Open positions in the 2019 summer session
Machine Learning in network management for improving QoE in web applications
Michela Meo (DET)
Luca Vassio (DET)
Paolo Garza (DAUIN)
The candidate should tackle the problem by collecting, storing and processing large amount of data using a big data framework. The candidate will leverage these solutions, designing and engineering novel machine learning techniques to tackle the fine-grained traffic classification problem.
The candidate will design a complete solution, from data collection, feature engineering, feature selection, model selection, model training and testing. Next, the candidate should revisit and propose new QoS mechanisms to address the specific needs of the applications and the network.
Inference and control of dynamic processes on large-scale networks: from data to models
Luca Dall’Asta (DISAT)
Gianluca Como (DISMA)
The main objective of this thesis is the development and improvement of approximate inference methods for dynamical processes on network. The PhD student will become familiar with the most advanced techniques in the field, both from statistical mechanics and machine learning community, and will learn how to deal with large datasets and perform data analysis.
Such cutting-edge theoretical notions and computational techniques will be possibly very useful for his/her future career, both in academic research and in the industrial applications of the ICT sector.
Machine-Learning Based Optimization of Navigation Algorithms and embedded implementation techniques for Service Robotics
Marcello Chiaberge (DET)
Fabrizio Lamberdi (DAUIN)
The main objective of the PhD activities will be the development of algorithms, technologies and systems that can effectively jointly optimize the performance of the considered systems in terms of traditional communication metrics (e.g., bandwidth, latency) and application-layer metrics (e.g., utility in terms of lowest cost navigation paths) which will be defined on a case-by-case basis depending on the application, while at the same time minimizing all costs (e.g., computational complexity, storage cost, number of required devices and their economic cost).
Such objectives will be achieved by using both theoretical and practical approaches. In fact, the adopted methodologies will include the development, wherever possible, of theoretical frameworks to model the whole system, in order to investigate the problem from an analytical point of view. The proposed techniques will build upon recent advances in artificial intelligence, exploiting machine learning to determine optimal planning strategies in the presence of multiple competing constraints and under the presence of uncertainty. Artificial intelligence techniques will be employed both for sensing the environment and encoding semantic (e.g., using supervised learning strategies), and for planning the optimal trajectory (e.g. through reinforcement learning). The research project will leverage resources, data and know-how acquired by the Pic4Ser center for training the intelligent components, as well as define simulation environments in which the artificial intelligence system can be safely and effectively trained.
The resulting insights will then be validated in practical cases by analyzing the performance of the system with simulations and real-world experiments. Both proponent research groups have extensive expertise in such fields.
Silvia Chiusano (DIST)
Danilo Giordano (DAUIN)
The PhD student will work on the study, design and development of proper data models and novel solutions and for the acquisition, integration, storage, management and analysis of big volumes of heterogeneous urban data.
The research activity involves multidisciplinary knowledge and skills including database, machine learning techniques, and advanced programming. Different case studies in urban scenarios such as citizen-centric contexts, urban mobility, and healthy city will be considered to conduct the research activity.
The objectives of the research activity consist in identifying the peculiar characteristics and challenges of each considered application domain and devise novel solutions for the management and analysis of urban data for each domain. More urban scenarios will be considered with the aim of exploring the different facets of urban data and evaluating how the proposed solutions perform on different data collections.