Personnes associées (11)
Alexander Mathis
Alexander studied pure mathematics with a minor in logic and theory of science at the Ludwig Maximilians University in Munich. For his PhD also at LMU, he worked on optimal coding approaches to elucidate the properties of grid cells. As a postdoctoral fellow with Prof. Venkatesh N. Murthy at Harvard University and Prof. Matthias Bethge at Tuebingen AI, he decided to study olfactory behaviors such as odor-guided navigation, social behaviors and the cocktail party problem in mice. During this time, he increasingly got interested sensorimotor behaviors beyond olfaction and started working on proprioception, motor adaption, as well as computer vision tools for measuring animal behavior. In his group, he is interested in elucidating how the brain gives rise to adaptive behavior. One of the major goals is to synthesize large datasets into computationally useful information. For those purposes, he develops algorithms and systems to analyze animal behavior (e.g. DeepLabCut), neural data, as well as creates experimentally testable computational models.
Rolf Gruetter
Awards: 1999 Young Investigator Award Plenary Lectureship , International Society for Neurochemistry 2011 Fellow , ESMRMB 2011 Teaching Award , Section Sciences de la Vie, EPFL
Jean-Philippe Thiran
Jean-Philippe Thiran was born in Namur, Belgium, in August 1970. He received the Electrical Engineering degree and the PhD degree from the Université catholique de Louvain (UCL), Louvain-la-Neuve, Belgium, in 1993 and 1997, respectively. From 1993 to 1997, he was the co-ordinator of the medical image analysis group of the Communications and Remote Sensing Laboratory at UCL, mainly working on medical image analysis. Dr Jean-Philippe Thiran joined the Signal Processing Institute (ITS) of the Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland, in February 1998 as a senior lecturer. He was promoted to Assistant Professor in 2004, to Associate Professor in 2011 and is now a Full Professor since 2020. He also holds a 20% position at the Department of Radiology of the University of Lausanne (UNIL) and of the Lausanne University Hospital (CHUV) as Associate Professor ad personam.  Dr Thiran's current scientific interests include Computational medical imaging: acquisition, reconstruction and analysis of imaging data, with emphasis on regularized linear inverse problems (compressed sensing, convex optimization). Applications to medical imaging: diffusion MRI, ultrasound imaging, inverse planning in radiotherapy, etc.Computer vision & machine learning: image and video analysis, with application to facial expression recognition, eye tracking, lip reading, industrial inspection, medical image analysis, etc.
Lijing Xin
Lijing Xin is a research staff scientist and 7T MR Operational Manager at the Center for Biomedical Imaging (CIBM), Ecole polytechnique fédérale de Lausanne (EPFL), Switzerland. Her research interests focus on developing cutting-edge magnetic resonance spectroscopy and imaging methods for better understanding the brain function and the pathophysiology of neurological diseases. Her journey on magnetic resonance imaging (MRI) started from her master project during 2002-2005, where she developed a gradient unit with eddy current compensation and a pulse sequence generator for MRI spectrometer, which enhanced her knowledge in MR instrumentation. Later, she obtained her PhD in physics from Ecole polytechnique fédérale de Lausanne (EPFL) in 2010, where she focused on developing various novel 1H and 13C magnetic resonance spectroscopy (MRS) acquisition and quantification methods as well as RF coils on high field preclinical MR scanners. Afterwards, she started working on the clinical MR platforms including both 3 and 7T and continued to improve and develop novel acquisition and quantification methods for 1H, 13C and 31P nuclei. She carries on interdisciplinary collaborations with different partners, particularly with clinical partners where translational strategies are performed to explore the pathophysiology of psychiatric disorders and disease biomarkers for early diagnose and intervention.

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.