Human–AI Teaming

This research program capitalizes on the well-established expertise at Idiap on multimodal interaction. It leverages Idiap’s unique ability to undertake in-depth multidisciplinary research across verbal and nonverbal communication, language processing, perceptual and cognitive systems, and human–robot interaction.

The aim of the program is to expand human capabilities in several fields (creativity, cognitive limitations, collaboration, knowledge). This research aims to improve machines’ sensing and understanding of human activities, improve information access (e.g., through chatbots serving as on-demand domain experts), use human feedback for improving learning systems, and use robots to assist humans in everyday tasks at work and at home.

 

Expertise domains

#Bioinformatics&HealthInformatics
#DataScience&SocialComputing
#HumanComputerInteraction
#Imaging&ComputerVision
#MachineLearning
#NaturalLanguageProcessing
#Robotics&AutonomousSystems
#Security&Privacy
#SignalProcessing
#Speech&AudioProcessing

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This program contributes to the following UN SDG


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People

ABBET, Philip
(Senior Research and Development Engineer)
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AKSTINAITE, Vita
(Postdoctoral Researcher)
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BEN MAHMOUD, Imen
(Research and Development Engineer)
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BILALOGLU, Cem
(PhD Student / Research Assistant)
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BORNET, Olivier
(Head of Research and Development Team)
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BURDISSO, Sergio (Gastón)
(R&D / Research Assistant)


CALINON, Sylvain
(Senior Research Scientist)
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CANÉVET, Olivier
(Senior Research and Development Engineer)
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CAROFILIS VASCO, Roberto Andrés
(Postdoctoral Researcher)
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CARRON, Daniel
(Research and Development Engineer)
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CHANG, Xiaoguang
(Research Intern)
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CHEN, Haolin
(PhD Student / Research Assistant)
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CLIVAZ, Guillaume
(Senior Research and Development Engineer)
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COMAN, Andrei (Catalin)
(PhD Student / Research Assistant)
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COPPIETERS DE GIBSON, Louise
(PhD Student / Research Assistant)
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DAYER, Yannick
(Research and Development Engineer)
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DE WAHA-BAILLONVILLE, Gilles
(Research Intern)


DELMAS, Maxime
(Postdoctoral Researcher)
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DROZ, William
(Senior Research and Development Engineer)
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EL HAJAL, Karl
(PhD Student / Research Assistant)
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EL ZEIN, Dina
(PhD Student / Research Assistant)
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FARKHONDEH, Arya
(PhD Student / Research Assistant)
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FEHR, Fabio (James)
(PhD Student / Research Assistant)
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FREITAS, André
(Research Scientist)
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GAIST, Samuel
(Senior Research and Development Engineer)
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GARNER, Philip
(Senior Research Scientist)
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GENTILHOMME, Théophile
(Senior Research and Development Engineer)
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GUPTA, Anshul
(PhD Student / Research Assistant)
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HE, Mutian
(PhD Student / Research Assistant)
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HENDERSON, James (Brinton)
(Senior Research Scientist)
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HERMANN, Enno
(Postdoctoral Researcher)
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HERMUS, James (Russel)
(Postdoctoral Researcher)
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HOVSEPYAN, Sevada
(Research Associate)
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ISMAYILZADA, Mahammad
(PhD Student / Research Assistant)
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JIANG, Liangze
(PhD Student / Research Assistant)
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JUNG, Vincent
(PhD Student / Research Assistant)
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KAYAL, Salim
(Senior Research and Development Engineer)
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KHALIL, Driss
(Junior R&D / Research Assistant)
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KULKARNI, Ajinkya (Vijay)
(Postdoctoral Researcher)
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KUMAR, Shashi
(PhD Student / Research Assistant)
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LI, Yiming
(PhD Student / Research Assistant)
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LÖW, Tobias
(PhD Student / Research Assistant)
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MACEIRAS, Jérémy
(Research and Development Engineer)
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MAGIMAI DOSS, Mathew
(Senior Research Scientist)
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MARCEL, Christine
(Senior Research and Development Engineer)
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MARIĆ, Ante
(PhD Student / Research Assistant)
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MAYORAZ, André
(Research and Development Engineer)
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MICHEL, Samuel
(Research and Development Engineer)
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MITRO, Ioanni
(Postdoctoral Researcher)
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MOSTAANI, Zohreh
(PhD Student / Research Assistant)
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MOTLICEK, Petr
(Senior Research Scientist)
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MURALIDHAR, Skanda
(Research Associate)
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NANCHEN, Alexandre
(Senior Research and Development Engineer)
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ODOBEZ, Jean-Marc
(Senior Research Scientist with Academic Title)
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PETERSEN, Molly (Rose)
(PhD Student / Research Assistant)
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PRASAD, Amrutha
(PhD Student / Research Assistant)
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PUROHIT, Tilak
(PhD Student / Research Assistant)
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RAJAPAKSHE, Shalutha
(PhD Student / Research Assistant)
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RANGAPPA, Pradeep
(Postdoctoral Researcher)
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RAZMJOO FARD, Amirreza
(PhD Student / Research Assistant)
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SANCHEZ LARA, Alejandra
(Research Intern)
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SANCHEZ-CORTES, Dairazalia
(Research Assistant)
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SARKAR, Eklavya
(PhD Student / Research Assistant)
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SCHONGER, Martin
(Research Intern)
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SENFT, Emmanuel
(Research Scientist (CRG Head))
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SHIRAKAMI, Haruki
(PhD Student / Research Assistant)
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SYLA, Valmir
(Research Intern)


TAFASCA, Samy
(PhD Student / Research Assistant)
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TARIGOPULA, Neha
(PhD Student / Research Assistant)
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TENEY, Damien
(Research Scientist)
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THORBECKE (NIGMATULINA), Iuliia
(PhD Student / Research Assistant)
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TIMONINA-FARKAS, Anna
(Research Associate)
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TORNAY, Sandrine
(Postdoctoral Researcher)
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VALENTINO, Marco
(Postdoctoral Researcher)
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VAN DER MEER, Michiel
(Postdoctoral Researcher)
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VÁSQUEZ RODRÍGUEZ, Laura
(Postdoctoral Researcher)
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VERZAT, Colombine
(Research and Development Engineer)
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VILLAMIZAR, Michael (Alejandro)
(Research Associate)
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VILLATORO TELLO, Esaú
(Research Associate)
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VLASENKO, Bogdan
(Research Associate)
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VUILLECARD, Pierre
(PhD Student / Research Assistant)
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WYSOCKI, Oskar
(Postdoctoral Researcher)
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XI, Lei
(Research Intern)


XUE, Teng
(PhD Student / Research Assistant)
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ZANGGER, Alicia
(Research and Development Engineer)
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ZHANG, Yan
(PhD Student / Research Assistant)
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Publication highlights

Neural Network Adaptation and Data Augmentation for MultiSpeaker Direction-of-Arrival Estimation, W. He, P. Motlicek and J.-M. Odobez, IEEE/ACM Trans. on Audio, Speech and Language Processing, 29, pp. 1303-1317, 2021.

The first viable deep learning framework (task definition, network architecture, training paradigm) for solving fundamental auditory tasks such as sound source localization, speaker identification and speech/non-speech classification. The framework is suitable for highly noisy environments and overcomes limitations of previous methods, which heavily relied on idealized sound and environment models and are inadequate for everyday situations with multiple sound sources, background noise, short utterances, and lack of prior knowledge on the number of sound sources. The method learns sound source localization models with limited training resources leveraging simulated and weakly-labeled real audio data.

 

Active Learning by Feature Mixing, A. Parvaneh, E. Abbasnejad, D. Teney, G. R. Haffari, A. Van Den Hengel, & J. Q. Shi, In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 12227-12236, 2022.

A method to train deep learning models with humans in the loop. Current approaches to machine learning depend on large amounts of data that are costly or difficult to acquire. This paper presents an active learning approach where human experts interact with the learning algorithm to iteratively refine and resolve inconsistencies in a model by labeling a small set of training examples. This approach contributes to widening the accessibility of machine learning technologies to small organizations.

 

Learning Joint Space Reference Manifold for Reliable Physical Assistance, Razmjoo, A., Brecelj, T., Savevska, K., Ude, A., Petric, T. and Calinon, S., In Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS), 2023.

Project highlights

C-LING, 2022-2026, SNSF, Van der Plas: TOWARDS CREATIVE SYSTEMS WITH LINGUISTIC MODELLING

This project aims to investigate what aspects computational models need to perform creative cognitive tasks, from generating relatively simple novel concepts to more complex and structured ideas, across multiple domains and languages. More in particular, it aims to answer what types of structured and unstructured knowledge are needed and what models best integrate these types of knowledge.

 

NeuMath, 2022-2024, SNSF, Freitas: NEUMATH: NEURAL DISCOURSE INFERENCE OVER MATHEMATICAL TEXTS

NeuMath will develop models which can jointly represent and reason over two symbolic modalities (natural language and mathematical expressions) and will build the foundations to deliver embedding models which can interpret and support the generation of mathematical arguments (by leveraging available large-scale scientific corpora).

 

SMILE-II, 2021-2024, SNSF Sinergia, Magimai Doss: SMILE-II SCALABLE MULTIMODAL SIGN LANGUAGE TECHNOLOGY FOR SIGN LANGUAGE LEARNING AND ASSESSMENT PHASE-II

The proposed project SMILE-II aims to research and build advanced technology for sign language learning. More precisely, the proposed project builds on the groundwork laid down by the SNSF Sinergia project SMILE, which dealt with assessment of the manual activity of Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS) in isolated signs produced by early learners and L2 learners. SMILE-II will extend this technology to continuous sign language assessment including both manual and non-manual components of signs so that a DSGS learner’s sentence-level production can be assessed in an automatic manner.

Full list of related projects

C-LING, 2022-2026, SNSF, Van der Plas

Building computational models of human creative thinking to help with creative tasks

 

NeuMath, 2022-2024, SNSF, Freitas

Neuro-symbolic architectures for supporting mathematical discovery

 

NAST, 2020-2024, SNSF, Garner

Neural architectures for speech technology

 

SteADI, 2021-2025, SNSF, Garner

Storytelling algorithms for digital interviews

 

NKBP, 2020-2024, SNSF, Henderson

Deep learning models for continual extraction of knowledge from text

 

SINFONIA, 2023-2027, Innosuisse, Teney, Freitas

Generalization and domain adaptation of large language models

 

LUCIDELES, 2020-2023, SFOE, Kämpf

Research at the interface between humans and building control systems

 

CODIMAN, 2020-2024, National Research Programme "Digital Transformation", SNSF, Calinon

Cobotics, digital skills and the re-humanization of the workplace

 

SESTOSENSO, 2022-2025, Horizon Europe, Calinon

Physical cognition for intelligent control and safe human-robot interaction

 

SMILE-II, 2021-2024, SNSF Sinergia, Magimai Doss

Assistive technology for sign language learning and testing

 

Amazon research award, 2023, Teney

Addressing underspecification for improved fairness and robustness in conversational AI

 

MALORCA, 2016-2018, EU, Motlicek

Machine learning of speech recognition models for controller assistance

 

HAAWAII, 2020-2022, EU, Motlicek

Highly automated air-traffic controller workstations with artificial intelligence integration

 

ATCO2, 2019-2022, EU, Motlicek

Automatic collection and processing of voice data from air-traffic communications

 

EUROCONTROL, 2023-2024,  France, Motlicek

Automatic speech recognition in air-traffic control simulation