Current Projects

  • MEDInA: sMart EDge fabric for Iot Applications

    MEDInA is an Innosuisse project, enabling the creation of low cost IoT self-adaptive Machine Learning based applications by developing an Artificial-Intelligence-as-a-Service (AIaaS) framework. Current work by the DMML team includes partnering with SixSq in order to develop an AI solution that runs on edge devices such as a Raspberry Pi 4, providing traffic volume information monitoring, to be used for adaptive smart lighting in Smart Cities. Read more
  • SimGait: Modeling pathological gait resulting from motor impairments

    Within this project we seek to develop machine learning methods for the modelling of pathological human locomotion. This is a collaborative Sinergia project funded by the Swiss National Science Foundation. Read more
  • OrbiLoc: Learning to position IoT devices in outdoor environments

    OrbiLoc is an Innosuisse project in which the Data Mining and Machine Learning (DMML) group collaborates with the company Orbiwise. The goal of this project is to improve the localization accuracy achieved by IoT devices utilizing the LoRa network of Orbiwise. In the context of this project, DMML introduces machine learning techniques for localization, achieving significant performance improvements, having as a baseline the trilateration method currently used by Orbiwise. The percentage of improvement of the localization accuracy achieved so far lays in the range of 30%-55%, depending on the environment of the deployment. Read more
  • SMELL: Learning olfactory models to support the perfume creation process

    SMELL  is an Innosuisse project undertaken with collabration with the company Firmenich.  The goal of  the project is to develop a data-driven methodology which will allow us to uncover the olfactory perception mechanisms related to perfume creation and exploit them to build rational solutions that improve product performance and differentiation. We designed  and developed novel machine learning algorithms that exploit side information to reliably predic olfaction of the product  and exploit the similarities of ingredients with respect to olfaction. Read more