Attila Kertesz-Farkas
- Laboratory Head: Faculty of Computer Science / Laboratory on AI for Computational Biology
- Professor: Faculty of Computer Science / School of Data Analysis and Artificial Intelligence
- Attila Kertesz-Farkas has been at HSE University since 2015.
Education and Degrees
University of Szeged
University of Szeged
A post-doctoral degree called Doctor of Sciences is given to reflect second advanced research qualifications or higher doctorates in ISCED 2011.
Supervisor of the following Doctoral theses
- 1
Academic supervision of PhD students
- 2S. Jin A visual analytics system for explaining and improving attention-based traffic forecasting models, 2024
- 3N. Moshkov Application of deep learning algorithms to single-cell segmentation and phenotypic profiling, 2022
- 4P. Sulimov Learning generative probabilistic models for mass spectrometry data identification, 2020
- 5R. Chereshnev Human gait controlling system using machine learning methods suitable for robotic prostheses for patients suffering from double transfemoral amputation, 2019
- 6S. Gerasimov Bias estimation in deep learning based methods for tandem mass spectrometry data analysis
- 7F. L. Acquaye A deep neural network for annotation of tandem mass spectrometry data.
- 8K. R. Bhimani Exact p-value calculation for high resolution Tandem Mass Spectrometry data
Courses (2023/2024)
- Machine Learning (Postgraduate course field of study Postgraduate Studies, field of study Postgraduate Studies; 1 year, 1 semester)Eng
- Mentor's Seminar "Data Analysis in Biology and Medicine" (Master’s programme; Faculty of Computer Science; 1 year, 1-4 module)Rus
- Past Courses
Courses (2022/2023)
- Discriminative Methods in Machine Learning (Postgraduate course; Faculty of Computer Science; 2 year, 1 semester)Eng
- Generative Models in Machine Learning (Postgraduate course; Faculty of Computer Science; 2 year, 1 semester)Eng
- Mentor's Seminar "Biomedical Data Analysis" (Master’s programme; Faculty of Computer Science; 1 year, 1-4 module)Rus
Courses (2021/2022)
- Discriminative Methods in Machine Learning (Postgraduate course; Faculty of Computer Science field of study Computer and Information Scienc, field of study Informatics and Computer Engineering; 1 year, 1 semester)Eng
- Generative Models in Machine Learning (Postgraduate course; Faculty of Computer Science; 1 year, 1 semester)Eng
- Scientific Seminar "Biomedical Data Analysis" (Master’s programme; Faculty of Computer Science; 1 year, 1-4 module)Rus
Courses (2020/2021)
- Discriminative Methods in Machine Learning (Postgraduate course; Faculty of Computer Science field of study Computer and Information Scienc, field of study Informatics and Computer Engineering; 2 year, 1 semester)Eng
- Generative Models in Machine Learning (Postgraduate course; Faculty of Computer Science field of study Computer and Information Scienc, field of study Informatics and Computer Engineering; 2 year, 1 semester)Eng
- Scientific Seminar "Biomedical Data Analysis" (Master’s programme; Faculty of Computer Science; 1 year, 1-4 module)Rus
Dissertation for a degree of Doctor of Science
- 2022
A. Kertesz-Farkas Вычислительные методы для аннотирования данных тандемной масс-спектрометрии
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
‘My Research Has Evolved into A Broader and More Encompassing Vision’
Seungmin Jin, from South Korea, is researching the field of Explainable AI and planning to defend his PhD on ‘A Visual Analytics System for Explaining and Improving Attention-Based Traffic Forecasting Models’ at HSE University this year. In September, he passed the pre-defence procedure at the HSE Faculty of Computer Science School of Data Analysis and Artificial Intelligence. In his interview for the HSE News Service, he talks about his academic path and plans for the future.
Attila Kertes-Farkas received the best award for his presentation at IMLC 2023 conference
The head of AIC Laboratory, Attila Kertes-Farkas, was awarded for the best presentation at the ICMLC 2023 conference.
Congratulations to the Head of Laboratory on AI for Computational Biology Kertesz-Farkas Attila on receiving the well-deserved award
The head of the laboratory is presented for the award.
Attila Kertesz-Farkas successfully defended Doctoral Thesis
On May 19th, 2022, Attila Kertesz-Farkas defended the doctoral thesis.
Attila Kertesz-Farkas had a talk on FCS's Colloquium meeting
AIC LAB Head had a talk at traditional colloquium.
Seminar "Computational methods for tandem mass spectrometry data annotation"
On November 26, 2021 an online seminar was held on the results of a study by the head of the laboratory Attila Kertesz-Farkas.
Congratulations to the Head of Laboratory on AI for Computational Biology Kertesz-Farkas Attila on receiving the well-deserved award
The head of the laboratory is presented for the 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.