Open positions in the 2018 summer session
Deadline for applications: May 28th 2018
Data and Information Extraction from multiple data sources for transport Innovation and Sustainability (Mobility as a Service)
The research aims at exploiting the full potential of big data to describe mobility patterns, extract information from existing massive data sources, crossing it with context-specific understanding of human behaviour, in order to analyse the different ways people interact with one another (Onnela, 2011). The project will provide – through an innovative approach, less invasive than current travel surveys – a cloud based framework for collecting, analysing, and extracting urban mobility information from several massive data sources.
One of the characteristic of the so called Big Data is the occurrence of high dimensional data. The analysis of high dimensional data is affected by the so-called curse of dimensionality. As an example, one of the aspects of this phenomenon is the impossibility of sampling efficiently point from neighborhood of a data set.
There are several methods used to overcome this difficulty, many of them based on projecting data onto smaller dimensional subspaces.
This project aims to devise new approaches and technologies for the compression of data using deep learning models, and to show their potential to produce more accurate and visually pleasing reconstructions at much higher compression levels for both image and video data.
The objective of this Ph.D. project is to advance image/video compression by exploiting recent advances in machine learning, and particularly to develop deep learning techniques for next-generation video coding. The objective of this Ph.D. project is to advance image/video compression by exploiting recent advances in machine learning, and particularly to develop deep learning techniques for next-generation video coding.
Cognitive data analysis consists in the set of algorithms, applications and computing platforms to perform tasks that mimic human intelligence. Image tagging, video highlight creation, automatic subtitling are just some examples of how cognitive data analysis can facilitate and speed up complex tasks usually done by hand in the media domain. Deep Neural Networks (DNN) is a family of technologies implementing such kind of systems.
The objective of this PhD project is to develop advanced techniques for audio-visual media understanding applicable in media industry context.