Alexandre Alahi

GC C1 383 (Bâtiment GC)
Station 18
CH-1015 Lausanne


Administrative data

Fields of expertise

Transportation and Mobility
Socially-aware Artificial Intelligence
Computer Vision
Machine Learning
Deep Learning
Human-Robot Interaction
Ambient Intelligence


Teaching & PhD


  • Civil Engineering,

PhD Programs

  • Doctoral Program in Civil and Environmental Engineering
  • Doctoral program in computer and communication sciences
  • Doctoral program in robotics, control, and intelligent systems
  • Doctoral Program in Electrical Engineering


Data and artificial intelligence for transportation

Data science and Artificial Intelligence (AI) are poised to reshape the transportation industry with self-driving cars, delivery robots, self-moving segways, or smart terminals. In this course, students will learn the fundamentals behind these AI-driven s... goto


Research summary

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.