PhD Thesis

Recent developments in data science and IoT provide new opportunities in the field of data acquisition, processing, visualization, and analysis from buildings. The goal of the DiagnoBat project is to create a computing platform dedicated to building diagnostics, integrating new data capture and visualization solutions, along with the latest AI advances for signal analysis.
In this project, we are developing geometric deep learning methods to support pathologists in the diagnostic process. Specifically, we are looking at tumor buds and lymphocytes in colorectal cancer and their spatial relationship.
SwissTranslation aims to develop novel methods for automatic translation from Swiss German to High German that are able to cope with a lack of large text corpora.
In the context of Facility 4.0, we will study the optimization of buildings energy consumption hand in hand with our partners. This will allow us to focus on real world anomalies in the vast amount of data at our disposal; thanks to our BBData infrastructure.
In this thesis, we would like to explore the possibilities of machine learning techniques to detect anomalies in time-series data.
In an international industrial research project we apply the latest technologies in machine learning to do handwriting recognition in tax forms.
This thesis by Oussama Zayene aims to contribute to the current research in the field of Video Optical Character Recognition (OCR) by developing novel approaches that automatically detect and recognize embedded Arabic text in news videos.
This thesis of Baptiste Wicht investigates the use of Deep Learning feature extraction for image processing tasks. The goal being to see if these features are able to outperform hand-crafted features and how difficult it is to generate such features.
In this PhD thesis by Beat Wolf, a graphical data analysis pipeline has been developed to make the analysis of genetic data easier in a diagnostics environment. Intuitive graphical user-interfaces as well as optimized algorithms have been developed to alllow geneticists to perform the data analysis even on off the shelf hardware. We also developed a distributed programming language called POP-Java to make the development of distributed algorithms easier.
PhD Thesis by Christophe Gisler: time series represent a large part of the data supply worldwide and many data mining tasks, such as prediction and classification, are concerned with them. This thesis focused on the analysis and development of generic machine learning approaches to multivariate time series classification.
PhD thesis of Lu Yao - Overhigh data redundancy and communication load are the most conventional challenges for the constrained resources of wireless sensors. How to efficiently transport the designated sensor readings from source nodes to sink nodes by using in-network data aggregation functions is the most important issue concerned in this thesis. For the purpose of guiding the transmission direction of data messages, Ant Colony Optimization (ACO) is embedded into the routing process.
PhD thesis of Antonio Ridi: In this thesis we describe our work on the use of generative modeling for the classi cation of time series in the context of in-home monitoring. More precisely, we use generative approaches as Gaussian Mixture models (GMMs) and Hidden Markov models (HMMs).
PhD Thesis of Gérôme Bovet - The thesis is about the conception and implementation of a framework to develop dedicated smart buildings applications. By relying on Web technologies, we demonstrate the development of seamless services while reusing the available resources within the network. We also demonstrated in this thesis the exposure of machine learning services made accessible through Web interfaces and hiding the complexity of the process.
Thesis of Nayla Sokhn - Niche-Overlap graphs depict the competition between species in the nature. In this thesis, we explore these networks from three different perspective: algorithmic, structure and dynamics.