Dr. Ricardo Chavarriaga

Arbeit an der ZHAW

Tätigkeit

• Leitung, Responsible AI Innovation
• Leitung, CLAIRE Office Zürich
• Redner
• Consultant, mentor, networker

Lehrtätigkeit

• BSc module Reinforcement Learning
• MSc EVA Module Artificial Intelligence Seminar
• MSc EVA Module Machine Intelligence Lab
• Gastdozent an verschiedenen Hochschulen und Universitäten im In- und Ausland

Berufserfahrung

• Chair, Industry Connections Group Neurotechnologies for Brain Machine Interface
  IEEE Standards Association
  04 / 2017 - heute
• Co-leader, Focus Topic: Responsible AI
  Innosuisse - Innovation Booster Databooster
  01 / 2023 - 2024
• Polymath Fellow
  Polymath Fellow
  04 / 2019 - 04 / 2023
• Research Scientist, Lecturer
  EPFL
  01 / 2009 - 08 / 2019
• Research Scientist
  Idiap research Institute, Switzerland
  01 / 2006 - 12 / 2008

Aus- und Weiterbildung

Ausbildung

• PhD / Computer Science - Computational Neuroscience
  EPFL
  08 / 2001 - 12 / 2005
• Graduate School / Computer Science
  EPFL
  09 / 2000 - 07 / 2001
• BSc / Electronics Engineering
  Pontificia Universidad Javeriana, Cali, Colombia
  07 / 1991 - 03 / 1998

Weiterbildung

• EPFL, Switzerland
  Certificate of Advanced Studies (CAS) in Management of Biotech, Medtech, and Pharma Ventures.
  12 / 2019
• PMI, USA
  Professional Project Management (PMP) Certification
  06 / 2019

Netzwerk

Mitglied in Netzwerken

• CAIRNE - Confederation of Laboratories for AI Research in Europe (Head of Zürich office)
• IEEE Standards Association group on Neurotechnologies for brain-machine interfacing (Chair)
• Brain Computer Interface Society
• IEEE Brain Initiative (Core group member)
• Databooster -co-lead Focus Topic: Responsible AI
• IEEE Standards Association group P7700 Recommended Practice for the Responsible Design and Development of Neurotechnologies (Vice-chair)
• IEEE Standards Association group P2863 Recommended Practice for Organizational Governance of AI (Chair Subgroup on Principles)
• IEEE EMBS, SMC, Brain community, Life Sciences community (Senior member)
• Datalab, the ZHAW Data Science Laboratory
• MIT Technology Review Global Panel

ORCID digital identifier

ORCID ID: 0000-0002-8879-2860

Auszeichnungen

DIZH Fellowship 2020
ZHAW Digital
12 / 2020

Social Media

LinkedIn

Projekte

• Trustworthy AI Circle / Stellv. Projektleiter:in / laufend
• A data-driven solution that optimizes ankle-foot-orthopedic braces for children / Projektleiter:in / laufend
• SCRAI – A Think-and-Do-Tank for Responsible Development and Societal Alignment of Artificial Intelligence Systems / Projektleiter:in / laufend
• AI for REAL-world NETwork operation / Teammitglied / laufend
• Certification program for assessing ethics of Autonomous Intelligent Systems (IEEE CertifAIEd Assessor Training) / Stellv. Projektleiter:in / abgeschlossen
• Brain Research International Data Governance & Exchange – Phase II / Co-Projektleiter:in / abgeschlossen
• Apéro Digital: Event Series / Co-Projektleiter:in / abgeschlossen
• Brain Research International Data Governance & Exchange / Co-Projektleiter:in / abgeschlossen
• Stability of self-organizing net fragments as inductive bias for next-generation deep learning / Teammitglied / abgeschlossen
• Consulting Service for the preparation of the GESDA Anticipation Observatory / Projektleiter:in / abgeschlossen
• certAInty – A Certification Scheme for AI systems / Projektleiter:in / abgeschlossen
• Mobile Inclusion Lab / Co-Projektleiter:in / abgeschlossen
• Good practices for responsible development of AI-based applications in healthcare / Projektleiter:in / abgeschlossen
• Visual Food Waste Analysis for Sustainable Kitchens / Teammitglied / abgeschlossen

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

• Kölbl, N. et al. (2026) ‘The predictive brain : neural correlates of word expectancy align with large language model prediction probabilities’, NeuroImage, 334(121966). doi: 10.1016/j.neuroimage.2026.121966.
• Bolck, H. et al. (2026) ‘LINA’s testing infrastructure enables AI to take-off in unmanned aerial vehicles (UAVs)’, Frontiers in Robotics and AI, 13(1764248). doi: 10.3389/frobt.2026.1764248.
• Kölbl, N. et al. (2026) ‘Prediction, syntax and semantic grounding in the brain and large language models’, Scientific Reports, 16(1), p. 8728. doi: 10.1038/s41598-026-41532-0.
• Sultana, M. et al. (2025) ‘An out-of-the-lab evaluation of dry EEG technology on a large-scale motor imagery brain-computer interface dataset’, Journal of Neural Engineering, 22(6). doi: 10.1088/1741-2552/ae2e8a.
• Eke, D., Chavarriaga, R. and Stahl, B. (2025) ‘Decoloniality impact assessment for AI’, AI & Society, 41(3), pp. 2213–2228. doi: 10.1007/s00146-025-02649-4.
• Mussi, M. et al. (2025) ‘Human-AI interaction in safety-critical network infrastructures’, iScience, 28(9), p. 113400. doi: 10.1016/j.isci.2025.113400.
• Carlson, D. E. et al. (2025) ‘The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist : ensuring machine learning advances neural engineering’, Journal of Neural Engineering, 22(2), p. 021002. doi: 10.1088/1741-2552/adbfbd.
• Starke, G. et al. (2025) ‘Finding consensus on trust in AI in health care : recommendations from a panel of international experts’, Journal of Medical Internet Research, 27, p. e56306. doi: 10.2196/56306.
• Iwane, F. et al. (2024) ‘Customizing the human-avatar mapping based on EEG error related potentials during avatar-based interaction’, Journal of Neural Engineering, 21(2), p. 026016. doi: 10.1088/1741-2552/ad2c02.
• Iwane, F. et al. (2023) ‘EEG error-related potentials encode magnitude of errors and individual perceptual thresholds’, iScience, 26(9), p. 107524. doi: 10.1016/j.isci.2023.107524.
• Meng, L. et al. (2023) ‘EEG-based brain-computer interfaces are vulnerable to backdoor attacks’, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, pp. 2224–2234. doi: 10.1109/TNSRE.2023.3273214.
• Porssut, T. et al. (2023) ‘EEG signature of breaks in embodiment in VR’, PLOS ONE, 18(5), p. e0282967. doi: 10.1371/journal.pone.0282967.
• Dell’Agnola, F. et al. (2022) ‘Machine-learning based monitoring of cognitive workload in rescue missions with drones’, IEEE Journal of Biomedical and Health Informatics, 26(9), pp. 4751–4762. doi: 10.1109/JBHI.2022.3186625.
• Ienca, M. et al. (2022) ‘Towards a governance framework for brain data’, Neuroethics, 15(2), p. 20. doi: 10.1007/s12152-022-09498-8.
• Huggins, J. E. et al. (2022) ‘Workshops of the eighth international brain-computer interface meeting : BCIs: the next frontier’, Brain-Computer Interfaces, 9(2), pp. 69–101. doi: 10.1080/2326263X.2021.2009654.
• Jao, P.-K. et al. (2021) ‘EEG correlates of difficulty levels in dynamical transitions of simulated flying and mapping tasks’, IEEE Transactions on Human-Machine Systems, 51(2), pp. 99–108. doi: 10.1109/THMS.2020.3038339.
• Chavarriaga, R. et al. (2021) ‘Standardization of neurotechnology for brain-machine interfacing : state of the art and recommendations’, IEEE Open Journal of Engineering in Medicine and Biology, 2, pp. 71–73. doi: 10.1109/OJEMB.2021.3061328.
• Iwane, F. et al. (2021) ‘Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states’, Journal of Neural Engineering, 18(4), p. 046044. doi: 10.1088/1741-2552/abfa70.
• Jao, P.-K., Chavarriaga, R. and Millan, J. d. R. (2021) ‘EEG-based online regulation of difficulty in simulated flying’, IEEE Transactions on Affective Computing, 14(1), pp. 394–405. doi: 10.1109/TAFFC.2021.3059688.
• Aydarkhanov, R. et al. (2021) ‘Closed-loop EEG study on visual recognition during driving’, Journal of Neural Engineering, 18(2), p. 026010. doi: 10.1088/1741-2552/abdfb2.
• Batzianoulis, I. et al. (2021) ‘Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials’, Communications Biology, 4(1406). doi: 10.1038/s42003-021-02891-8.
• Eke, D. O. et al. (2021) ‘International data governance for neuroscience’, Neuron, 110(4), pp. 600–612. doi: 10.1016/j.neuron.2021.11.017.
• Delaux, A. et al. (2021) ‘Mobile brain/body imaging of landmark‐based navigation with high‐density EEG’, European Journal of Neuroscience, 54(12), pp. 8256–8282. doi: 10.1111/ejn.15190.
• Bontempi, G. et al. (2021) ‘The CLAIRE COVID-19 initiative : approach, experiences and recommendations’, Ethics and Information Technology, 23(Suppl 1), pp. S127–S133. doi: 10.1007/s10676-020-09567-7.
• Aydarkhanov, R. et al. (2020) ‘Spatial covariance improves BCI performance for late ERPs components with high temporal variability’, Journal of Neural Engineering, 17(3), p. 036030. doi: 10.1088/1741-2552/ab95eb.
• Orset, B. et al. (2020) ‘User adaptation to closed-loop decoding of motor imagery termination’, IEEE Transactions on Biomedical Engineering, 68(1), pp. 3–10. doi: 10.1109/TBME.2020.3001981.
• Jeunet, C. et al. (2020) ‘Uncovering EEG correlates of covert attention in soccer goalkeepers : towards innovative sport training procedures’, Scientific Reports, 10(1705). doi: 10.1038/s41598-020-58533-2.
• Zhang, X. et al. (2020) ‘Tiny noise, big mistakes : adversarial perturbations induce errors in brain-computer interface spellers’, National Science Review. doi: 10.1093/nsr/nwaa233.

Buchbeiträge, peer-reviewed

• Bessa, R. J. et al. (2026) ‘Toward a holistic framework for human-AI collaboration in safety-critical systems’, in Curry, E. et al. (eds) Artificial intelligence, data and robotics : foundations, transformations and future directions. Cham: Springer, pp. 343–402. doi: 10.1007/978-3-032-10561-5_13.
• McKinney, Z. et al. (2023) ‘Integrating innovation : the role of standards in promoting responsible development of human–machine systems’, in Fortino, G. et al. (eds) Handbook of Human‐Machine Systems. Hoboken: Wiley, pp. 431–449. doi: 10.1002/9781119863663.ch35.
• Iturrate, I., Chavarriaga, R. and Millán, J. d. R. (2020) ‘General principles of machine learning for brain-computer interfacing’, in Millan, J. d. R. and Ramsay, N. F. (eds) Handbook of Clinical Neurology ; 168. Elsevier, pp. 311–328. doi: 10.1016/B978-0-444-63934-9.00023-8.

Schriftliche Konferenzbeiträge, peer-reviewed

• Fedorova, A. et al. (2025) ‘Continuous assessment-driven requirement elicitation for trustworthy AI systems’, in European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Porto, Portugal, 15-19 September 2025. Zenodo. doi: 10.5281/zenodo.17099001.
• Leyli-abadi, M. et al. (2025) ‘A conceptual framework for AI-based decision systems in critical infrastructures’, in 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, pp. 5799–5806. doi: 10.1109/SMC58881.2025.11342626.
• Weiss, M. and Chavarriaga, R. (2025) ‘Transformative and generative data augmentation for EEG-based BCIs’, in 11th International Brain-Computer Interface (BCI) Meeting, Banff, Canada, 2 -5 June 2025. BCI Society.
• de Neeling, M. G. J. et al. (2025) ‘Reporting checklist for observational implanted and non-implanted neural interface studies : protocol for a Delphi process’, in 11th International Brain-Computer Interface (BCI) Meeting, Banff, Canada, 2 -5 June 2025. BCI Society.
• Frischknecht-Gruber, C. et al. (2025) ‘AI assessment in practice : implementing a certification scheme for AI trustworthiness’, in Görge, R. et al. (eds) Symposium on Scaling AI Assessments (SAIA 2024). Schloss Dagstuhl – Leibniz-Zentrum für Informatik, pp. 15:1–15:18. doi: 10.4230/OASIcs.SAIA.2024.15.
• Sultana, M. et al. (2024) ‘Evaluating dry EEG technology out of the lab’, in 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE, pp. 752–757. doi: 10.1109/metroxraine62247.2024.10797021.
• Billeter, Y. et al. (2024) ‘MLOps as enabler of trustworthy AI’, in 2024 11th IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 37–40. doi: 10.1109/SDS60720.2024.00013.
• Denzel, P. et al. (2024) ‘Towards the certification of AI-based systems’, in 2024 11th IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 84–91. doi: 10.1109/SDS60720.2024.00020.
• Lone, O. et al. (2024) ‘A Python-based open software for EEG-based brain-machine interfaces’, in Annual Meeting of the Swiss Society for Biomedical Engineering SSBE Abstract Book. Winterthur: SSBE, p. 26. doi: 10.21256/zhaw-31394.
• Weng, J. et al. (2024) ‘Certification scheme for artificial intelligence based systems’, in 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-30549.
• Simmler, N. et al. (2021) ‘A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications’, in Proceedings of the 8th SDS. IEEE, pp. 26–31. doi: 10.1109/SDS51136.2021.00012.
• Amirian, M. et al. (2021) ‘Two to trust : AutoML for safe modelling and interpretable deep learning for robustness’, in Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020. Springer. doi: 10.21256/zhaw-22061.

Weitere Publikationen

• Specht, B. et al. (2026) PrivateBoost : privacy-preserving federated gradient boosting for cross-device medical data. medRxiv. doi: 10.64898/2026.02.10.26345891.
• Frischknecht-Gruber, C. et al. (2025) ‘Assessment tool for trustworthy AI systems : operational workflows for compliance assessment with regulatory requirements’, in AI Days @ HES-SO, Geneva and Lausanne, Switzerland, 27–29 January 2025. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-32422.
• Chavarriaga, R., Rickli, J.-M. and Mantellassi, F. (2023) Neurotechnologies : the new frontier for international governance. Geneva Centre for Security Policy. doi: 10.21256/zhaw-28985.
• Weng, J. et al. (2023) ‘certAInty : a certification scheme for AI systems (Innosuisse project)’, in Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-27261.
• Orset, B. et al. (2021) Stopping vs Resting state during motor imagery paradigm. bioRxiv. doi: 10.1101/2021.06.15.448360.
• Perdikis, S. et al. (2020) ‘Context-aware learning for generative models’, IEEE Transactions on Neural Networks and Learning Systems. doi: 10.1109/TNNLS.2020.3011671.
• Chavarriaga, R. (2020) ‘Standards for neurotechnologies and brain-machine interfacing’, IEEE Systems, Man, and Cybernetics Magazine, 6(3), pp. 50–51. doi: 10.1109/MSMC.2020.2995438.

Mündliche Konferenzbeiträge und Abstracts

• Pestilli, F. et al. (2025) ‘Bridging the gap in data sharing : developing international data governance frameworks for brain health research’, in Neuroscience. Elsevier, p. 42. doi: 10.1016/j.neuroscience.2025.05.155.
• Denzel, P. et al. (2023) ‘A framework for assessing and certifying explainability of health-oriented AI systems’, in Explainable AI in Medicine Workshop, Lugano, Switzerland, 2-3 November 2023.

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