PeWe Family

What we do?

Datalys is a research subgroup of PeWe at Informatics and Software Engineering, Slovak University of Technology in Bratislava, Faculty of Informatics and Information Technologies. It consists of postdoc, several doctoral students and their master and bachelor students. We focus on the data analysis, machine learning in the scale of big data volumes, various domains and purposes. Especially, some members concern the topics of Neural Networks. Members are participating in various research and application projects across the faculty.

Jakub and Andrián got places in SVOC

Jakub Mačina and Adrián Huňa with their works Jakub Mačina: Recommendation of New Questions in Online Student Communities Adrián Huňa: Automatic Answering of […]


Lukáš Marták won Artificial Intelligence SVOC Category

Lukáš Marták with his work on Modelling Music Structure using Artificial Neural Networks won Artificial Intelligence category in Czech and Slovak Student Research […]

21th PeWe Workshop is over

On April 7, 2017 our regular PeWe workshop took place on Faculty of Informatics and Information Technologies in Bratislava.


Our member Ivan Srba in media

Read an interview with Ivan Srba in Pravda newspaper: IT expert works with projects on collective wisdom and also stupidity.  


Our participation in student research competition – SVOC 2017 in Pilsen

Based on results presented on our student research conference IIT.SRC 2017, representative works from each category were nominated and their authors offered participation […]


Sentigrade in Connection magazine

An article about our tool Sentigrade was published in Connection magazine. You can read it both in English and Slovak: Machines can understand […]


Sentigrade – how it all began?

Sentigrade is a result of a cooperation of Faculty of informatics and information technologies STU in Bratislava and PR company Seesame. It automatically […]


Python For Data Science at edX

As a researcher in area of information technologies you have to work with data somehow. Data are everywhere and we must be able […]