Trial lecture
“Machine Learning for classification: Their pros and cons, and the ability to generalize to unseen data”
Time and place: April 1st, 2025 11:15 AM. Kristen Nygaards sal (5370), Ole-Johan Dahls hus.
Main research findings
Transport Mode Detection (TMD) refers to systems capable of distinguishing between various transport modes, such as cars and buses, and is a crucial resource for urban planning, transportation, etc.
Traditional TMD methods, such as user-reported surveys, impose limitations, like user error. Therefore, current solutions have shifted toward leveraging smartphones. The ubiquitous nature of smartphones and their advanced built-in sensors position them as a rich source of data and a robust platform for running TMD applications.
In this thesis, we focus on developing TMD systems that employ Machine Learning algorithms to infer the transport modes directly on the smartphone, known as local TMD. We aim to address the challenges of local TMD systems, including accuracy, battery consumption, delay, and generalization while mitigating privacy concerns.
Following a comprehensive review, we propose three solutions, FogTMDetector, TeLeGaiT, and FogFLeeT, each improving the previous, employing a fog-based architecture that leverages the edge-fog-cloud layers. FogTMDetector uses a Random Forest trained on data from magnetometers, accelerometers, and GPS for real-time detection. TeLeGaiT employs Transfer Learning to enhance the generalization of a Multi-Layer Perceptron model across cities. FogFLeeT proposes a Federated Transfer Learning framework addressing accuracy and privacy challenges while providing an adaptive solution across cities and smartphones.
Adjudication committee
- Assistant Professor Mélanie Bouroche, Trinity College Dublin, Ireland
- Assistant Professor João Garcia, University of Lisbon, Portugal
- Senior Adviser, PhD, Geir Horn, University of Oslo, Norway
Supervisors
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Professor Paulo Jorge Pires Ferreira, University of Oslo, Norway
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Professor Eric Bartley Jul, University of Oslo, Norway
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Professor Amirhosein Taherkordi, University of Oslo, Norway
Chair of defence
- Associate Professor Ragnhild Kobro Runde, Department of Informatics, University of Oslo
Contact information at Department: Mozhdeh Sheibani Harat