SV seminars

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Conférence AMAM 2019

La recherche sur la manière dont les animaux et les humains excellent dans les mouvements adaptatifs, y compris la locomotion, peut aider les ingénieurs à améliorer les capacités d'adaptation des robots. En contrepartie, les robots peuvent être utilisés en tant qu'outils scientifiques pour explorer les principes de base des systèmes biologiques, en particulier les mécanismes neuromécaniques sous-tendant à leurs fascinantes capacités de locomotion. AMAM 2019 est la 9ème conférence internationale visant à créer des interactions fructueuses entre biologistes et ingénieurs intéressés par le mouvement adaptatif. Elle vise à réunir des chercheurs en robotique, biomécanique, neurosciences, sciences du sport et autres domaines liés au comportement dans les systèmes biologiques et artificiels.
Les collaborateurs de l'EPFL peuvent s'inscrire à la conférence (y compris les repas de midi et pauses café, mais pas le banquet) au prix réduit de 350 CHF (veuillez utiliser votre adresse courriel EPFL pour l'enregistrement). Détails sous https://amam2019.epfl.ch/register.php.

By: several internationally recognized experts

Carsten Eickhoff

Clinical Natural Language Processing

Clinical research has never been more active and diverse than it is at this moment. Research efforts span national and cultural borders and broad online dissemination of results makes insights available at a global scale with ever decreasing latency. In the face of these developments, individual researchers and practitioners are confronted with a seemingly intractable amount of material (approximately 1 Million scholarly articles are newly published in the life sciences each year). While highly trained human experts excel at making precision diagnoses, coverage, especially for uncommon conditions can be greatly improved. In this talk, we will discuss a range of (deep) machine learning techniques that provide automatic clinical decision support on the basis of large-scale data collections. I will present early and ongoing work on a) Predictive assistants in post-operative care of cardiac surgery patients, that serve as early warning systems in case of undesirable and dangerous complications. b) Automatic summarization of individually long patient records to obtain concise and topically targeted summaries for physicians. c) Data-driven diagnosis of rare diseases that individually occur too infrequently to allow clinical specialists to establish the necessary routine and experience.

Biography
Carsten is an assistant professor of medical and computer science at Brown University where he leads the Biomedical AI Lab, specializing in the development of data science and information retrieval techniques with the goal of improving patient safety, individual health and quality of medical care. Before coming to Brown, he studied artificial intelligence and machine learning at the University of Edinburgh, TU Delft and ETH Zurich. Carsten has authored more than 80 conference and journal articles on topics pertaining to automatic large-scale text processing and retrieval as well as information extraction from unstructured natural language resources. Aside from his academic endeavors, he is involved in several deep technology startups in the health sector that strive to translate technological innovation to improved safety and quality of life for patients.

(For your own information, the talk will be recorded and the webcast availlable few days after the talk presentation. Please, then check the following page for the webcast:  https://www.idiap.ch/en/talks/)

By: Assistant Professor Carsten Eickhoff from the Brown University