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Kalousis Alexandros

Professeur HES ordinaire

Hauptkompetenzen

Professeur HES ordinaire

Büro: F 1.24

Haute école de gestion de Genève
Campus Battelle, Rue de la Tambourine 17, 1227 Carouge, CH

Bereich
Economie et services

Hauptstudiengang
Informatique de gestion

BSc HES-SO en Informatique de gestion - Haute école de gestion de Genève

Data Mining

BSc HES-SO en Economie d'entreprise - Haute école de gestion de Genève

Machine Learning
Deep Learning

Laufend

SimGait, Modeling pathological gait resulting from motor impairments: compare and combine neuromechanical simulation and machine learning approaches

Rolle: Mitgesuchsteller/in

Requérant(e)s: Armand Stéphane, Laboratoire de Cinésiologie Willy Taillard Hôpitaux Universitaires de Genève

Financement: SNSF

Description du projet :

The aim ofSimGait is to create a musculoskeletal model of the human with neural control to be able to model healthy and impaired gait, for example due to cerebral palsy. The model consista of a dynamics model that models the motion of the legs and trunk, and is operated by muscle forces. Machine learning methods will be used to predict a patient’s gait from their clinical data using a data-driven model as well as to learn controllers that will imitate the gait of individual patients. The overarching goal is to create models that will allow medical doctors to explore the effect that different treatments will have on the gait of any given patient.

Forschungsteam innerhalb von HES-SO: Kalousis Alexandros

Partenaires académiques: Ijspeert Auke Jan, Laboratoire de biorobotique EPFL - STI - IBI - BIOROB, Lausanne; Armand Stéphane, Laboratoire de Cinésiologie Willy Taillard Hôpitaux Universitaires de Genève

Durée du projet: 01.09.2018 - 31.08.2022

Montant global du projet: 2'115'000 CHF

Statut: Laufend

Abgeschlossen

Road-, Air- and Water-based Future Internet Experimentation H2020-ICT-2014-1

AGP

Rolle: Hauptgesuchsteller/in

Financement: Commission européenne

Description du projet : The purpose of the RAWFIE initiative is to create a federation of different network testbeds that will work together to make their resources available under a common framework. Specifically, it aims at delivering a unique, mixed experimentation environment across the space and technology dimensions. RAWFIE will integrate numerous testbeds for experimenting in vehicular (road), aerial and maritime environments. A Vehicular Testbed (VT) will deal with Unmanned Ground Vehicles (UGVs) while an Aerial Testbed (AT) and a Maritime Testbed (MT) will deal with Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) respectively. The RAWFIE consortium includes all the possible actors of this highly challenging experimentation domain, from technology creators to integrators and facility owners. The basic idea behind the RAWFIE effort is the automated, remote operation of a large number of robotic devices (UGVs, UAVs, USVs) for the purpose of assessing the performance of different technologies in the networking, sensing and mobile/autonomic application domains. RAWFIE will feature a significant number of UxV nodes for exposing to the experimenter a vast test infrastructure. All these items will be managed by a central controlling entity which will be programmed per case and fully overview/drive the operation of the respective mechanisms (e.g., auto-pilots, remote controlled ground vehicles). Internet connectivity will be extended to the mobile units to enable the remote programming (over-the-air), control and data collection. Support software for experiment management, data collection and post-analysis will be virtualized to enable experimentation from everywhere in the world. The vision of Experimentation-as-a-Service (EaaS) will be promoted through RAWFIE. The IoT paradigm will be fully adopted and further refined for support of highly dynamic node architectures.

Forschungsteam innerhalb von HES-SO: Kalousis Alexandros , Blonde Lionel , Ramapuram Jason Emmanuel

Partenaires académiques: 539,Informatique de gestion; Kalousis Alexandros, 539,Informatique de gestion

Durée du projet: 01.01.2015 - 31.03.2019

Montant global du projet: 623'271 CHF

Statut: Abgeschlossen

Learning olfactory models to support the parfum creation process 19291.1 PFES-ES

AGP

Rolle: Hauptgesuchsteller/in

Financement: CTI; Firmenich

Description du projet : We aim at developing rational solutions for improving product performance and differentiation through data-driven and computational approaches. Statistical learning algorithms embeding side information are studied with the objective to design reliable models assessing product properties and qualities.

Forschungsteam innerhalb von HES-SO: Kalousis Alexandros , Strasser Pablo , Aminanmu Maolaaisha , Lavda Frantzeska

Partenaires académiques: 539,Informatique de gestion

Durée du projet: 01.01.2017 - 28.02.2019

Statut: Abgeschlossen

Machine learning tools exploiting hidden structures for forecasting multivariate time series (Hidden Structures in Time Series)

AGP

Rolle: Hauptgesuchsteller/in

Financement: HES-SO Rectorat

Description du projet : We will develop new methods and tools for forecasting multivariate time series that exploit hidden structures in the data to improve the accuracy of the forecasts. These shall be able to cope with time series from various application areas, such as financial and economic, transport, or electricity supply and demand, where there are large numbers of indicators developing in parallel along non-trivial and possibly unstable structures in their relationships.

Forschungsteam innerhalb von HES-SO: Kalousis Alexandros , Gregorova Magda

Partenaires académiques: 539,Informatique de gestion

Durée du projet: 01.11.2016 - 01.05.2018

Montant global du projet: 158'030 CHF

Statut: Abgeschlossen

Novel forecasting tools for very large scale time series systems

AGP

Rolle: Hauptgesuchsteller/in

Financement: HES-SO Rectorat

Description du projet : We will use a specific application area of air trafic forecasting to direct our research in learning multivariate time series models regularized by data-driven yet meaningful constraints. The objective is to develop algorithms that reduce dimensionality and improve the predictive performance of the models by exploiting additional knowledge from domain-theory, the specific spatial structure of the data (low network), or learned in a multi-task setting.

Forschungsteam innerhalb von HES-SO: Kalousis Alexandros , Gregorova Magda

Partenaires académiques: 539,Informatique de gestion; Kalousis Alexandros, 539,Informatique de gestion

Durée du projet: 01.11.2013 - 30.04.2015

Montant global du projet: 157'000 CHF

Statut: Abgeschlossen

Combining metric and kernel learning

AGP

Rolle: Hauptgesuchsteller/in

Financement: HES-SO Rectorat; 539,Informatique de gestion; FNS - Fonds national suisse

Description du projet : Kernel and metric learning have become very active research fields in machine learning over the last years. Although they have developed as distinct research fields they share common elements. One of the most popular approaches to kernel learning is learning a linear combination of a set of kernels, usually identified as Multiple Kernel Learning (MKL), this essentially corresponds to learning a block diagonal transformation of the concatenation feature space induced by the concatenation of the feature spaces that correspond to the basis kernels. On the metric learning side, probably the most prominent approach is learning a Mahalanobis distance in some feature space which in fact corresponds to learning a linear transformation in that feature space. So both methods learn linear feature transformations, where in the case of the MKL the learned transformation has a specific structure. Many of the metric learning methods are kernelized which raises the issue of which kernel should one use for a given problem, nevertheless there is no work so far that tries to combine metric learning with MKL. On the other hand since MKL is learning a special form of linear transformation over the concatenation feature space one could use metric learning techniques in order to learn such linear transformations or more general forms of them. In fact one can use metric learning techniques to learn linear transformations over the feature space induced by any kernel. On the same time, and despite the increasing popularity of metric learning methods, there exist so far no such method that will scale well with increasing problem sizes, i.e. large feature space dimensionality and large number of instances, and on the same time retain a good generalization performance. In the present proposal we want to take a step to address the issues briefly described above. More precisely the work described in the present proposal is divided into two workpackages. In the first workpackage we link metric learning and kernel learning methods, by using tools from one domain in the other and vice versa. In the second workpackage we will propose metric learning methods that can scale well with large datasets. The work of the first workpackage is divided into two main tasks. In the first task we will combine metric learning with MKL, i.e. learning metrics over kernels learned by MKL. We will explore different metric parametrizations which will lead to different learning problems. In the second task we will go on the opposite direction and use metric learning ideas for kernel learning. More precisely we will learn linear transformations of the feature space induced by some kernel, whether this is a kernel that is learned or it is a standard single kernel. By learning a linear transformation of the feature space we are in 31.03.2011 20:30:52 Page - 6 - fact learning in the general case a new non-linear, quadratic, kernel. We will experiment with different objective functions in order to learn the linear transformations. In the second workpackage we will also have two main tasks. In the first we will explore the use of stochastic gradient descent methods in order to improve the scalability of metric learning. In the second task we will go to the extreme case of metric learning and we will learn a linear transformation of rank one in order to make metric learning possible for very large datasets. At first we might think that learning a rank one metric might be too restrictive limiting its application only to simple learning problems. Nevertheless, by kernelizing it, we can apply it on learning problems of any complexity.

Forschungsteam innerhalb von HES-SO: Kalousis Alexandros

Durée du projet: 01.10.2011 - 31.08.2013

Montant global du projet: 56'820 CHF

Statut: Abgeschlossen

Errungenschaften

PEOPLE@HES-SO
Verzeichnis der Mitarbeitenden und Kompetenzen

Kalousis Alexandros

Professeur HES ordinaire

Hauptkompetenzen

Machine Learning

Data mining

Artificial Intelligence (AI)

Biomedical applications

predictive maintenance

Professeur HES ordinaire

PEOPLE@HES-SO Verzeichnis der Mitarbeitenden und Kompetenzen

Kalousis Alexandros

Professeur HES ordinaire

Hauptkompetenzen

Machine Learning

Data mining

Artificial Intelligence (AI)

Biomedical applications

predictive maintenance

Professeur HES ordinaire

PEOPLE@HES-SO
Verzeichnis der Mitarbeitenden und Kompetenzen