machine learning group of the lig
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Machine Learning group of the LIG
http://ama.liglab.fr/
The team at a glance
Ø 9 permanent members (6 HDR): 1 DR (CNRS), 2 PR, 4 MdC, 1 CR (CNRS), 1 DR-Emeritus,
Ø 3 Post-docs, 15 PhDs & 2 Research Engineers,
Ø 17 PhDs defended since the creation of the team in 2011.
Research axes
u Analysis and Mining of Complex Data, Machine Learning, Knowledge Representation, Information Retrieval, Social Networks
Project proposal: Representation Learning for Temporal / Spatio-Temporal Data
u Context- Human interactions conducted either via the web and mobile services, or with artifacts, moving objects, and intelligent sensors generate large flows of complex dynamic data.
- These user traces may have a space (e.g. geo-localization) and temporal components that are often composed of multiple types of information.
u Objectives- Build formal representation learning models and algorithmic tools that aimed at understanding, modeling and analyzing complex dynamic traces (spatio-temporal data)
- We focus on a set of generic machine learning tasks (Classification, forecasting and cokriging),
- Semantic information diffusion and urban computing will support the theoretical contributions and serve for evaluating the models and algorithms (provided by DEEZER).
u Supervisors: [email protected] & [email protected]
Project proposal: Text and Time Series Alignment on via Representation Learning on Heterogeneous Data
u Learning representation for heterogeneous data (video, text, image) using deep Neural Networks
u Overall aim is to build a recommendation system based on the learned representation
u Supervisors: [email protected], [email protected], [email protected]
Project proposal: Cobotics: cooperation between a human and a robot to solve a task
u The Master Thesis will focus on:o Design and develop a perception system to perceive the collaborative workspace
based on RGBD sensors
o Estimate the virtual allowed work space of the robot
o Estimate the virtual safe space around the person
o Estimate the separation distance between the 2 spaces
Safe space around the robot arm defined based on time to stop
Safe space around the person defined based on reach and max velocity
Allowed work space
Person far away from robotRobot allowed full access
Separationdistance
Supervisors: [email protected] & [email protected]
Project proposal: Learning non-technical skills from user feedbacks in a virtual environment
u For critical situations (like driving in bad situations) no-technical skills (depending on personal congitive nature) are generally decisive
u The aim of the project is to learn personnal no-technical skilleautomatically from user feedbacks
u Supervisors: [email protected], [email protected], [email protected]
Project proposal: Asynchronous distributed optimisation
u In very large scale learning, the situation where data cannot be stored in one single machine is common
u The aim of the project is to develop an asynchronous distributed framework that learns a predictive model by agregating the local updates made by optimization algorithms on different machines
u Supervisors: [email protected], [email protected], [email protected]
Project proposal: Learning models for credit risk prediction
u In the finance sector, different indicators are used to derterminethe risk of investiement for a bank
u The aim of the project is to develop machine learning tools that are able to take different sources of information for predicting the credit risk
u Supervisors: [email protected], [email protected], [email protected], [email protected]