Bryan Alexander FordLinks
DEDIS lab home pagePersonal home page
Curriculum vitae https://bford.info/cv.pdf
Teaching & PhD PhD Students Shailesh Mishra Past EPFL PhD Students Eleftherios Kokoris Kogias, Kirill Nikitin, Cristina Basescu, Enis Ceyhun Alp, Simone Colombo, Pasindu Nivanthaka Tennage, Louis-Henri Manuel Jakob Merino Past EPFL PhD Students as codirector David Froelicher, Ludovic Barman, Georgia Fragkouli Courses Decentralized systems engineering CS-438 A decentralized system is one that works when n Bruno Emanuel Ferreira De Sousa CorreiaThroughout my PhD and postdoctoral studies I was trained in world-renowned laboratories and institutions in the United States of America (University of Washington and The Scripps Research Institute). Very early in my scientific career I found out my fascination about protein structure and function. My PhD studies evolved in the direction of immunogen design and vaccine engineering which sparked my interest in the many needs and opportunities in vaccinology and translational research. My efforts resulted in an enlightening piece of work where for the first time, computationally designed immunogens elicited potent neutralizing antibodies. During my postdoctoral studies I joined a chemical biology laboratory at the Scripps Research Institute. In this stage I developed novel chemoproteomics methods for the identification of protein-small molecule interaction sites in complex proteomes. In March 2015, I joined the École Polytechnique Fédérale de Lausanne (EPFL) – Switzerland as a tenure track assistant professor. The focus of my research group is to develop computational tools for protein design with particular emphasis in applying these strategies to immunoengineering (e.g. vaccine and cancer immunotherapy). The activities in my laboratory focus on computational design methods development and experimental characterization of the designed proteins. Our laboratory has been awarded with 2 prestigious research grants from the European Research Council. Lastly, I have been awarded the prize for best teacher of Life sciences in 2019.
Sara BonellaExpertise
Simulation and modelling of condensed phase systems, quantum and classical dynamics and statistical mechanics, adiabatic dynamics for classical polarizable models and first principles simulations, classical charge transport for systems in external magnetic fields.
Expertise
Simulation and modelling of condensed phase systems, quantum and classical dynamics and statistical mechanics, adiabatic dynamics for classical polarizable models and first principles simulations, classical charge transport for
Cécile HardebolleCurrent work
Responsible information systems (https://go.epfl.ch/digitalethics), ethical skills Jupyter notebooks for education (https://go.epfl.ch/notebooks), digital skills, computational thinking Transversal/professional skillsEngineering design skillsFlipped classrooms Project-based learning
Education PhD | Computer Science 2006 – 2008 Université Paris-Sud XI, France Directed by Prof. Guy Vidal-Naquet MSc | Electrical Engineering, major in Computer and Inf Maud EhrmannMaud 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.
Elison de Nazareth MatioliTeaching & PhD PhD Students Hongkeng Zhu, Yuan Zong, Utku Agtas, Ibrahim Osama Elhagali, Mohammad Rezaei, Zheng Hao, Amirhossein Esteghamat, Ivan Krsic, Luca Mazzone, Önder Soydal, Francesco Marcantoni, Hakan Cankat Gür Past EPFL PhD Students Jun Ma, Giovanni Santoruvo, Minghua Zhu, Riyaz Mohammed Abdul, Reza Soleiman Zadeh Ardebili, Armin Jafari, Catherine Erine, Taifang Wang, Nirmana Perera, Remco Franciscus Peter van Erp, Luca Nela, Mohammad Samizadeh Nikoo, Alessandro Floriduz Past EPFL PhD Students a
Maria BrbicMaria Brbic is an assistant professor in computer science at EPFL. Prior to joining EPFL, Maria was a postdoctoral researcher in Computer Science at Stanford University working with Jure Leskovec. She received her PhD degree from University of Zagreb in 2019, while also researching at Stanford University and University of Tokyo. Her research was awarded with the Fulbright Scholarship, L’Oreal UNESCO for Women in Science Scholarship, Branimir Jernej award for outstanding publication in biology and biomedicine, and Josip Loncar Silver Plaque award for the best doctoral dissertation. She has been named a Rising Star in EECS by MIT in 2021. Her research is focused on developing new machine learning methods and applying her methods to advance biomedical research.
Simon GuilloudExpertise
My main domains of research are proof assistants and algorithms for logic-related problems.I also have interests in various areas of computer science and mathematics and their applicability in proof systems such as probabilistic proofs, type theory, quantum computing and more.
Expertise
My main domains of research are proof assistants and algorithms for logic-related problems.I also have interests in various areas of computer science and mathematics and their applicability in proof systems such as
Johanni Michael BreaTeaching & PhD PhD Students Aude Maier, Lucas Louis Gruaz, Flavio Martinelli Courses Artificial intelligence and society HUM-286 This course integrates social sciences and humanities with engineering sciences, and introduces students to the social, technical and political issues surrounding artificial intelligence. Biological data science II : machine learning BIO-322 Students understand basic concepts and methods of machine learning. They can describe them in mathematical
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