Alexandre Alahi is an associate professor at EPFL leading the Visual Intelligence for Transportation laboratory (VITA). Before joining EPFL in 2017, he spent multiple years at Stanford University as a Post-doc and Research Scientist.
His research lies at the intersection of Computer Vision, Machine Learning, and Robotics applied to transportation & mobility. To make Artificial Intelligence (AI) driven systems such as autonomous vehicles a safe reality, his lab works on a new type of Artificial Intelligence (AI), namely socially-aware AI, i.e., an AI augmented with social intelligence.
In 2022&2023, Alexandre was recognized as one of the top 100 Most Influential Scholar in Computer Vision over the past 10 years.
His research team received the editor's choice award from the journal Image and vision computing (2021) for their work on human motion prediction, the honorable mention at an ICCV workshop (2019) for their work on human pose estimation,
the CVPR Open Source Award (2012) for their work on Retina-inspired image descriptors, and the ICDSC Challenge Prize (2009) for their sparsity-driven algorithm that has tracked more than 100 million pedestrians to date.
His work has been licensed to several companies and covered internationally by BBC, abc, PBS, Euronews, Wall street journal, and other national news outlets around the world. Alexandre has also co-founded multiple startups such as Visiosafe, and won several startup competitions. He was elected as one of the Top 20 Swiss Venture leaders in 2010.

We work on the theoretical challenges and practical applications of socially-aware systems, i.e., machines that can not only perceive human behavior, but reason with social intelligence in the context of transportation problems and smart spaces. We envision a future where intelligent machines are ubiquitous, where self-driving cars, delivery robots, and self-moving Segways are facts of everyday life. Beyond embodied agents, we will also see our living spaces & our homes, buildings, and cities & become equipped with ambient intelligence which can sense and respond to human behavior. However, to realize this future, intelligent machines need to develop social intelligence and the ability to make safe and consistent decisions in unconstrained crowded social scenes. Self-driving vehicles must learn social etiquette in order to navigate cities like Paris or Naples. Social robots need to comply with social conventions and obey (unwritten) common-sense rules to effectively operate in crowded terminals. For instance, they need to respect personal space, yield right-of-way, and «read» the behavior of others to predict future actions. Our research is centered around understanding and predicting human social behavior with multi-modal visual data. Our work spans multiple aspects of socially-aware systems: from 1- collecting multi-modal data at scale, 2- Extracting coarse-to-fine grained behaviours in real-time, 3- designing deep learning methods that can learn to predict human social behavior in a fully data-driven way, to 4- integrating the developed methods in real-world systems such as a vehicle or a socially-aware robot that navigates crowded social scenes.