A representative list of research projects of our group.

  • Rawfie: Road-, Air-, and Water- based Future Internet Experimentation

    RAWFIE (Road-, Air-, and Water- based Future Internet Experimentation) is a project funded by the European Commission (Horizon H2020 program) under the Future Internet Research Experimentation (FIRE+) initiative that aims at providing research facilities for Internet of Things (IoT) devices. The DMML team has been involved in the RAWFIE project since early 2015 and delivered their final contributions in June 2019 as the the project ended. The members of the team invested in the project, Jason and Lionel, were responsible for the design and development of the platform's data analysis tools, enabling experimenters to apply machine learning algorithms on data collected via the platform's unmanned devices. Read more
  • 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


We proudly present our partners, with whom we have collaborated in numerous research projects, offering an applied machine learning approach in solving real world problems. If you are interested in a collaboration, do not hesitate to contact our team.


The members of our group have been to various prestigious institutions for internships, during which they've sharpened their skills and expertise.

Magda Gregorova,  IBM Research, June - September 2015

Jason RamapuramApple AI research, June - September 2017, January - March 2019

Amina Mollaysa, Alan Touring Institute, August - September 2018, September - October 2019

Lionel Blonde, Apple AI research, June - September 2019

Jason RamapuramDeep Mind, December 2019 - March 2020