About the DMML group
The Data Mining and Machine Learning group of Geneva was established in 2011 by Prof. Alexandros Kalousis. It operates as a collaboration between the Department of Information Systems of the University of Applied Sciences, Western Switzerland, Geneva, and the VIPER group of the Computer Science Department of the University of Geneva. Many of the members currently follow a PhD under the joint supervision of Profs. Alexandros Kalousis and Stephane Marchand-Maillet.
We conduct research in various areas of data mining and machine learning, publishing at major international conferences (NeurIPS, ICML, etc.). In our latest work we focus on leveraging the power of modern deep learning architectures to address the problems of generative modeling, continual- and meta- learning, modelling of dynamical systems, and imitation and reinforcement learning. In these we also build on our experiences with metric and kernel learning, feature selection, structured regularization, and many other topics explored over the years by our members.
Many of our research directions have been defined on the basis of real world problems. We collaborate with multiple industrial and academic partners on joint projects providing the necessary machine learning expertise. The group receives funding from different sources, such as the Swiss National Science Foundation, SNSF, InnoSuisse (former CTI), the European Union, Horizon 2020, as well as directly from industrial partners.
We have an opening for a PhD position. The research target is the development of deep generative models for discrete data structures such as graphs and in particular molecules. We seek to develop generative models capable of conditional generation as well as what can be considered the equivalent of style transfer for discrete structures. The successful ...Read more
We are very pleased to announce that the Hasler Foundation has approved the project proposal of Dr. Grigorios Anagnostopoulos, and will fund the project "LiBertaS". LiBertaS aims to liberate the access of Location Based Services (LBS) to the highly accurate fingerprinting methods in business practice, by greatly downscaling the data volume needed for their operation. ...Read more
Our latest paper: “Can I Trust This Location Estimate? Reproducibly Benchmarking the Methods of Dynamic Accuracy Estimation of Localization”. has been published in the Journal Sensors by MDPI. This work reviews the relevant literature related to the accuracy estimation of indoor positioning systems, presenting in a consistent terminology commonalities and differences of existing works and discussing baselines and evaluation metrics. ...Read more