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Regular version of the site
Language Proficiency
English
Contacts
Phone:
22668
E-mail:
Address: 11 Pokrovsky Bulvar, Pokrovka Complex, room S941
Timetable
ORCID: 0000-0001-8110-7253
ResearcherID: A-3676-2008
Scopus AuthorID: 12242912100
Google Scholar
10-17
Supervisor
S. Kuznetsov
Assistant
E. Burova
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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

Calendar

Academic Supervision

for a degree of Candidate of Science
1

Academic supervision of PhD students

P. Sulimov Learning generative probabilistic models for mass spectrometry data identification, 20202020-09-09 
R. Chereshnev Human gait controlling system using machine learning methods suitable for robotic prostheses for patients suffering from double transfemoral amputation, 20192019-10-10 
N. Moshkov A fully automated self-learning system to discover cell-to-cell(-to-cell) communication and to phenotype neurons using multi-patch clamping (aspirantura: 4th year of study)2017-09-01 

Student Term / Thesis Papers

Full list of of student term / thesis papers

Courses (2021/2022)

Courses (2020/2021)

Courses (2019/2020)

Courses (2018/2019)

Courses (2017/2018)

Publications36

Conferences

  • 2100
    Proteomics-2017 (Valencia). Presentation: High-dimensional generative probabilistic models for peptide-spectrum-matching in tandem mass spectrometry
  • Proteomics-2017 (Valencia). Presentation: PTMTreeSearch: a new algorithm for post-translational modification identification in tandem mass spectrometry data
  • Proteomics-2017 (Valencia). Presentation: Cascaded false discovery rate control tandem mass spectrometry (MS/MS) data for peptide identification
  • 2019
    Biotechnology: state and prospects of development (Moscow). Presentation: Generative probabilistic modelling of peptide-spectrum matching in tandem mass spectrometry
  • Biotechnology: state and prospects of development (Moscow). Presentation: Bias in false discovery rate estimation in mass-spectrometry-based peptide identification
  • Biotechnology: state and prospects of development (Moscow). Presentation: Filtering of tandem mass spectrometry data using convolutional neural networks
  • 2018
    The 7th International Conference on Analysis of Images, Social Networks, and Texts (AIST'2018) (Москва). Presentation: Lookup Lateration: Non-linear Received Signal Strength to Distance Mapping for Non-Line-of-Sight Geo-localization in Outdoor Urban Areas
  • 2017
    Analysis of Images, Social Networks and Texts. 6th International Conference, AIST 2017 (Moscow). Presentation: HuGaDB: Database for Human Gait Analysis from Wearable Inertial Sensor Networks
  • 2016
    The 3rd Professor Day (Moskva). Presentation: Large-scale localization method for urban area
  • The 5th international conference on Analysis of Images, Social Networks, and Texts (AIST) (Екатеринбург). Presentation: False discovery rate control for database search methods over heterogeneous biological data
  • 2014
    US HUPO (Seattle). Presentation: Peptide identification in tandem mass spectrometry data via cascade search

Research and course projects (BSc, MSc, PhD, postdoc) 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 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, PhD and postdoc 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 learned 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, postdoc) 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, PhD, and postdoc levels, which focus on development of deep and machine learning methods to identify mass spectrometry data. You 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.


Employment history

 

2021-present Laboratory Head, Laboratory on AI for Computational Biology, HSE University, Moscow, Russia,

2015-present Assistant Professor (Docent), HSE University, Moscow, Russia

2013-2015 Postdoctoral Fellow, Bill Noble's Lab University of Washington, Seattle WA, USA

2009-2013 Postdoctoral Fellow, Bioinformatics Group, International Centre of Genetic Engineering and Biotechnology (ICGEB), Trieste, Italy

2008-2009 Research Fellow, Division of Imaging and Applied Mathematics, CDRH, U.S. Food and Drug Administration (U.S. FDA), Silver Spring MD, USA. Joint affiliation with Department of Biology, University of Maryland Baltimore County (UMBC), Catonsville MD, USA

2004-2008 Ph.D. Student, University of Szeged, Hungary

2000-2004 Undergraduate Research Assistant, Research Group on Artificial Intelligence, Hungarian Academy of Sciences, Szeged, Hungary

 

Timetable for today

Full timetable

Congratulations to the Head of Laboratory on AI for Computational Biology Kertesz-Farkas Attila on receiving the well-deserved award

'The Efforts Taken by HSE University to Make My Internship Format Online Are Laudable'

John Hopkins graduate Kayode Ahmed is interning at the Faculty. We talked to him about his career path, internship project, and hobbies.

Attila Kertesz-Farkas about New Lab and Research

Laboratory on AI for Computational Biology has opened at the Faculty not so long ago. We talked with its head, Attila Kertesz-Farkas, about the lab, research and his way in science.

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.