Andrea Cavallaro

EPFL STI IEM LIDIAP
ELD 241 (Bâtiment ELD)
Station 11
1015 Lausanne

Web site:  Web site:  https://idiap.epfl.ch/

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Administrative data

Fields of expertise

I focus on machine learning for audio-visual sensing to enable systems to serve agreed purposes and improve their abilities through interactions with the environment, with other systems and with people.

The goal of my research is to create the next-generation machine perception models for the effective use of sensory data, and the safe operation of autonomous systems that gain information about their environment and make decisions in partnership with humans or independently. These models are transforming the ability of autonomous systems to see, hear and confidently act in previously unseen scenarios.

This research is particularly important to maintain trust in machine learning, now that our society is facing an unprecedented pace of technological change.

Keywords: Machine Learning, Artificial Intelligence, Computer Vision, Audio Processing, Robot Perception, Privacy.

Invited Talks and Keynotes (2025)
- Multi-modal models: from text to images and beyond, ELLIS Winter School (January)
- The pursuit of privacy in the AI age, AMLD (February)
- The pursuit of privacy in the AI age, Keynote at ACM SAC (April)
- Images, perception, and the subjective space of privacy, Sapienza Univ. (May)
- From data to culture and back: building trustworthy learning systems, IFOSS Summer School (July)
- Images, perception, and the subjective space of privacy, Keynote at EUVIP (October)


Research

Teaching & PhD

Teaching

Electrical and Electronics Engineering

Deep learning: course & project

This course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models.

EE-559 - Group Project.

Theme: Deep learning to foster safer online spaces

Scope: The group mini-project aims to support a safer online environment by tackling hate speech in various forms, ranging from text and images to memes, videos, and audio content.

Objective: To develop deep learning models that help foster healthier online interactions by automatically identifying hate speech across diverse content formats. These deep learning models shall be carefully designed to prioritize accuracy and context comprehension, ensuring they differentiate between harmful hate speech and legitimate critical discourse or satire.

Context: Developing deep learning models that help prevent the surfacing of hateful rhetoric will lead to a more respectful online environment where diverse voices can coexist and thrive.

Open Poster Session on 28 May 2025
Time: 8:30am-1:30pm
Location: MED hall, EPFL

Additional Information

Moodle Page.

Courses

Deep learning

This course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models.