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

• 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

• Bolck, H., Vollenweider, J., Merkli, F., Barden, A., Jajcay, M., Trempeck, P., Rafailović, B., Fraefel, R., Lenhart, P. M., Chavarriaga, R., Renold, M., Bogojeska, J., Stadelmann, T., & Guillaume, M. (2026). LINA’s testing infrastructure enables AI to take-off in unmanned aerial vehicles (UAVs). Frontiers in Robotics and AI, 13(1764248). https://doi.org/10.3389/frobt.2026.1764248
• Kölbl, N., Rampp, S., Kaltenhäuser, M., Tziridis, K., Maier, A., Kinfe, T., Chavarriaga, R., Krauss, P., & Schilling, A. (2026). Prediction, syntax and semantic grounding in the brain and large language models. Scientific Reports, 16(1), 8728. https://doi.org/10.1038/s41598-026-41532-0
• Bolck, H., Vollenweider, J., Merkli, F., Barden, A., Jajcay, M., Trempeck, P., Rafailović, B., Lenhart, P. M., Chavarriaga, R., Renold, M., Bogojeska, J., Stadelmann, T., & Guillaume, M. (2026). Perspective: LINA’s testing infrastructure enables AI to take-off in unmanned aerial vehicles (UAVs). Frontiers in Robotics and AI. https://doi.org/10.21256/zhaw-35722
• Sultana, M., Matran-Fernandez, A., Halder, S., Nawaz, R., Jain, O., Scherer, R., Chavarriaga, R., Millán, J. d. R., & Perdikis, S. (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). https://doi.org/10.1088/1741-2552/ae2e8a
• Eke, D., Chavarriaga, R., & Stahl, B. (2025). Decoloniality impact assessment for AI. AI & Society. https://doi.org/10.1007/s00146-025-02649-4
• Mussi, M., Metelli, A. M., Restelli, M., Losapio, G., Bessa, R. J., Boos, D., Borst, C., Leto, G., Castagna, A., Chavarriaga, R., Dias, D., Egli, A., Eisenegger, A., El Manyari, Y., Fuxjäger, A., Geraldes, J., Hamouche, S., Hassouna, M., Lemetayer, B., et al. (2025). Human-AI interaction in safety-critical network infrastructures. iScience, 28(9), 113400. https://doi.org/10.1016/j.isci.2025.113400
• Carlson, D. E., Chavarriaga, R., Liu, Y., Lotte, F., & Lu, B.-L. (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), 21002. https://doi.org/10.1088/1741-2552/adbfbd
• Starke, G., Gille, F., Termine, A., Aquino, Y. S. J., Chavarriaga, R., Ferrario, A., Hastings, J., Jongsma, K., Kellmeyer, P., Kulynych, B., Postan, E., Racine, E., Sahin, D., Tomaszewska, P., Vold, K., Webb, J., Facchini, A., & Ienca, M. (2025). Finding consensus on trust in AI in health care : recommendations from a panel of international experts. Journal of Medical Internet Research, 27, e56306. https://doi.org/10.2196/56306
• Iwane, F., Porssut, T., Blanke, O., Chavarriaga, R., Millan, J. D. R., Herbelin, B., & Boulic, R. (2024). Customizing the human-avatar mapping based on EEG error related potentials during avatar-based interaction. Journal of Neural Engineering, 21(2), 26016. https://doi.org/10.1088/1741-2552/ad2c02
• Iwane, F., Sobolewski, A., Chavarriaga, R., & Millán, J. d. R. (2023). EEG error-related potentials encode magnitude of errors and individual perceptual thresholds. iScience, 26(9), 107524. https://doi.org/10.1016/j.isci.2023.107524
• Meng, L., Jiang, X., Huang, J., Zeng, Z., Yu, S., Jung, T.-P., Lin, C.-T., Chavarriaga, R., & Wu, D. (2023). EEG-based brain-computer interfaces are vulnerable to backdoor attacks. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 2224–2234. https://doi.org/10.1109/TNSRE.2023.3273214
• Porssut, T., Iwane, F., Chavarriaga, R., Blanke, O., Millán, J. D. R., Boulic, R., & Herbelin, B. (2023). EEG signature of breaks in embodiment in VR. Plos One, 18(5), e0282967. https://doi.org/10.1371/journal.pone.0282967
• Dell’Agnola, F., Jao, P.-K., Arza, A., Chavarriaga, R., Millan, J. D. R., Floreano, D., & Atienza, D. (2022). Machine-learning based monitoring of cognitive workload in rescue missions with drones. IEEE Journal of Biomedical and Health Informatics, 26(9), 4751–4762. https://doi.org/10.1109/JBHI.2022.3186625
• Ienca, M., Fins, J. J., Jox, R. J., Jotterand, F., Voeneky, S., Andorno, R., Ball, T., Castelluccia, C., Chavarriaga, R., Chneiweiss, H., Ferretti, A., Friedrich, O., Hurst, S., Merkel, G., Molnár-Gábor, F., Rickli, J.-M., Scheibner, J., Vayena, E., Yuste, R., & Kellmeyer, P. (2022). Towards a governance framework for brain data. Neuroethics, 15(2), 20. https://doi.org/10.1007/s12152-022-09498-8
• Huggins, J. E., Krusienski, D., Vansteensel, M. J., Valeriani, D., Thelen, A., Stavisky, S., Norton, J. J. S., Nijholt, A., Müller-Putz, G., Kosmyna, N., Korczowski, L., Kapeller, C., Herff, C., Halder, S., Guger, C., Grosse-Wentrup, M., Gaunt, R., Dusang, A. N., Clisson, P., et al. (2022). Workshops of the eighth international brain-computer interface meeting : BCIs: the next frontier. Brain-Computer Interfaces, 9(2), 69–101. https://doi.org/10.1080/2326263X.2021.2009654
• Chavarriaga, R., Carey, C., Contreras-Vidal, J. L., McKinney, Z., & Bianchi, L. (2021). Standardization of neurotechnology for brain-machine interfacing : state of the art and recommendations. IEEE Open Journal of Engineering in Medicine and Biology, 2, 71–73. https://doi.org/10.1109/OJEMB.2021.3061328
• Aydarkhanov, R., Ušćumlić, M., Chavarriaga, R., Gheorghe, L., & Millán, J. d. R. (2021). Closed-loop EEG study on visual recognition during driving. Journal of Neural Engineering, 18(2), 26010. https://doi.org/10.1088/1741-2552/abdfb2
• Jao, P.-K., Chavarriaga, R., & Millan, J. d. R. (2021). EEG-based online regulation of difficulty in simulated flying. IEEE Transactions on Affective Computing, 14(1), 394–405. https://doi.org/10.1109/TAFFC.2021.3059688
• Iwane, F., Iturrate, I., Chavarriaga, R., & Millán, J. d. R. (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), 46044. https://doi.org/10.1088/1741-2552/abfa70
• Jao, P.-K., Chavarriaga, R., Dell’Agnola, F., Arza, A., Atienza, D., & Millan, J. d. R. (2021). EEG correlates of difficulty levels in dynamical transitions of simulated flying and mapping tasks. IEEE Transactions on Human-Machine Systems, 51(2), 99–108. https://doi.org/10.1109/THMS.2020.3038339
• Batzianoulis, I., Iwane, F., Wei, S., Correia, C. G. P. R., Chavarriaga, R., Millán, J. d. R., & Billard, A. (2021). Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials. Communications Biology, 4(1406). https://doi.org/10.1038/s42003-021-02891-8
• Eke, D. O., Bernard, A., Bjaalie, J. G., Chavarriaga, R., Hanakawa, T., Hannan, A. J., Hill, S. L., Martone, M. E., McMahon, A., Ruebel, O., Crook, S., Thiels, E., & Pestilli, F. (2021). International data governance for neuroscience. Neuron, 110(4), 600–612. https://doi.org/10.1016/j.neuron.2021.11.017
• Delaux, A., Saint Aubert, J.-B., Ramanoël, S., Bécu, M., Gehrke, L., Klug, M., Chavarriaga, R., Sahel, J.-A., Gramann, K., & Arleo, A. (2021). Mobile brain/body imaging of landmark‐based navigation with high‐density EEG. European Journal of Neuroscience, 54(12), 8256–8282. https://doi.org/10.1111/ejn.15190
• Bontempi, G., Chavarriaga, R., De Canck, H., Girardi, E., Hoos, H., Kilbane-Dawe, I., Ball, T., Nowé, A., Sousa, J., Bacciu, D., Aldinucci, M., De Domenico, M., Saffiotti, A., & Maratea, M. (2021). The CLAIRE COVID-19 initiative : approach, experiences and recommendations. Ethics and Information Technology, 23(Suppl 1), S127–S133. https://doi.org/10.1007/s10676-020-09567-7
• Aydarkhanov, R., Ušćumlić, M., Chavarriaga, R., Gheorghe, L., & del R Millán, J. (2020). Spatial covariance improves BCI performance for late ERPs components with high temporal variability. Journal of Neural Engineering, 17(3), 36030. https://doi.org/10.1088/1741-2552/ab95eb
• Orset, B., Lee, K., Chavarriaga, R., & Millan, J. d. R. (2020). User adaptation to closed-loop decoding of motor imagery termination. IEEE Transactions on Biomedical Engineering, 68(1), 3–10. https://doi.org/10.1109/TBME.2020.3001981
• Jeunet, C., Tonin, L., Albert, L., Chavarriaga, R., Bideau, B., Argelaguet, F., Millán, J. d. R., Lécuyer, A., & Kulpa, R. (2020). Uncovering EEG correlates of covert attention in soccer goalkeepers : towards innovative sport training procedures. Scientific Reports, 10(1705). https://doi.org/10.1038/s41598-020-58533-2
• Zhang, X., Wu, D., Ding, L., Luo, H., Lin, C.-T., Jung, T.-P., & Chavarriaga, R. (2020). Tiny noise, big mistakes : adversarial perturbations induce errors in brain-computer interface spellers. National Science Review. https://doi.org/10.1093/nsr/nwaa233

Buchbeiträge, peer-reviewed

• Bessa, R. J., Leyli-Abadi, M., Yagoubi, M., Boos, D., Borst, C., Castagna, A., Chavarriaga, R., Dias, D., Egli, A., Eisenegger, A., Ellerbroek, J., Fedorova, A., Felix, C., Fuxjäger, A., Geraldes, J., Hamouche, S., Hassouna, M., Kop, S., Lemetayer, B., et al. (2026). Toward a holistic framework for human-AI collaboration in safety-critical systems. In E. Curry, P. Piatkiewicz, F. Heintz, H. Vornhagen, A. N. Belbachir, E. Girardi, M. Schoenauer, & J. Röning (Eds.), Artificial intelligence, data and robotics : foundations, transformations and future directions (pp. 343–402). Springer. https://doi.org/10.1007/978-3-032-10561-5_13
• McKinney, Z., de Neeling, M., Bianchi, L., & Chavarriaga, R. (2023). Integrating innovation : the role of standards in promoting responsible development of human–machine systems. In G. Fortino, D. Kaber, A. Nürnberger, & D. Mendonça (Eds.), Handbook of Human‐Machine Systems (pp. 431–449). Wiley. https://doi.org/10.1002/9781119863663.ch35
• Iturrate, I., Chavarriaga, R., & Millán, J. d. R. (2020). General principles of machine learning for brain-computer interfacing. In J. d. R. Millan & N. F. Ramsay (Eds.), Handbook of Clinical Neurology ; 168 (pp. 311–328). Elsevier. https://doi.org/10.1016/B978-0-444-63934-9.00023-8

Schriftliche Konferenzbeiträge, peer-reviewed

• Fedorova, A., Stefani, W., Heitz, C., & Chavarriaga, R. (2025, September 11). Continuous assessment-driven requirement elicitation for trustworthy AI systems. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Porto, Portugal, 15-19 September 2025. https://doi.org/10.5281/zenodo.17099001
• Leyli-abadi, M., Bessa, R. J., Viebahn, J., Boos, D., Borst, C., Castagna, A., Chavarriaga, R., Hassouna, M., Lemetayer, B., Leto, G., Marot, A., Meddeb, M., Meyer, M., Schiaffonati, V., Schneider, M., & Waefler, T. (2025). A conceptual framework for AI-based decision systems in critical infrastructures [Conference paper]. 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 5799–5806. https://doi.org/10.1109/SMC58881.2025.11342626
• de Neeling, M. G. J., Hill, K., Chavarriaga, R., Huggins, J. E., Bianchi, L., Porter, A., & Vansteensel, M. (2025, June). Reporting checklist for observational implanted and non-implanted neural interface studies : protocol for a Delphi process. 11th International Brain-Computer Interface (BCI) Meeting, Banff, Canada, 2 -5 June 2025.
• Weiss, M., & Chavarriaga, R. (2025, June). Transformative and generative data augmentation for EEG-based BCIs. 11th International Brain-Computer Interface (BCI) Meeting, Banff, Canada, 2 -5 June 2025.
• Frischknecht-Gruber, C., Denzel, P., Reif, M., Billeter, Y., Brunner, S., Forster, O., Schilling, F.-P., Weng, J., & Chavarriaga, R. (2025). AI assessment in practice : implementing a certification scheme for AI trustworthiness [Conference paper]. In R. Görge, E. Haedecke, M. Poretschkin, & A. Schmitz (Eds.), Symposium on Scaling AI Assessments (SAIA 2024) (pp. 15:1–15:18). Schloss Dagstuhl – Leibniz-Zentrum für Informatik. https://doi.org/10.4230/OASIcs.SAIA.2024.15
• Sultana, M., Jain, O., Halder, S., Matran-Fernandez, A., Nawaz, R., Scherer, R., Chavarriaga, R., del R. Millán, J., & Perdikis, S. (2024). Evaluating dry EEG technology out of the lab [Conference paper]. 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 752–757. https://doi.org/10.1109/metroxraine62247.2024.10797021
• Billeter, Y., Denzel, P., Chavarriaga, R., Forster, O., Schilling, F.-P., Brunner, S., Frischknecht-Gruber, C., Reif, M. U., & Weng, J. (2024). MLOps as enabler of trustworthy AI [Conference paper]. 2024 11th IEEE Swiss Conference on Data Science (SDS), 37–40. https://doi.org/10.1109/SDS60720.2024.00013
• Denzel, P., Brunner, S., Billeter, Y., Forster, O., Frischknecht-Gruber, C., Reif, M. U., Schilling, F.-P., Weng, J., Chavarriaga, R., Amini, A., Repetto, M., & Iranfar, A. (2024). Towards the certification of AI-based systems [Conference paper]. 2024 11th IEEE Swiss Conference on Data Science (SDS), 84–91. https://doi.org/10.1109/SDS60720.2024.00020
• Lone, O., Weiss, M., Baumgartner, D., & Chavarriaga, R. (2024). A Python-based open software for EEG-based brain-machine interfaces [Conference poster]. Annual Meeting of the Swiss Society for Biomedical Engineering SSBE Abstract Book, 26. https://doi.org/10.21256/zhaw-31394
• Weng, J., Denzel, P., Reif, M. U., Schilling, F.-P., Billeter, Y., Frischknecht-Gruber, C., Brunner, S., Chavarriaga, R., Repetto, M., & Iranfar, A. (2024, June). Certification scheme for artificial intelligence based systems. 34th European Safety and Reliability Conference (ESREL), Cracow, Poland, 23-27 June 2024. https://doi.org/10.21256/zhaw-30549
• Simmler, N., Sager, P., Andermatt, P., Chavarriaga, R., Schilling, F.-P., Rosenthal, M., & Stadelmann, T. (2021). A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications [Conference paper]. Proceedings of the 8th SDS, 26–31. https://doi.org/10.1109/SDS51136.2021.00012
• Amirian, M., Tuggener, L., Chavarriaga, R., Satyawan, Y. P., Schilling, F.-P., Schwenker, F., & Stadelmann, T. (2021, March). Two to trust : AutoML for safe modelling and interpretable deep learning for robustness. Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020. https://doi.org/10.21256/zhaw-22061

Weitere Publikationen

• Specht, B., Garbaya, S., Ermis, O., Schneider, R., Chavarriaga, R., Khadraoui, D., & Tayeb, Z. (2026). PrivateBoost : privacy-preserving federated gradient boosting for cross-device medical data. medRxiv. https://doi.org/10.64898/2026.02.10.26345891
• Frischknecht-Gruber, C., Denzel, P., Forster, O., Billeter, Y., Iranfar, A., Repetto, M., Reif, M. U., Schilling, F.-P., Weng, J., & Chavarriaga, R. (2025, January 28). Assessment tool for trustworthy AI systems : operational workflows for compliance assessment with regulatory requirements. AI Days @ HES-SO, Geneva and Lausanne, Switzerland, 27–29 January 2025. https://doi.org/10.21256/zhaw-32422
• Chavarriaga, R., Rickli, J.-M., & Mantellassi, F. (2023). Neurotechnologies : the new frontier for international governance. Geneva Centre for Security Policy. https://doi.org/10.21256/zhaw-28985
• Weng, J., Reif, M., Chavarriaga, R., & Schilling, F.-P. (2023, January 11). certAInty : a certification scheme for AI systems (Innosuisse project). Datalab Symposium, Winterthur, Schweiz, 11. Januar 2023. https://doi.org/10.21256/zhaw-27261
• Orset, B., Lee, K., Chavarriaga, R., & Millán, J. del. R. (2021). Stopping vs Resting state during motor imagery paradigm. bioRxiv. https://doi.org/10.1101/2021.06.15.448360
• Perdikis, S., Leeb, R., Chavarriaga, R., & Millán, J. d. R. (2020). Context-aware learning for generative models. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2020.3011671
• Chavarriaga, R. (2020). Standards for neurotechnologies and brain-machine interfacing. IEEE Systems, Man, and Cybernetics Magazine, 6(3), 50–51. https://doi.org/10.1109/MSMC.2020.2995438

Mündliche Konferenzbeiträge und Abstracts

• Pestilli, F., Ray, K., Collier, M., Yi, A., Hershman, S. G., Troval-Moll, F., Ihunwo, A. O., Eke, D., Chavarriaga, R., & Nichols, T. E. (2025). Bridging the gap in data sharing : developing international data governance frameworks for brain health research [Conference presentation]. Neuroscience, 580, 42. https://doi.org/10.1016/j.neuroscience.2025.05.155
• Denzel, P., Brunner, S., Luley, P.-P., Frischknecht-Gruber, C., Reif, M. U., Schilling, F.-P., Amini, A., Repetto, M., Iranfar, A., Weng, J., & Chavarriaga, R. (2023, November 2). A framework for assessing and certifying explainability of health-oriented AI systems. Explainable AI in Medicine Workshop, Lugano, Switzerland, 2-3 November 2023.

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