Swiss Data Science Center

This page lists the Swiss Data Science Center projects available to EPFL students. The SDSC is a joint venture between EPFL and ETH Zurich. Its mission is to accelerate the adoption of data science and machine learning techniques within academic disciplines of the ETH Domain, the Swiss academic community at large, and the industrial sector. In particular, it addresses the gap between those who create data, those who develop data analytics and systems, and those who could potentially extract value from it. The center is composed of a large multi-disciplinary team of data and computer scientists, and experts in select domains, with offices in Lausanne and Zurich.
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Projects – Autumn 2021

It may be possible to convert a thesis project into a semester project or extend a semester project to be suitable for a thesis project. If any of the present or past projects interests you, please feel free to contact us. We are always looking forward to meeting motivated and talented students who want to work on exciting projects.

Laboratory:
Swiss Data Science Center

Type:
Semester Project

Description:
Polysomnography (PSG) is the current gold standard for sleep monitoring and clinical assessment of sleep disorders. However, this technique is intrusive, in general limited to a single night in-hospital of recordings, and therefore not representative of “natural sleep” at home. Alternative, inexpensive, and non-intrusive monitoring is thus needed for accurate and recurrent sleep monitoring. In the project, we aim to predict, using machine learning algorithms, sleep-stages and potential sleep disorders, from pressure sensors recordings located in a mat.

Goals/benefits:

  • Getting a first experience in supervised machine learning.
  • Experience working with a real data set from medical sciences.
  • Possibility to participate to the development of a start-up.

Prerequisites:

  • Courses in machine learning and statistics (time series analysis).
  • Basic knowledge of Python/R and corresponding packages (scikitlearn, pandas, . . . ).
  • Knowledge of Jupyter Notebooks/RMarkdown.

Deliverables:

  • Clean and documented code
  • Report
  • Oral presentation

Contact:
Raphaël de Fondeville: [email protected]