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Maud Ehrmann
Maud Ehrmann is a research scientist at EPFL’s Digital Humanities Laboratory lead by Prof.
Frédéric Kaplan
. She holds a PhD in Computational Linguistics from the Paris Diderot Universtiy (Paris 7) and has been engaged in a large number of scientific projects centred on information extraction and text analysis, both for present-time and historical documents. Her main research interests span Natural Language Processing and Digital Humanities and include, among others, historical text annotation, historical data processing and representation, named entity recognition, and multilingual linguistic resources creation.
Her current work at the DHLAB focuses on
‘impresso - Media Monitoring of the Past’
, a SNF sinergia project she initiated with
Marten Düring
(
C2DH
) and
Simon Clematide
(
ICL
) and which aims at enabling critical analysis of historical newspapers. In addition to the overall project management, her contributions to this project include system design and data management, annotation and benchmarking and named entity processing. Besides, she participates to the activities of the
Venice Time Machine
, working particularly on information extraction and knowledge representation tasks. Previously, she worked on the
Garzoni
project where she supervised and contributed to the development of a web-based transcription and annotation interface - in collaboration with Orlin Topalov, and built a linked data-based historical knowledge base. She also contributed to the
Le Temps Digital Archives project
.
Prior to joining the DHLAB, she worked at the
Linguistics Computing Laboratory
at the Sapienza University of Rome with Roberto Navigli, where she worked on the
BabelNet
resource - a very large multilingual encyclopaedic dictionary and semantic network - and contributed to the
LIDER
project. Before that, she has been working for four years at the European Commission’s Joint Research Centre in Ispra, Italy, as member of the OPTIMA unit (now
Text and Data mining
unit), which develops innovative and application-oriented solutions for retrieving and extracting information from the Internet with a focus on high multilinguality. Together with Erik van der Goot,
Ralf Steinberger
, Hristo Tanev, Leo della Rocca and many others, she contributed to the development of the
Europe Media Monitor
(EMM). Prior, she worked at the Xerox Europe Research Centre in Grenoble, France (now
Naver Labs Europe
) in the Parsing & Semantics group led by Frédérique Segond, first as PhD candidate supported through a CIFRE grant under the supervision of
Caroline Brun
and
Bernard Victorri
, then as a post-doctoral researcher. There her research focused mainly on the automatic processing and fine-grained analysis of entities of interest, specifically named entities and temporal expressions.
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.