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A new professor joins the Automated Manufacturing Engineering Department
April 6, 2017
As a Professor-Researcher, Marc Pedersoli plans to examine ways in which the machine learning process can be improved, especially with respect to images.
Social media now provides us with massive quantities of data in a variety of formats, including text, audio and video. In order to advance, machine learning still has to annotate all of these data. However, producing these annotations relies on experts, whose services are expensive, not to mention the fact that these experts are encountering difficulties related to the ever-increasing volume of data.
As a Professor-Researcher, Marc Pedersoli plans to examine ways in which the machine learning process can be improved, especially with respect to images. He will attempt to develop learning methods that require minimal annotations.
Marco Pedersoli earned his Bachelor's degree in Electronic Engineering from the University of Brescia in his home country of Italy. His studies led him to travel extensively in Europe. He started out in Barcelona, completing his Master’s and Doctoral degrees in Artificial Vision and Object Recognition, which are extremely useful for automatic driving systems, avoiding pedestrians, recognizing and identifying faces, etc.
He went on to earn two post-doctoral degrees, one in Belgium and one in France. The first focused on human pose estimation and low-supervision machine learning. The second focused on the automatic description of images.
Marco Pedersoli taught at the University of Barcelona and at University of Leuven in Belgium while supervising a number of students in the completion of their Master’s and Doctoral projects.
He also worked on a research project that resulted in the creation of a company called Visual Tagging, which offers market analysis services based on images or videos that have been viewed or liked by Internet users.
Marco Pedersoli will begin teaching at ÉTS in September 2017, and will work with the Imaging, Vision and Artificial Intelligence Laboratory (LIVIA) and the Control and Robotics Laboratory (CoRo).
Areas of research
His research focuses on three areas:
Deep learning with reduced annotations – Marco Pedersoli will be working on low-supervision or semi-supervised learning. Given that machine learning requires an explanatory annotation for each data item, he will focus on ways to reduce the required portion of annotations or on learning with simplified annotations.
Learning through exploration – Learning through exploration is an avenue that requires more in-depth study in order to avoid the need for annotations for each data item. This method is similar to how parents teach young children to speak by identifying items one at a time and naming them. The algorithm would be able to validate or invalidate the response based on experience.
Computational savings – Deep learning with reduced annotations.
For more information:
Service des communications ÉTS
514 396-8800, poste 7893