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We are continuously recruiting Postdoctoral researchers, PhD candidates, and MSc students in Machine Learning, Computer Science, Electrical Engineering, and related fields.
Interested candidates are welcome to contact me at any time throughout the year.
The list of announcements below may be incomplete.
Research topic
Change-point detection (CPD) is a fundamental problem in statistics and machine learning, focused on identifying abrupt shifts in the properties of data over time. These shifts, known as change-points, indicate transitions in the underlying distribution or dynamics of a system, which may result from external events or internal structural changes. The objective of CPD is to pinpoint when these changes occur and, in some cases, to understand the nature of the shifts. CPD has a wide range of applications across domains that require online insights and adaptive decision-making, such as medical monitoring, real-time trading, and network security. A growing number of these applications are generating structured, high-dimensional data with non-trivial and intricate geometric properties. These data often display complex relationships and dependencies that go beyond Euclidean spaces, necessitating sophisticated techniques for analysis and interpretation. Prominent examples include time sequences on groups and manifolds, time sequences of graphs, graph signals, etc.
A major challenge with dynamic structured data is finding representations that can effectively handle their underlying geometry, which is often defined by application-specific pseudo-distances. A common approach is to embed such data into conventional geometric spaces, like Euclidean spaces, even when the data may be more naturally represented in non Euclidean domains. This mismatch in representation complicates both learning and inference processes. Another challenge is that dynamic structured data are generated by a variety of sensors and infrastructures that continuously produce, disseminate, and store information. However, this data deluge already surpasses our capacity for analysis and decision making, necessitating online actions rather than offline processing due to its time-sensitive nature. The monitoring of telecommunications and energy production and distribution networks are characteristic examples of such time-critical applications.
The project aims to propose unsupervised online CPD algorithms for dynamic structured data, with a particular focus on time sequences on groups and manifolds. Non-parametric frameworks will be specifically considered, as they make fewer, if any, assumptions about the data’s underlying distribution and are better suited for detecting a wider variety of changes. The CPD algorithms will be designed to be computationally efficient to ensure scalability. Finally, an open-world recognition setting will be developed to classify changes on-the-fly into either previously seen classes or unknown classes. Applications to smart cities monitoring are considered.
Required Qualifications and Skills
A master’s degree in a field related to machine learning, such as computer science, electrical engineering, mathematics, statistics, or physical science.
Strong background in applied mathematics and statistics. Additional qualifications in optimization and numerical methods would be appreciated.
Strong programming skills in languages such as Python, C++ or MATLAB
Localisation
Campus Valrose, Université Côte d'Azur, Nice (downtown), France
Contact
Cédric Richard (cedric.richard@univ-cotedazur.fr)
Abstract
With rapid urbanization and growing environmental challenges, the demand for real-time monitoring solutions has surged. This thesis explores Distributed Acoustic Sensing (DAS) as an innovative approach to environmental and traffic monitoring. DAS technology transforms existing telecommunication optical fiber networks into dense acoustic sensor arrays, enabling the detection and analysis of seismic and acoustic events. This cost-effective and scalable alternative to conventional sensor-based systems offers new possibilities for smart city infrastructure. The research investigates advanced signal processing and artificial intelligence (AI) techniques to enhance real-time data analysis and interpretation.
Scientific Context and Methodology
Urban monitoring systems face multiple challenges, including data acquisition limitations, real-time processing constraints, and the integration of heterogeneous data sources. DAS produces vast, complex datasets that require robust and scalable signal processing and AI methodologies to extract meaningful insights. The identification of change points in traffic patterns, environmental disturbances, and infrastructure degradation necessitates specialized techniques capable of handling high-dimensional, non-stationary data streams.
This research develops a DAS-based monitoring framework that addresses key challenges in urban sensing.
First, it focuses on Change-Point Detection (CPD) by developing unsupervised machine learning algorithms capable of detecting and segmenting seismo-acoustic events in real time.
Second, it explores Scene Recognition, leveraging deep learning and domain adaptation techniques to classify different acoustic environments. This allows for distinguishing between various scenarios, such as traffic congestion, infrastructure anomalies, and natural events.
Finally, the research includes Experimental Validation, conducting real-world DAS experiments to assess the performance and effectiveness of the proposed algorithms.
These contributions aim to enhance real-time monitoring capabilities, improve event classification accuracy, and demonstrate the practical feasibility of DAS technology for smart city applications.
Required Qualifications and Skills
A master’s degree in a field related to machine learning, such as computer science, electrical engineering, mathematics, statistics, or physical science.
Strong background in applied mathematics and statistics. Additional qualifications in optimization and numerical methods would be appreciated.
Strong programming skills in languages such as Python, C++ or MATLAB
Localisation
Campus Valrose, Université Côte d'Azur, Nice (downtown), France
Contact
Cédric Richard (cedric.richard@univ-cotedazur.fr)
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