If you are interested in doing a Master thesis or semester project in data science, you can apply for one of the projects bellow. Your primary supervisors will be Francisco Pinto and Patrick Jermann, from the Center for Digital Education (CEDE).
List of Projects
- Implement Graph Clustering Algorithm [Master level]
- Predict Research Collaborations based on Historical Paper Co-Authorships [Master level]
- Predict Investment Opportunities for EPFL Startups [Master level]
Implement Graph Clustering Algorithm
Level: Master
Subject area(s): Discrete mathematics, Graph theory.
Description: The EPFL knowledge graph (graphsearch.epfl.ch) maps all courses and publications to a network of scientific concepts extracted from Wikipedia, that we call the concepts graph. The goal of this project is to develop a hierarchical graph clustering algorithm that organises the massive number of concepts in the graph into areas and sub-areas.
Pre-requisites: Proficiency in Python; solid knowledge of discrete mathematics and/or graph theory.
Useful tools: Python notebooks, NetworkX.
Contact: [email protected]
Please attach the grade transcripts from both your Bachelor and Master studies.
Predict Research Collaborations based on Historical Paper Co-Authorships
Level: Master
Subject area(s): Machine learning, graph theory.
Description: The EPFL knowledge graph (graphsearch.epfl.ch) contains a historical network of paper co-authorships dating back 15+ years. The goal of this project is to use that historical data to predict, using machine learning techniques, and recommend future collaborations to our doctoral students and postdocs. NOTE: We receive many applications for our machine learning projects; keep in mind that we’ll give priority to students who have a solid mathematical knowledge of graph theory.
Pre-requisites: Proficiency in Python; experience with machine learning algorithms, including ANNs and RFs; mathematical knowledge of graph theory. Bonus if you’ve taken course(s) on discrete mathematics.
Useful tools: Python notebooks, NetworkX, Apache GraphX.
Contact: [email protected]
Please attach the grade transcripts from both your Bachelor and Master studies.
Predict Investment Opportunities for EPFL Startups
Level: Master
Subject area(s): Machine learning, graph theory.
Description: The EPFL knowledge graph (graphsearch.epfl.ch) contains a historical list of funding rounds and VC investor networks dating back a few years. The goal of this project is to use that historical data to predict, using machine learning techniques, and recommend investment oportunities for EPFL labs and researchers who wish to spin-off their work into a startup. NOTE: We receive many applications for our machine learning projects; keep in mind that we’ll give priority to students who have a solid mathematical knowledge of machine learning and graph theory.
Pre-requisites: Proficiency in Python; experience with machine learning algorithms, including ANNs and RFs; mathematical knowledge of graph theory. Bonus if you’ve taken course(s) on discrete mathematics.
Useful tools: Python notebooks, NetworkX, Apache GraphX.
Contact: [email protected]
Please attach the grade transcripts from both your Bachelor and Master studies.