My research journey is described here, through some milestones along the way, older works are at the end.
Feel free to have a look at some of my publications here.
Have a nice journey !
Current research activities at LITIS Lab
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… (under construction) …
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Machine Learning for Graph Data
(more here…)
The Pre-image Problem in Machine Learning
Spectral Unmixing (Blind Source Separation) in Hyperspectral Image
This part of the website is under construction.
See references in the publications page, here.
Distributed Learning for Big Data
This part of the website is under construction.
See references in the publications page, here.
Online Detection with One-Class Classification
This part of the website is under construction.
See references in the publications page, here.
Collaborative Learning in (Wireless) Sensor Networks
We propose machine learning techniques for collaborative learning in wireless sensor networks, taking into account the limited resources in computation, memory usage, communication as well as power. We are interested in wireless sensor networks deployed in an environment to sense for modeling and monitoring given physical phenomena, such as gas, pollution, biochemical, temperature, humidity, acoustic field, motion, etc. It is worth noting that collaborative learning greatly enhances the accuracy of the estimation, and the detection performance.
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GPS-Free Indoor GeoLocalization in Wireless Sensor Networks
Sensors are often randomly deployed, such as for monitoring inhospitable habitats and disaster areas, or equipping mobile entities, such as humans (e.g., Alzheimer patients in an adult daycare center) or animals in wilderness or even vehicles (in vehicular ad hoc networks (VANETs)). Information captured by each sensor remains obsolete as long as it stays unaware of its location. Implementing a self-localization device, such as a global positioning system (GPS) receiver, at each sensor device may be too power-hungry for the desired application with battery-powered devices. Moreover, GPS or any global navigation satellite system (GNSS), are not functional in indoor environments. The inference of the position of location-unaware sensors from inter-sensor ranging is a hard and ill-posed problem. We propose many contributions, including zoning, localization and tracking, with and without radio-location fingerprints. Investigated methods include machine learning, Bayesian methods, polar intervals, pre-image problem and matrix regression.
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Online Dictionary Adaptation (Sparse Learning)
Online Learning and Nonlinear Adaptive Filtering
Relative Stationarity Testing
Time-Frequency Learning Machines