{"id":98,"date":"2024-11-03T15:53:04","date_gmt":"2024-11-03T15:53:04","guid":{"rendered":"https:\/\/njaquier.ch\/?page_id=98"},"modified":"2026-05-10T16:01:28","modified_gmt":"2026-05-10T16:01:28","slug":"bio","status":"publish","type":"page","link":"https:\/\/njaquier.ch\/?page_id=98","title":{"rendered":"Bio"},"content":{"rendered":"\n

Biography<\/h2>\n\n\n\n
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No\u00e9mie Jaquier is an assistant professor at the KTH Royal Institute of Technology, where she heads the Geometric Robot Learning (GeoRob) Lab at the Division of Robotics, Perception and Learning. She received her PhD degree from the Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Switzerland in 2020. Prior to joining KTH, she was a postdoctoral researcher in the High Performance Humanoid Technologies Lab (H\u00b2T) at the Karlsruhe Institute of Technology (KIT) and a visiting postdoctoral scholar at the Stanford Robotics Lab. Her research investigates data-efficient and theoretically-sound learning algorithms that leverage differential geometry- and physics-based inductive bias to endow robots with close-to-human learning and adaptation capabilities. <\/p>\n\n\n\n

No\u00e9mie is the recipient of a WASP-AI\/MLX professorship and a starting grant from the Swedish research council. She received several awards, notably the Best Presentation Award at CoRL\u201919, Best Paper Award Finalists (IROS\u201923, ICRA\u201924), the Hector-Stiftung Preis 2024 from the Heidelberg Academy of Sciences, and AI newcomer of Technical and Engineering Sciences 2023 by the German Federal Ministry of Education and Research. <\/p>\n<\/div>\n\n\n\n

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Photo credits: Heidelberger Akademie der Wissenschaften<\/figcaption><\/figure>\n<\/div><\/div>\n<\/div>\n\n\n\n

Education and Employment<\/h2>\n\n\n
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\n 2024 –\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Assistant professor at the Division of Robotics, Perception and Learning<\/a>, KTH Royal Institute of Technology\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2020 – 2024\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Postdoctoral Researcher at the High Performance Humanoid Technology (H2<\/sup>T)<\/a> group, Karlsruhe Institute of Technology<\/a> (KIT)\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 09\/2023 – 02\/2024\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Visiting Postdoctoral Scholar at the Stanford Robotics Lab<\/a>, Stanford University\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2016 – 2020\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n D.Sc , Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne<\/a> (EPFL) and Idiap Research Institute<\/a>
PhD thesis: “Robot Skills Learning with Riemannian Manifolds: Leveraging Geometry-awareness in Robot Learning, Optimization and Control<\/a>”\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 04\/2019 – 09\/2019\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n PhD Sabbatical, Bosch Center for Artificial Intelligence<\/a> (BCAI)\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2014 – 2016\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n M.Sc. in Robotics and autonomous systems with minor in Computational Neurosciences, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne<\/a> (EPFL)\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2011 – 2014\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n B.Sc. in Microengineering, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne<\/a> (EPFL)\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n \n <\/figure>\n\n\n

Some highlights…<\/h2>\n\n\n
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\n 2024\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Hector-Stiftung Preis<\/a>, awarded by the Heidelberger Akademie der Wissenschaften<\/a> and sponsored by the Hector Foundation\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2024\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Finalist for the IEEE ICRA Best Paper Award on Human-Robot Interaction<\/a> for our paper “Incremental Learning of Full-Pose Via-Point Movement Primitives on Riemannian Manifolds<\/a><\/em>”\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2023\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n AI newcomer 2023<\/a> of Technical and Engineering Science, awarded by The Federal Ministry of Education and Research (BMBF) and German Informatics Society (GI) \n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2023\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Finalist for the IROS Best Paper Award on Mobile Manipulation sponsored by OMRON Sinic X Corp<\/a> for our paper “On the Design of Region-Avoiding Metrics for Collision-Safe Motion Generation on Riemannian Manifolds<\/em><\/a>”\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2020\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n PhD thesis nominated for the EPFL Asea Brown Boveri Ltd. Award<\/a>\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2019\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Idiap PhD Student award<\/a>\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2019\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Best Presentation award at CoRL’19 for our paper “Bayesian Optimization Meets Riemannian Manifolds in Robot Learning<\/em><\/a>”\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2018\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Robotics: Science and Systems (R:SS) 2018 Pioneer<\/a>\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n \n <\/figure>\n\n\n

Co-organized events<\/h2>\n\n\n
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\n 2024\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n ICRA’24 \u2014 Riemann and Gauss meet Asimov: 2nd tutorial on Geometric Methods in Robot Learning, Optimization, and Control<\/a>\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2023\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n IROS’23 \u2014 Workshop on Assistive Robotics for Citizens<\/a>\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2022\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n IROS’22 \u2014 Riemann and Gauss meet Asimov: A tutorial on Geometric Methods in Robot Learning, Optimization, and Control<\/a>\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2021\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n R:SS’21 \u2014 Workshop on Geometry and Topology in Robotics: Learning, Optimization, Planning, and Control<\/a>\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n 2020\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n IROS’20 \u2014 Workshop on Bringing Geometric Methods to Robot Learning, Optimization and Control<\/a>\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n \n <\/figure>\n\n\n

Teaching<\/h2>\n\n\n
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\n WS 22\/23\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Riemannian methods for learning in robotics, KIT\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n \n \n

\n WS 21\/22\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td>\n \n \n

\n Riemannian methods for learning in robotics, KIT\n <\/p>\n <\/div>\n <\/div>\n \n \n <\/td><\/tr>\n <\/tbody>\n <\/table>\n <\/div>\n \n <\/figure>","protected":false},"excerpt":{"rendered":"

Biography No\u00e9mie Jaquier is an assistant professor at the KTH Royal Institute of Technology, where she heads the Geometric Robot Learning (GeoRob) Lab at the Division of Robotics, Perception and Learning. She received her PhD degree from the Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL), Switzerland in 2020. Prior to joining KTH, she was a postdoctoral researcher … <\/p>\n

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