L'IA pour la vie

Le programme de recherche l’IA pour la Vie exploite l’intelligence artificielle pour approfondir notre compréhension des organismes vivants, d’un état de bonne santé aux maladies complexes. Nos recherches intègrent des données variées, longitudinales et interventionnelles avec des connaissances d’experts, afin de développer des technologies pilotées par l’IA qui améliorent la compréhension biologique, facilitent le diagnostic, ouvrent de nouvelles voies thérapeutiques et soutiennent les patients.

En collaboration avec des experts biomédicaux et des professionnels de santé, nous concevons des modèles qui contribuent à révéler les mécanismes des maladies, qu’il s’agisse de cancers, de pathologies neurodégénératives, de troubles de la santé mentale ou de maladies rares. Grâce à des partenariats industriels, nous développons des modèles et des systèmes qui soutiennent la découverte de nouveaux traitements et optimisent les chaînes de développement de médicaments. Nous créons également des technologies d’assistance qui améliorent la qualité de vie, facilitent la communication et favorisent des solutions de santé plus inclusives. En permettant aux individus d’interagir avec leurs propres données de santé, nos recherches comblent les lacunes de connaissances sur les maladies et apportent des preuves solides en faveur de nouvelles approches thérapeutiques.

Domaines d’application

  • Modélisation des systèmes biologiques
  • Optimisation de la découverte de médicaments et des essais cliniques
  • Santé prédictive et préventive
  • Médecine personnalisée et inclusive
  • Diagnostic & Traitement
  • Informatique médicale & Systèmes de données de santé
  • Autonomie & Engagement des patients

Domaines d'expertise

  • Bio-informatique & Informatique de la santé
  • Interaction homme–machine
  • Imagerie & Vision par ordinateur
  • Apprentissage automatique
  • Sécurité & Protection de la vie privée
  • Robotique & Systèmes autonomes
  • Traitement du signal
  • Traitement de la parole & du signal audio

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Domaines d'expertise

Ce programme contribue aux ODD des Nations-Unies suivants


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Personnes

ABBET, Philip
(Senior Research and Development Engineer)
- website


ACHAKRI, Maximilian
(Research Intern)


ALONSO DEL BARRIO, David
(Research and Development Engineer)


AMIRI, Mahdi
(PhD Student / Research Assistant)


ANNAPUREDDY, Ravinithesh Reddy
(PhD Student / Research Assistant)
- website


BAKKER, Saray
(Research Intern)


BECKMANN, Pierre
(PhD Student / Research Assistant)
- website


BEN MAHMOUD, Imen
(Research and Development Engineer)
- website


BILALOGLU, Cem
(PhD Student / Research Assistant)
- website


BOLLENGIER, Alexis
(Research Intern)


BORNET, Olivier
(Head of Research and Development Team)
- website


BROS, Victor
(PhD Student / Research Assistant)


BURDISSO, Sergio (Gastón)
(R&D / Research Assistant)


CALINON, Sylvain
(Senior Research Scientist)
- website


CANÉVET, Olivier
(Senior Research and Development Engineer)
- website


CAROFILIS VASCO, Roberto Andrés
(Postdoctoral Researcher)


CARRON, Daniel
(Senior Research and Development Engineer)
- website


CLIVAZ, Guillaume
(Senior Research and Development Engineer)
- website


DARWICHE, Nael
(Research Intern)


DAYER, Yannick (Nicolas)
(Senior Research and Development Engineer)
- website


DELMAS, Maxime
(Postdoctoral Researcher)


DROZ, William
(Senior Research and Development Engineer)
- website


EL HAJAL, Karl
(PhD Student / Research Assistant)


FOURNIER, Lisa
(Research Intern)


FREITAS, André
(Research Scientist)
- website


FUCHS, Michael
(Postdoctoral Researcher)
- website


GAIST, Samuel
(Senior Research and Development Engineer)
- website


GATICA-PEREZ, Daniel
(Senior Research Scientist with Academic Title)
- website


GATTI, Martina
(Research Intern)


GUPTA, Anshul
(PhD Student / Research Assistant)
- website


HERMANN, Enno
(Postdoctoral Researcher)
- website


HERMUS, James (Russell)
(Postdoctoral Researcher)
- website


HOU, Mingchi
(PhD Student / Research Assistant)


HOVSEPYAN, Sevada
(Research Associate)


JULLIEN, Maël
(Research Intern)
- website


JUNG, Vincent
(PhD Student / Research Assistant)


KALOGA, Yacouba
(Postdoctoral Researcher)


KAYAL, Salim
(Senior Research and Development Engineer)
- website


KHALIL, Driss
(Junior R&D / Research Assistant)
- website


KIM, Haeeun
(Research Assistant)


KODRASI, Ina
(Research Scientist)
- website


KULKARNI, Ajinkya (Vijay)
(Postdoctoral Researcher)


KULKARNI, Atharva (Abhijit)
(Research Intern)


KUMAR, Shashi
(PhD Student / Research Assistant)
- website


LI DONG, Virgílio
(Apprentice)


LI, Yiming
(PhD Student / Research Assistant)
- website


LÖW, Tobias
(Postdoctoral Researcher)
- website


LUISIER, Raphaëlle
(External Research Fellow)
- website


LUTZIGER, Daniel
(Research Intern)


MACEIRAS, Jérémy
(Senior Research and Development Engineer)
- website


MAGIMAI DOSS, Mathew
(Senior Research Scientist)
- website


MARCEL, Christine
(Senior Research and Development Engineer)
- website


MARIĆ, Ante
(PhD Student / Research Assistant)
- website


MAYORAZ, André
(Research and Development Engineer)
- website


MESSORI, Elisa
(Research Intern)


MICHEL, Samuel
(Research and Development Engineer)
- website


MOHAMMADI, Amir
(Research and Development Engineer)


MOHR, Isabelle
(PhD Student / Research Assistant)


MOTLICEK, Petr
(Senior Research Scientist)
- website


NANCHEN, Alexandre
(Senior Research and Development Engineer)
- website


ODOBEZ, Jean-Marc
(Senior Research Scientist with Academic Title)
- website


ÖZBULAK, Gokhan
(PhD Student / Research Assistant)
- website


PALLANCA, Olivier
(Postdoctoral Researcher)


PARK, Gyeonglee
(Research Intern)


PULVIRENTI, Roberto
(Research Assistant)


PUROHIT, Tilak
(Research Intern)


RABELLO DOS ANJOS, André
(Research Scientist)
- website


RAJAPAKSHE, Shalutha
(PhD Student / Research Assistant)
- website


RAZMJOO FARD, Amirreza
(PhD Student / Research Assistant)
- website


SCHONGER, Martin
(PhD Student / Research Assistant)
- website


SENFT, Emmanuel
(Research Scientist (CRG Head))
- website


SPAHR, Guillaume
(Research Intern)


TAFASCA, Samy
(PhD Student / Research Assistant)


TIMONINA-FARKAS, Anna
(Research Associate)


VERZAT, Colombine
(Senior Research and Development Engineer)
- website


VILLAMIZAR, Michael (Alejandro)
(Research Associate)
- website


VILLATORO TELLO, Esaú
(Research Associate)
- website


VUILLECARD, Pierre
(PhD Student / Research Assistant)


WATAWANA, Hasindri (Sankalpana)
(PhD Student / Research Assistant)
- website


WYSOCKA, Magdalena
(Research Intern)


WYSOCKI, Oskar
(Postdoctoral Researcher)


XU, Lei
(PhD Student / Research Assistant)


XU, Zhi Ming
(Research Intern)


XUE, Teng
(PhD Student / Research Assistant)
- website


ZANGGER, Alicia
(Research and Development Engineer)
- website


ZHANG, Yan
(PhD Student / Research Assistant)
- website


 

Publications choisies

Deep Learning Architectures for Estimating Breathing Signal and Respiratory Parameters from Speech Recordings. S. Nallanthighal, Z. Mostaani, A. Härma, H. Strik, and M. Magimai-Doss. Neural Networks, 2021.

This work is a result of a collaboration between Idiap and Philips Research Eindhoven on deep learning techniques for sensing breathing signal and breathing parameters from speech for healthcare. This work has potential implications for the development of novel speech-based methods for healthcare, such as breathing monitoring in telehealth and assessment for early recognition of abnormal breathing syndromes.

 

Spatially-Variant CNN-Based Point Spread Function Estimation for Blind Deconvolution and Depth Estimation in Optical Microscopy. A. Shajkofci and M. Liebling. IEEE Trans. on Image Processing, 29, 5848–5861, 2020.

Optical microscopy is a central tool for biomedical research and diagnostics, which calls for instruments performance to be continuously improved. We leverage a learning approach to locally determine image distortion parameters in the form of a parametric point spread function without requiring instrument- or object-specific calibration. This approach is robust to photon noise and is a key element for downstream applications, such as spatially variant deconvolution, depth localization, and flow estimation. This work is an outcome of the SNSF project Computational biomicroscopy: advanced image processing methods to quantify live biological systems (2018-2022).

 

Image-based Deep Learning Reveals the Responses of Human Motor Neurons to Stress and VCP-related ALS. Verzat, J. Harley, R. Patani, R. Luisier. Neuropathology and Applied Neurobiology, 48 (2): e12770, February 2022.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive and incurable neurodegenerative disease, for which the early cellular and molecular events remain poorly understood. Morphological attributes of cells and their substructures have been relatively understudied in ALS research. Transfer learning was used to leverage the power of imaging fluorescent data to enable unbiased, robust and efficient testing of biological hypotheses, and to resolve the extent of aberrant cellular morphological indices during earlier phases of ALS pathogenesis. This work, performed in collaboration with the Francis Crick Institute, is transformational to the information that can be gleaned from image analysis.

Projets choisis

AI4Autism, 2021-2025, SNSF, Sinergia project, Odobez

ChaSpeePro, 2021-2025, SNSF Sinergia, Kodrasi

NeuroCIRT, 2022-2026, SNSF, Luisier


 

Liste de tous les projets

AI4Autism, 2021-2025, SNSF, Sinergia project, Odobez

Design automatic children behavior and interaction analysis digital tools for the screening and automated profiling of autism phenotype

 

ePartner4ALL, 2021-2024, Eureka NL-CH call, Odobez

A (personalized and) blended care solution with virtual buddy for child health

 

BipedAI, 2023-2024, Innosuisse, Odobez

Develop perception tools for helping blind people navigate outdoors

 

WeNet, 2019-2023, H2020, Gatica-Perez

Smartphone sensing to support well-being of university students

 

AI4Media, 2020-2024, H2020, Gatica-Perez

AI-based analysis of European health news

 

SECure, 2022-2024, The Ark foundation, Anjos, Freitas

Safe & Explainable Clinical AI for Orthopaedic Surgical Assessment with Med4CAST

 

CAD4IED, 2022-2023, Idiap & Luzerner Kantonsspital, Anjos

Computer assisted detection and grading of inflammatory eye diseases via fluorescein angiograms

 

ABRoad, 2022-2024, The Ark, Freitas

AI-augmented scientific discovery of new antibiotics

 

LunarBase, 2023-2024, The Ark, Freitas

LunarBase: industrial-scale inference

 

NCCR Evolving Language, 2020-2024, SNSF, Garner

Low-level mechanisms of language evolution

 

NCCR Evolving Language, 2020-2024, SNSF, Magimai Doss

Discovering the parallel between human and animal communications, as part of biological discovery

 

TIPS, 2019-2023, SNSF, Magimai Doss

Exploring relation between speech activity, breathing activity and cardiac activity

 

EMIL, 2021-2025, Bridge Discovery, Magimai Doss

Prediction and monitoring of Parkinson’s Disease symptoms

 

SWITCH, 2021-2024, SNSF, Calinon

Learning by switching roles in physical human-robot collaboration for physical assistance for sitting/standing

 

INTELLIMAN, 2022-2026, Horizon Europe, Calinon

AI-powered manipulation system for advanced robotic service, manufacturing and prosthetics for prosthetic hands

 

Melas, 2023-2025, Idiap-NIBR, Luisier

Towards the development of predictive biomarkers for patient stratification and immunotherapy response in cancer

 

NeuroCIRT, 2022-2026, SNSF, Luisier

Investigating the role of cytoplasmic intronic sequences in ALS pathogenesis

 

MelAS, 2022-2023, Novartis Foundation for Medical-Biological Research, Luisier

Alternative splicing and polyadenylation from single-cell RNA sequencing towards tumor subpopulation identification in melanoma

 

ChaSpeePro, 2021-2025, SNSF Sinergia, Kodrasi

Characterizing speech disorders and processes