About

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.

News

Archive of news

  • Post-doc position on generative models for inverse…

    30.6.2022

    We have an opening for a full-time post-doc position. The research target is to develop machine learning methods to solve inverse problems accounting for the inherent uncertainty of the inverse problem. We will formulate these problems as inference problems with prior scientific knowledge available in the form of simulators. We will follow the simulation-based inference ...

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  • PhD position on generative models for inverse prob…

    30.6.2022

    We have an opening for a PhD position. The research target is to develop machine learning methods to solve inverse problems accounting for the inherent uncertainty of the inverse problem. We will formulate these problems as inference problems with prior scientific knowledge available in the form of simulators. We will follow the simulation-based inference paradighm ...

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  • Post-doc position on generative models for discret…

    14.6.2022

    We have an opening for a full-time post-doc position on a research project on the development of 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. ...

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