Research Groups
Speech & Audio Processing
Head: Prof. Hervé Bourlard
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Speech processing has been one of the mainstays of Idiap’s research portfolio for many years. Today it is still the largest group in the institute, and Idiap continues to be recognized as a leader in the field. The expertise of the group encompasses statistical automatic speech recognition (based on hidden Markov models or hybrid systems exploiting connectionist approaches), text-to-speech, and generic audio processing (covering sound source localization, microphone arrays, speaker diarization, audio indexing, very-low-bit-rate speech coding, and perceptual background noise analysis for telecommunication systems).
Social Computing
Head: Prof. Daniel Gatica-Perez
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Social computing is an interdisciplinary domain that integrates theories and models from mobile and ubiquitous computing, multimedia, machine learning, and social sciences in order to sense, analyze, and interpret human and social behavior in daily life, and to create devices and systems that support interaction and communication. Current lines of research include ubiquitous sensing of face-to-face interaction, behavioral analysis of social video, crowdsourcing, and urban datamining using smartphones and mobile social networks.
Machine Learning
Head: Prof. François Fleuret
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The goal of our group is the development of new machine learning techniques, with a particular interest in their computational properties. Our application domain is mainly computer vision and includes object detection, scene analysis, tracking of persons and biological structures, and image recognition in general.
Perception & Activity Understanding
Head: Dr. Jean-Marc Odobez
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This group conducts research into human–human activity analysis using multimodal data. This entails the investigation of fundamental tasks such as the representation, detection, segmentation, and tracking of objects and people, the characterization of their state, and the modeling of sequential data and the interpretation of that data in the form of gestures, activities, behavior, or social relationships. These investigations take place through the design of principled algorithms that extend models from computer vision, statistical learning, or multimodal signal processing. Surveillance, traffic analysis, analysis of behavior, human–robot interfaces, and multimedia content analysis are the main application domains.
Uncertainty Quantification and Optimal Design
Head: Prof. David Ginsbourger
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The Uncertainty Quantification and Optimal Design group focuses on quantifying and reducing uncertainties in the context of hi-fidelity models, with a main expertise in Gaussian process methods and the sequential design of computer experiments for optimization, inversion, and related problems. Application domains notably include energy and geosciences, with current collaborations ranging from safety engineering to hydrology and climate sciences.
Computational Bioimaging
Head: Prof. Michael Liebling
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This group focuses on research into computational imaging and the analysis of biomedical images. This includes developing algorithms for image deconvolution and super-resolution in optical microscopy, three-dimensional tomography reconstruction from projections, and—more generally—combining unusual sensing devices and approaches with computational methods to produce images ideally suited to the observation and quantification of complex and live biological systems.
Robot Learning & Interaction
Head: Dr. Sylvain Calinon
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The Robot Learning & Interaction group focuses on human-centric robot applications. The scientific objective is to develop probabilistic approaches for encoding movements and behaviors in robots evolving in unconstrained environments. In these applications, the models serve several purposes (recognition, prediction, online synthesis), and are shared by different learning strategies (imitation, emulation, incremental refinement, or exploration). The aim is to facilitate the transfer of skills from end users to robots, or between robots, by exploiting multimodal sensory information and by developing intuitive teaching interfaces.
Natural Language Understanding
Head: Dr. James Henderson
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The Natural Language Understanding group works at the intersection of machine learning and natural language processing, with an emphasis on representation learning for the meaning of language, attention-based deep learning models, and structured prediction. We model summarization, abstraction (textual entailment), machine translation, knowledge extraction, syntactic structure, and lexical semantics, among other NLP problems. We develop deep learning models of the discovery and prediction of entities and their relations at multiple levels of representation for multiple tasks.
Biometrics Security and Privacy
Head: Dr. Sébastien Marcel
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Biometrics refers, in computer science, to the automatic recognition of individuals based on their behavioral and biological characteristics. The Biometric Person Recognition group investigates and develops novel image-processing and pattern-recognition algorithms for face recognition (2-D, 3-D, and near-infrared), speaker recognition, anti-spoofing (attack detection), and emerging biometric modes (EEG and veins). The group is geared toward reproducible research and technology transfer, using its own signal-processing and machine-learning toolbox.
Biosignal Processing
Head: Dr. Rabello Dos Anjos, André
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The Biosignal Processing Group was created in 2018. Biosignals are signals from biomedical sensors and their analysis to support medical or related research. This group focuses on biomedical-related areas such as the analysis of e-Health records, human-signal sensing for healthcare and other related applications. Current trends in the field show refreshed interest on the use of machine learning techniques, complementing basic signal and sequence processing, all of which are key domains of research at Idiap. It leverages on Idiap’s expertise on human subject handling, data acquisition, open science and data processing.
Energy Informatics
Head: Dr. Jérôme Kämpf
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The Swiss Energy Strategy 2050 resides in three pillars: increasing the energy efficiency (including the building sector), increasing the use of renewable energy (by their promotion) and withdrawal from nuclear energy. These objectives are perfectly in-line with the Energy Informatics concepts: to exploit state-of-the-art Information and Communication Technologies to tackle global warming and climate change challenges, to increase integration of renewable and distributed energy sources by making energy systems smarter, and to increase energy efficiency beyond what improvements at component level can achieve. In that vein, the Energy Informatics Group at Idiap researches into ways of simulating energy transition pathways with intelligent control and adjustment mechanisms of evolving buildings with retrofitting and use, renewable energy production and energy storage in a changing climate.
Genomics and Health Informatics
Head: Dr. Raphaëlle Luisier
The mass generation of high-content genomic, imaging and digital data has brought rapid advances in our understanding of cell biology and human diseases, and offers promising avenues for the field of precision medicine. A major obstacle revolves around delivering meaningful information from these data, requiring the deployment of a variety of methods such as statistics, machine learning and data visualisation in tight collaboration with clinicians. The Genomics and Health Informatics Group was created in 2019 to develop computational and informatics methods for integrative analysis of such complex data. Applications domains currently include the understanding and early diagnosis of neurodegenerative diseases in collaboration with clinical neuroscientists.