The Institute hosts speakers for public conferences at Idiap on a regular basis.


Past events


A Critical Systems Thinking approach to implementing ethics in a medical AI app

Magali Goirand (Macquarie University in Sydney)

September 08, 2023


Implementing ethics in medical AI is a complex issue that is context and worldview sensitive for which Critical Systems Thinking is a well-suited methodology. Such an approach involves a participatory process engaging diverse stakeholders. This study has set out to explore how to use a Systems Thinking approach involving stakeholders for a Covid-19 AI app. In this talk, study design, results and outcomes will be shared including guidelines about the process itself.


DisorBERT: A Double Domain Adaptation Model for Detecting Signs of Mental Disorders in Social Media

Dr. Adrian Pastor López-Monroy from the Mathematics Research Center (CIMAT), Mexico

August 10, 2023


Mental disorders affect millions of people worldwide and cause interference with their thinking and behavior. Through the past years, awareness created by health campaigns and other sources motivated the study of these disorders using information extracted from social media platforms. In this work, we aim to contribute to the study of these disorders and to the understanding of how mental problems reflect on social media. To achieve this goal, we propose a double-domain adaptation of a language model. First, we adapted the model to social media language, and then, we adapted it to the mental health domain. In both steps, we incorporated a lexical resource to guide the masking process of the language model and, therefore, to help it in paying more attention to words related to mental disorders. We have evaluated our model in the detection of signs of three major mental disorders: Anorexia, Self-harm, and Depression. Results are encouraging as they show that the proposed adaptation enhances the classification performance and yields competitive results against state-of-the-art methods.


Regularized information geometric and optimal transport distances for Gaussian processes

Dr Minh Ha Quang (RIKEN AIP)

March 7, 2023


Information geometry (IG) and Optimal transport (OT) have been attracting much research attention in various fields, in particular machine learning and statistics. In this talk, we present results on the generalization of IG and OT distances for finite-dimensional Gaussian measures to the setting of infinite-dimensional Gaussian measures and Gaussian processes. Our focus is on the Entropic Regularization of the 2-Wasserstein distance and the generalization of the Fisher-Rao distance and related quantities. In both settings, regularization leads to many desirable theoretical properties, including in particular dimension-independent convergence and sample complexity. The mathematical formulation involves the interplay of IG and OT with Gaussian processes and the methodology of reproducing kernel Hilbert spaces (RKHS). All of the presented formulations admit closed form expressions that can be efficiently computed and applied practically.


Action Recognition for People Monitoring

François Brémond – STARS – INRIA – Sophia Antipolis

August 17, 2022


In this talk, we will discuss how Video Analytics can be applied to human monitoring using as input a video stream. Existing work has either focused on simple activities in real-life scenarios, or on the recognition of more complex (in terms of visual variabilities) activities in hand-clipped videos with well-defined temporal boundaries. We still lack methods that can retrieve multiple instances of complex human activity in a continuous video (untrimmed) flow of data in real-world settings.

Therefore, we will first review few existing activity recognition/detection algorithms. Then, we will present several novel techniques for the recognition of ADLs (Activities of Daily Living) from 2D video cameras. We will illustrate the proposed activity monitoring approaches through several home care application datasets: Toyota SmartHome, NTU-RGB+D, Charades and Northwestern UCLA. We will end the talk by presenting some results on home care applications.


Physics-based modeling and the quest for intelligent robots

Prof. Stelian Coros, Computational Robotics Lab, ETH Zurich

June 30, 2022


Thanks to recent advances in sensing, perception and actuation technologies, robots are no longer just mindless machines designed to perform repetitive tasks on factory floors. Nevertheless, the vision of intelligent robotic assistants capable of helping us with every-day tasks at work and at home remains elusive. This is to a large extent because, unlike humans, robots lack an innate understanding of the physical principles that govern the dynamics of the physical world. To overcome this technological barrier, our group develops theoretical and algorithmic foundations for computational models that enable machines to predict how physical objects move and deform. Our efforts in this area have led to an analytically differentiable formulation of dynamics for multi-body systems. Within a unified framework, our simulation model handles rigid bodies, deformable objects, as well as frictional contact. In this talk, I will present our simulation framework and show how it can be used for tasks such as trajectory optimization, policy learning and computational design. Through a set of applications that range from soft robot locomotion to dynamic manipulation of deformable objects, I will also highlight early successes in using our simulation model to bridge the reality gap.


Optical Tracking - from the lab to the NBA and the English Premier League

Horesh Ben Shitrit and Charles Dubout

June 28, 2022


In this talk, a complete automatic real-time system for sport analytics, including tracking 3D skeletal pose of multiple players and the ball from multiple video cameras will be presented. This system was developed by Second Spectrum and successfully deployed in top tier sport leagues including the NBA and the English Premier League.

About Second Spectrum

Second Spectrum creates products that fuse design with spatiotemporal pattern recognition, machine learning, and computer vision to create sports insight and experiences. In Sep 2015, Second Spectrum acquired PlayfulVision, a spin-off from EPFL, which provided video-based player and ball tracking technology. After integrating its core technology, Second Spectrum has signed multiple multi-years league-wide contracts with major sports leagues such as the NBA (basketball), the English Premier League and MLS (football) to be their official player tracking provider. In May of 2021, Second Spectrum was acquired by the Genius Sports group, which covers more than 400 leagues worldwide.


Active interaction between robots and humans for automatic curriculum learning and assistive robotics

Dr. Sao Mai Nguyen - IMT Atlantique

November 17, 2021


We illustrate through the example of a robot coach for physical rehabiliation the application of GMM in Riemanian manifolds, but also the need to represent complex movements and tasks, as well as the need to evaluate the motivation for patients to interact with their coach.

This motivation to interact is modeled through the theory of intrinsic motivation.

Multi-task learning by robots poses the challenge of the domain knowledge: complexity of tasks, complexity of the actions required, relationship between tasks for transfer learning. However, this domain knowledge can be learned to address the challenges of high-dimensionality and unboundedness in life-long learning. For instance, the hierarchy between tasks of various complexities can be learned to bootstrap transfer of knowledge from simple to composite tasks.

We focus in hierarchical reinforcement learning framework, on algorithms based on intrinsic motivation to explore the action space and task space. They can discover the relationship between tasks and learnable subtasks. Robots can efficiently associate sequences of actions to multiple control tasks: representations of task dependencies, emergence of affordances mechanism, curriculum learning and active imitation learning. These active learning algorithms choose the most appropriate exploration strategy based on empirical measures of competence and learning progress. It infers its curriculum by deciding which tasks to explore first, how to transfer knowledge, and when, how and whom to imitate.