About us


Our mission

Data-driven decision-making is one of, if not the hottest field of science today. However, extracting information from data and machine learning based on data are not trivial processes, they require deeper expertise. This is the area of expertise of the Applied Data Science Research Group (AAK, according to the Hungarian abbreviation) at the Faculty of Informatics of Eötvös Loránd University (ELTE IK), which includes pre-processing, transformation and analysis of data using data science methods, and the development of industrial applications supported by artificial intelligence and machine learning. We aim to achieve new scientific results and their application in practice.


Areas of expertise

All our staff members have teaching and research responsibilities in the following areas in the application of ELTE IK:


Professional Team


Sensors and data

<p class="rpFeature">Usually, a company already has some kind of data collection solution. If this is still missing, sensors must be installed. AAK's services in this area include: data storage, database structure planning and implementation, and data delivery from the source to the storage location (database, servers) are among our competencies. Due to the large amount of data, it may be necessary to speed up data access for subsequent analyses, so we use data aggregations and various transformations. We design the database structure in such a way that we are able to receive new data without making any changes later on.</p>

Data preprocessing

<p class="rpFeature">Data preprocessing is an extremely diverse field. If we submit incorrect or wrong data to an analysis method, we will naturally get false results. AAK competence solutions: filtering of faulty data, handling and/or replacing missing data, detecting data gaps (e.g.: a sensor fails and does not send data, then this data is lost until it is repaired), preparation of basic statistical data, execution of various data transformation steps for the given depending on the analysis method, selection of features (feature selection), selection of main components depending on the given analysis method, dimensionality reduction and matrix factorization.</p>


Data analysis


During the analysis of the data, we provide answers to problems defined by each company, but different analytical methods provide answers to different problems. Therefore, the selection and implementation of the appropriate method requires professional competence. Based on the input data created as a result of the pre-processing, we can create models that are capable of automatically supporting corporate decision-making. We can apply the methods of the following IT subfields:


Staff


Dr. habil. Farkas Gábor

Dr. habil. Farkas Gábor

Lead researcher

ELTE, Faculty of Informatics, Computer algebra Department, university associate professor

Research Fields:

  • Analytic and computer number theory

  • Probability theory

  • Data science, matrix factorization

Dr. Szekeres Béla János

Dr. Szekeres Béla János

Researcher

ELTE, Faculty of Informatics, Department of Numerical Analysis, university assistant professor

Research Fields:

  • Neural networks

  • Functional-differential equations

  • Machine learning

Dr. Pödör Zoltán

Dr. Pödör Zoltán

Researcher

ELTE, Faculty of Informatics, Department of Numerical Analysis, university associate professor

Research Fields:

  • Statistically based data analysis

  • Data mining, Time series analysis

  • Management and processing of sensor data

Dr. Gludovátz Attila PhD.

Dr. Gludovátz Attila PhD.

Researcher

ELTE Faculty of Informatics, Department of Programming Theory and Software Technology, University assistant professor

Research Fields:

  • Statistically based data analysis

  • Data mining, Time series analysis

  • Management and processing of sensor data

Dr. Bencsik Gergely

Dr. Bencsik Gergely

Researcher

ELTE Faculty of Informatics, Department of Data Science and Data Technology, university associate professor

Research Fields:

  • Statistical tests

  • Artificial Intelligence

  • Decision support systems

Szőlősi József

Szőlősi József

Researcher

ELTE Faculty of Informatics, Research Assistant, PhD student

Research Fields:

  • Data science

  • Management and processing of sensor data

  • Deep learning

Magyar Péter

Magyar Péter

University teaching assistant

ELTE, Faculty of Informatics, Computer algebra Department, University teaching assistant

Research Fields:

  • Data science, matrix factorization

  • High Performance Computing (HPC)

  • Artificial Intelligence

Papatyi Dániel

Papatyi Dániel

Research assistant

ELTE Faculty of Informatics, MSc student

Research Fields:

  • Data science, matrix factorization

  • High Performance Computing (HPC)

  • Artificial Intelligence

Karakai Patrik

Karakai Patrik

Research assistant

ELTE Faculty of Informatics, PhD student

Research Fields:

  • Data science, matrix factorization

  • High Performance Computing (HPC)

  • Artificial Intelligence

Contact


Adattudomány Alkalmazásai Kutatócsoport
Eötvös Loránd Tudományegyetem Informatikai Kar
Szombathely, Károlyi Gáspár tér 4, 9700
E-mail: contact@adat2k.eu

Contact us with confidence.