|| School of Computer and Communication Sciences
|| Laboratory for Computational Biology and Bioinformatics
|| EPFL > IC > LCBB
Laboratory for Computational
Biology and Bioinformatics
The Laboratory was started in June 2006 by Professor Bernard Moret,
as he joined EPFL after working 26 years on the faculty at the University of New Mexico. Our main area
of interest has been the design of discrete models and algorithms for problems arising in biology,
mostly evolutionary biology (phylogenetics) and genomics (regulatory, comparative, and evolutionary genomics).
See also the 2011 ACM Ubiquity interview with Prof. Moret on Experimental Algorithmics,
our previous area of research, which led us to computational biology.
The Laboratory closed at end of 2016, as Prof. Moret retired (after 36.5 years as a faculty member).
To celebrate the achievements of lab members and collaborators, and to thank major sources of inspiration, a 2-day scientific meeting,
CLIMB (Colloquium on aLgorithms in Molecular Biology),
took place in Lausanne on Nov. 7-8, 2016, organized by lab members Daniel Doerr and Min Ye.
A second event, organized by Profs. Tandy Warnow (UIUC) and Satish Rao (UC Berkeley)
and focussed more on Professor Moret and his area of research, took place in Berkeley on June 2, 2017.
We regularly taught the Master's level courses Computational Molecular Biology
(5 European/3 US credits) and Advanced Algorithms
(7 European/5 US credits).
You can find class notes, homeworks and tests (both with solutions sheets), in subdirectories named algsxx (09 through 16, except for 15)
and compbioxx (07 through 16, except for 10, 13, and 15).
EPFL and its sister institution ETHZ are consistently ranked among Europe's top 5 (along with Cambridge, Oxford, and Imperial College) and among the world's top 30 in Computer Science; see, for instance,
Keep in mind that most of these rankings vary widely (almost "wildly") from year to year for mostly commercial reasons, tend to promote geographic diversity at the expense of quality, and, in some cases are also strongly biased in favor of institutions in their own region. The cumulative effect for EPFL, however, is clear.