process.
GreenMod aims at providing a framework easing the development of building management systems on top of Web technologies.
The idea of the project is to leverage on large sets of linguistic data and on machine learning algorithms able to discover automatically pronunciation rules. Such rules are then used in “symbolic mappers” able to transform a grapheme representation into a phonetic representation and to propose probable alterations of the pronunciation. The mappers are then used to propose clients with new domain names that sound similar to their original search.

All projects here.