Dean — Ivan Arzhantsev
First Deputy Dean— Tamara Voznesenskaya
Deputy Dean for Research and International Relations - Sergei Obiedkov
Deputy Dean for finance and administration - Irina Gergart
Phone: +7 (495) 772-95-90 * 12332
Moscow, 3 Kochnovsky Proezd (near metro station 'Aeroport').
The faculty trains developers and researchers. The programme has been created based on the experience of leading American and European universities, such as Stanford University (U.S.) and EPFL (Switzerland). Also taken into consideration when creating the faculty was the School of Data Analysis, which is one of the strongest postgraduate schools in the field of computer science in Russia. The wide range of elective courses will allow each student to create his or her own educational path. In the faculty, learning is based on practice and projects.
Vereshchagin N., Shen A.
Lecture Notes in Computer Science. 2017. Vol. 10010. P. 669-737.
M.N.Vyalyi, Lawrencenko S., Zgonnik L.
Australasian Journal of Combinatorics. 2017. Vol. 67. No. 2. P. 119-130.
Babin M. A., Kuznetsov S. O.
Theoretical Computer Science. 2017. Vol. Volume 658, Part B. No. 7 January. P. 316-326.
Fundamenta Informaticae. 2016. Vol. 147. No. 2-3. P. 315-336.
Scedrov A., Barthe G., Fagerholm E. et al.
IET Information Security. 2016.
For a researcher in a diverse and quickly developing area of knowledge such as computer science, it is important to maintain a broad perspective and strive to understand what colleagues in related fields are studying. This requires a platform where specialists can meet and tell each other about their latest findings in a common language. Such a platform is the Colloquium of HSE's Faculty of Computer Science. This platform is a faculty-wide academic seminar designed for teachers and research staff, graduate and undergraduate students, as well as those who are interested in computer science.
Colloquium meetings are held on Thursdays in the Faculty of Computer Science building at 3 Kochnovsky Proezd, lecture hall Descartes on floor 3.
NB: a somewhat more detailed web page is available in Russian here.
Registration for the Colloquium is open: firstname.lastname@example.org
In this talk I will give an overview of the computational steps in the analysis of a single cell-based large-scale microscopy experiments. First, I will present a novel microscopic image correction method designed to eliminate vignetting and uneven background effects which, left uncorrected, corrupt intensity-based measurements. New single-cell image segmentation methods will be presented using energy minimization methods. I will discuss the Advanced Cell Classifier (ACC) (www.cellclassifier.org), a machine learning software tool capable of identifying cellular phenotypes based on features extracted from the image. It provides an interface for a user to efficiently train machine learning methods to predict various phenotypes. For cases where discrete cell-based decisions are not suitable, we propose a method to use multi-parametric regression to analyze continuous biological phenomena. Finally, to improve the learning speed and accuracy, we recently developed an active learning scheme which selects the most informative cell samples.