Elena Gavagnin

Lecturer and Senior Researcher

Zurich University of Applied Sciences

About me

Zurich University of Applied Sciences
Institute of Business Information Technology
Office: ST 10.35

I am a senior scientist and lecturer for Data Science at the Zurich University of Applied Science (ZHAW). I am affiliated to the Institute of Business Information Technology and I am an associate fellow of the Centre for Artificial Intelligence. My research interests are visual perception and image understanding using deep learning. My projects are usually at the intersection between social good, environment and sustainability. Fun fact: I hold a PhD in Computational Astrophysics from University of Zurich.


My research interest lays at the intersection between human and artificial intelligence, from a computational perspective.

My focus is mostly on image data, leveraging state of the art multi-modal deep learnining algorithms to be able to understand images at different semantic levels. In particular, I am interested to apply these techniques in contexts, where images carry an additional highly-human amount of information, for example when images are used to comunicate a message, a problem or an intention, or when to object represented in an image is associated a "value", which is the result of a tacit and collective assignement. In these cases, I am interested in studying whether computers can be trained to perceive these meta-meanings images carry and how this can advance our current view on artificial intelligence.

Current fields of applications are waste management and recycling, rehabilitation therapy and citizen-based reporting systems.


Here you can find some of the projects my students have been working on. Have a look!


I am currently teaching the courses "Data Science Fundamentals" (MSc) and "Machine Learning II" (BSc) in the curriculum of Business Information Technology. "Data Science Fundamentals" offers an introduction to several basic aspects of data science, starting from data retrieval, cleaning and management to analysis, visualisation and modelling. "Machine Learning II" focuses strongly on deep learning, with application in computer vision and natural language processing.

Besides I am supervisior of several bachelor, master and semester projects. For ZHAW students: If you are interested in writing your thesis with me, feel free to contact me.


5. "Foundations of Data Science : a comprehensive overview formed at the 1st International Symposium on the Science of Data Science."
F. Schilling, D. Flumini, R. F├╝chslin, E. Gavagnin, A. Geller, S. Quarteroni, T. Stadelmann,
Archives of Data Science, Series A. 8(2)

4. "Star cluster formation in a turbulent molecular cloud self-regulated by photoionization feedback. "
E. Gavagnin, A. Bleuler, J. Rosdahl, R. Teyssier,
Monthly Notices of the Royal Astronomical Society, Volume 472, Issue 4, December 2017"

3. "A critical look at the merger scenario to explain multiple populations and rotation in iron-complex globular clusters"
E. Gavagnin, M. Mapelli, G. Lake,
Monthly Notices of the Royal Astronomical Society, Volume 461, Issue 2, 11 September 2016

2. "The Gaia-ESO Survey: N-body modelling of the Gamma Velorum cluster"
M. Mapelli, A. Vallenari, R. D. Jeffries, E. Gavagnin, et al.,
A&A, 578 (2015) A35