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Regular version of the site
Language Proficiency
English
Contacts
Phone:
(495)772-95-90
22668
E-mail:
Address: Moscow, Pokrovsky Boulevard, 11, Room S901
Timetable
Download CV
ORCID: 0000-0001-8110-7253
ResearcherID: A-3676-2008
Scopus AuthorID: 12242912100
Google Scholar
Office hours
10-17
Supervisor
S. Kuznetsov
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Attila Kertesz-Farkas

  • Attila Kertesz-Farkas has been at HSE University since 2015.

Education and Degrees

  • 2010

    PhD
    University of Szeged

  • 2004

    Master's in Computer Science
    University of Szeged

Professional Interests

Student Term / Thesis Papers

Full list of of student term / thesis papers

Courses (2019/2020)

Courses (2018/2019)

Research Seminar (Master’s programme; Faculty of Computer Science; 1 year, 1-4 module)Rus

Courses (2017/2018)

Scientific Seminar "Biomedical Data Analysis" (Master’s programme; Faculty of Computer Science; 1 year, 1-4 module)Rus

Courses (2016/2017)

Scientific Seminar "Biomedical Data Analysis" (Master’s programme; Faculty of Computer Science; 1 year, 1-4 module)Rus

Courses (2015/2016)

Scientific Seminar ''Intelligent Systems and Structural Analysis'' (Master’s programme; Faculty of Computer Science; spec. "Интеллектуальные системы и структурный анализ "; 2 year, 1-4 module)Rus

Publications22

Research and course projects (BSc, MSc, PhD) on reasoning with neural differentiable machines

Question Answering (QA) and Machine Reasoning (MR) have become a crucial application problem in evaluating the progress of AI systems in the realm of natural language processing and understanding, and to measure the progress of machine intelligence in general. However, most of the advances have focused on “shallow” QA tasks that can be tackled very effectively by existing retrieval-based techniques. Deep learning based methods achieve a human like performance on benchmark datasets; however, it is suspected that these methods merely learn to match answers to questions or focus attention on specific words and pieces of text and they do not perform real reasoning or cognition.

There are several research projects related to this topic on BSc, MSc, and PhD levels ranging from testing a scrutinizing recent methods to developing your own method for machine reasoning using augmented machines (RNNs with external memory which is learnt to be used by data, also known as neural Turing machines).

All projects are conducted in English, so it’s a good opportunity to improve your communication & presentation skills in English. All projects involve programming.


Research and course projects (BSc, MSc, PhD) on learning for mass spectrometry data identification (Bioinformatics)

Mass spectrometry has become the de facto method to identify molecules in complex mixtures (e.g. blood, cell, food) in many areas. A mass spectrometer analyses a complex mixture of biological or chemical samples and produces tens of thousands of spectra from the input sample. These spectrum data can be considered as fingerprints of the samples and the main computational challenge is to identify the original materials (reverse engineering). Mass spectrometry is used in e.g.: (a) Proteomics to identify proteins in biological samples (e.g. blood), (b) Clinical applications to identify proteins related to cancer or other diseases, (c) Pharmaceutical analysis to determine the effects of new drugs, (d) Environmental contamination analysis to ensure that the air, drinking water, soils, and food are safe to consume and does not contain pollution, heavy metals, hormone, pesticides, and herbicides,  (e) Forensic analysis to trace of evidence in arson investigation, drug abuse, and (f) Metabolomics to identify small molecules used by bacteria for communication in microbiome, etc.

There are several research projects available on BSc, Msc, and PhD levels, which focus on development of deep and machine learning methods to identify mass spectrometry data. The student will obtain a good understanding of the aspects and the challenges of computational mass spectrometry and deep learning with non-human readable data.

All projects are conducted in English, so it’s a good opportunity to improve your communication & presentation skills in English. All projects involve programming.

Timetable for today

Full timetable

A PhD student from the HSE Faculty of Computer Science visited Broad Institute of MIT and Harvard

Nikita Moshkov told us about his experience in Broad Institute of MIT and Harvard, where he stayed from 17 March till May 21.

HSE and University of London: Joint BA Programme in Applied Data Analysis

In 2018, the Higher School of Economics will launch an English-taught double degree programme in partnership with the University of London in Applied Data Analysis. Graduates will be awarded an undergraduate degree from HSE in Applied Mathematics and Information Science and a Bachelor of Science in Data Science and Business Analytics from the University of London. International applicants are invited to apply online starting November 15, 2017.

International Experts in the Faculty of Computer Science

An important step in integrating the university into the global educational, scientific and research space is the expansion of international recruiting. Since its very first year, the Faculty of Computer Science at the Higher School of Economics has had a foreign professor working on staff. In 2015, four internationally recruited experts teach and conduct research in the faculty.

International Experts in the Faculty of Computer Science 

An important step in integrating the university into the global educational, scientific and research space is the expansion of international recruiting. Since its very first year, the Faculty of Computer Science at the Higher School of Economics has had a foreign professor working on staff. In 2015, four internationally recruited experts teach and conduct research in the faculty.