PhD Degree in Partnership with FIT BUT Brno

We nurture IT talents. KInIT PhD students will become experts in artificial intelligence, starting their education journey with us. Every KInIT PhD student works on their research idea in collaboration with their supervising team.

KInIT PhD students research problem-oriented topics, utilizing advanced research methods with their supervising team. We provide a supervising team depending on the dissertation topic chosen by our PhD students. Apart from your KInIT supervisor, your supervising team usually also comprises external mentors – top scientists from abroad or consultants from industrial research partners (companies). The supervising teams are in some cases connected to international projects.

Our PhD program is a combined form of study, it runs in collaboration with the Faculty of Information Technology at BUT, Brno (Czechia). PhD students at KInIT attend and have to pass selected courses at FIT BUT.

A PhD degree is the highest degree of education at KIniT. How it works?

Why PhD at KInIT:

  • Learn from the best
  • Rise to Excellence
  • Build your network
  • Research that impacts
  • Open culture
  • No distractions

PhD Students

Ivan Agarský Solving empirical risk minimization problems using efficient optimization methods More info
Close Ivan Agarský Solving empirical risk minimization problems using efficient optimization methods

Supervising team: Peter Richtárik (King Abdullah University of Science and Technology)

With my supervisor Peter Richtárik, a professor at KAUST, we try to design methods for efficient optimization in the context of federated learning.

Ivana Beňová Understanding and grounding of multimodal image-language models More info
Close Ivana Beňová Understanding and grounding of multimodal image-language models

Supervising team: Marián Šimko (KInIT), Jana Košecká (George Mason University)

The connection of image and language to multimodal image-language modeling brings many challenges to AI and previous work shows that the current models have misunderstood or unexplored issues, like relying on language priors. These problems suggest that it is essential to understand how models work and how the knowledge is encoded in them. In this dissertation thesis, we are focusing on grounding language in vision and understanding multimodal image-language models. This thesis is supervised by Jana Košecká, professor of computer science at George Mason University, USA.

Ján Čegiň Machine learning with human in the loop More info
Close Ján Čegiň Machine learning with human in the loop

Supervising team: Jakub Šimko (KInIT), Peter Brusilovsky (University of Pittsburgh)

Adversarial training is one of the methods to increase robustness of machine learning models used in false information detection. Increasing the diversity of adversarial examples is crucial in successful adversarial training, which is where human-in-the-loop methods come into play.

Matej Čief Recommender and adaptive web-based systems More info
Close Matej Čief Recommender and adaptive web-based systems

Supervising team: Michal Kompan (KInIT), Branislav Kveton (Amazon’s lab in Berkeley)

This work deals with some fundamental problems when using off-policy evaluation and learning in multi-armed bandit systems, namely:
(1) off-policy evaluation with large action spaces,
(2) overconfidence in off-policy optimization for structured recommendations, and
(3) designing safe and optimal data-gathering policies.

Supervised by Branislav Kveton, principal scientist at Amazon, focusing on online and offline bandit algorithms.

Patrik Goldschmidt Network Intrusion Detection Using Machine Learning More info
Close Patrik Goldschmidt Network Intrusion Detection Using Machine Learning

Supervising team: Daniela Chudá (KInIT)

The research focuses on the detection of computer network anomalies and intrusions utilizing machine learning techniques. Instead of following the classical “better model, better results” style, we rather decided to adopt a data-centric approach to the problem. This way, we expect to improve the practical usability of intrusion detection systems by improving data quality rather than creating and fine-tuning complex machine learning architectures.

Santiago Jose de Leon Martinez Eye Tracking and Recommender Systems More info
Close Santiago Jose de Leon Martinez Eye Tracking and Recommender Systems

Supervising team: Mária Bieliková (KInIT), Róbert Móro (KInIT)

As part of Eyes4ICU MSCA doctoral network, we study eye tracking in recommender systems  to better understand users. Using computational gaze models combined with other feedback, we can better understand what information users are receiving throughout their interaction with systems and create better recommendation systems and solve problems, such as biases and filter bubbles.

Martin Mocko Malware clustering Using Machine Learning More info
Close Martin Mocko Malware clustering Using Machine Learning

Supervising team: Daniela Chudá (KInIT), Eset (industry partner)

Our research deals with clustering of samples in the malware domain utilizing primarily machine learning techniques. Our focus is on

1.) clustering evaluation and experiment methodology,
2.) the use of a larger number of clusters, and
3.) the use of deep clustering and contrastive learning in the domain.

The work is realized in cooperation with our industry partner, ESET.

Rastislav Papšo Recommender and adaptive web-based systems More info
Close Rastislav Papšo Recommender and adaptive web-based systems

Supervising team: Michal Kompan (KInIT), Luigi’s Box (industry partner)

In my work I explore recommendation methods that perform well not only in controlled laboratory conditions and on standard datasets, but also in real world scenarios, where additional requirements are put on the methods (e.g., computation costs). Together with our industrial partner Luigi’s Box, we aim at improving the recommender system in the e-commerce domain.

Peter Pavlík Physics-informed Deep Learning in Forecasting More info
Close Peter Pavlík Physics-informed Deep Learning in Forecasting

Supervising team: Viera Rozinajová (KInIT), Anna Bou Ezzeddine (KInIT), Softec (industry partner)

In cooperation with an industry partner SOFTEC, we focus on using deep learning for efficient and accurate forecasting. To achieve this, we apply principles of physics-informed machine learning. The performance of developed algorithms is evaluated on the task of precipitation nowcasting.

Branislav Pecher Addressing small labeled data in machine learning training More info
Close Branislav Pecher Addressing small labeled data in machine learning training

Supervising team: Mária Bieliková (KInIT), Ivan Srba (KInIT)

To circumvent the issue, many approaches are emerging and are presently researched by many researchers: meta-learning, transfer-learning, weak-supervision, zero/one-shot learning, semi-supervised learning. Each of these fields (or combination thereof) presents opportunities for new discoveries. Orthogonal to this, explainability and interpretability of models is an important factor to consider and advances in this regard are welcome (generally in AI and particularly in the mentioned approaches).

Your future
starts at KInIT

Transform your curiosity into excellence. If you are interested in doing a PhD degree at KInIT, let us know and contact our admissions team. We can discuss your academic background and research ideas to ensure we have supervisory expertise to support you.

Get in touch How to apply?

Supervising team

Mária Bieliková Expert researcher, KInIT More info
Close Mária Bieliková Expert researcher, KInIT

Mária Bieliková is an expert researcher at KInIT. She focuses on human-computer interaction analysis, user modeling and personalization. Recently, she has been working in data analysis and modeling of antisocial behavior on the Web. She is active in discussions on trustworthy AI at the national and European levels. Maria has supervised 19 successful doctoral graduates to date. She co-authored 70+ journal publications, 200+ conference papers, received 4,400+ citations (Google Scholar h-index 30), and serves on the editorial board of two CC journals. She has been the principal investigator in 40+ research projects.

Anna Bou Ezzeddine Expert researcher, KInIT More info
Close Anna Bou Ezzeddine Expert researcher, KInIT

Anna Bou Ezzeddine is an expert researcher at KInIT focusing on artificial intelligence, machine learning and probabilistic modeling.  She has rich experience in nature-inspired computing. In particular, she used nature-inspired computing to help develop a forecasting system that could predict a country’s macroeconomic development. Before her employment at KInIT, she worked in the Faculty of Informatics and Information Technologies at the Slovak University of Technology in Bratislava as an associate professor, where she supervised more than 80 successful Bachelor’s and Master’s theses.

Peter Brusilovsky Professor, University of Pittsburgh, USA More info
Close Peter Brusilovsky Professor, University of Pittsburgh, USA

Peter Brusilovsky is a Professor at the School of Computing and Information, University of Pittsburgh, where he directs the Personalized Adaptive Web Systems (PAWS) lab. His research is focused on user-centered intelligent systems in the areas of adaptive learning, recommender systems, and personalized health. He is a recipient of Alexander von Humboldt Fellowship, NSF CAREER Award, and Fulbright-Nokia Distinguished Chair. Peter served as the Editor-in-Chief of IEEE  Trans. on Learning Technologies, and a program chair for several conferences including RecSys.

Daniela Chudá Expert researcher, KInIT More info
Close Daniela Chudá Expert researcher, KInIT

Daniela Chudá is an expert researcher at KInIT. She focuses on information security, in particular behavioral biometrics in the context of user authentication, detection of security vulnerabilities and network anomaly detection. She graduated from the Faculty of Mathematics and Physics, Comenius University. She worked as a researcher and as an associate professor and she is a former vice-dean for Bachelor’s, Master’s and PhD study and student mobility at the Slovak University of Technology.

Juraj Jánošík Leader of AI/ML section, Eset More info
Close Juraj Jánošík Leader of AI/ML section, Eset

Juraj Jánošík is a Senior Manager of Threat Detection and Machine Learning at ESET. He is the leader of Machine Learning research and is responsible for incorporating ML approaches into ESET’s multi-layer endpoint protection products. He was a member of the international working groups responsible for various botnet takedowns and speaker on conferences like RSA, MWC and CARO.

Michal Kompan Expert researcher, KInIT More info
Close Michal Kompan Expert researcher, KInIT

Michal Kompan is an expert researcher at KInIT. He focuses on recommender systems, machine learning, user modeling, and information retrieval. His research is focused on predictive modeling and customer behavior (e.g., churn prediction, next-item recommendation), as well as content-based adaptive models. Michal serves as a reviewer or/and program committee member at several international conferences, such as RecSys, SIGIR, WWW, ADBIS, Hypertext, UMAP and SMAP.

Jana Kosecka Professor, George Mason University, USA More info
Close Jana Kosecka Professor, George Mason University, USA

Jana Kosecka is a Professor at the George Mason University. She is interested in computational models of vision systems, acquisition of static and dynamic models of environments by means of visual sensing, high-level semantic scene understanding and human-computer interaction. She held visiting positions at UC Berkeley, Stanford University, Google and Nokia Research, and served as Program chair, Area chair or senior member of editorial board for  leading conferences in the field CVPR, ICCV, ICRA.

Jana is currently mentor of our PhD student: Ivana Beňová

Branislav Kveton Principal Scientist, Amazon’s lab, USA More info
Close Branislav Kveton Principal Scientist, Amazon’s lab, USA

Branislav Kveton is a Principal Scientist at Amazon’s lab in Berkeley. He proposes, analyzes, and applies algorithms that learn incrementally, run in real time, and converge to near-optimal solutions as they learn. He made several fundamental contributions to the field of multi-armed bandits. His earlier work focused on structured bandit problems with graphs, submodularity, and low-rank matrices, and ranked lists. His recent work focuses on making bandit algorithms practical

Filip Mazán Senior Software Engineer Team Lead, Eset More info
Close Filip Mazán Senior Software Engineer Team Lead, Eset

Filip Mazán is a Senior Software Engineer and Team Lead at ESET. He is working on various machine learning research projects leveraging deep-learning and he is leading a team responsible for automated threat detection and application of artificial intelligence in threat hunting. Some of the highlights of his career include speaking at the RSA Conference and membership in several botnet eradication groups taking on botnets such as Dorkbot and Gamarue.

Róbert Móro Senior researcher, KInIT More info
Close Róbert Móro Senior researcher, KInIT

Róbert Móro is a senior researcher at KInIT. He focuses on user modeling, personalization and machine learning. His current primary research interest is in countering online disinformation and modeling users and human-computer interaction on the Web. He has participated in several international research projects (including Horizon Europe) and served as a program committee member at several conferences (e.g., ACM UMAP, ACM ETRA, IJCAI).

Peter Richtárik Professor, King Abdullah University of Science and Technology, Saudi Arabia More info
Close Peter Richtárik Professor, King Abdullah University of Science and Technology, Saudi Arabia

Peter Richtárik is a Professor of Computer Science & Mathematics at KAUST. He is one of the founders and a Fellow of the Alan Turing Institute. Through his work on randomized and distributed optimization algorithms, he has contributed to the foundations of machine learning and federated learning. He serves as an Area Chair of leading machine learning conferences, including NeurIPS, ICML and ICLR.

Viera Rozinajová Expert researcher, KInIT More info
Close Viera Rozinajová Expert researcher, KInIT

Viera Rozinajová is an expert researcher at KInIT. She is focusing on intelligent data analysis, particularly predictive modeling, cluster analysis, anomaly detection and optimization. Before her employment at KInIT, she worked as an associate professor at the Faculty of Informatics and Information Technologies at the Slovak University of Technology in Bratislava, where she headed up the Big Data Analysis group. She has authored/co-authored more than 70 publications in scientific journals and conferences and has participated in more than 25 national and international research projects and has led several of them. 

Ivan Srba Senior researcher, KInIT More info
Close Ivan Srba Senior researcher, KInIT

Ivan Srba is a senior researcher at KInIT. Ivan has expertise in artificial intelligence and machine learning, with a focus on data emerging from user behavior on social computing and collective intelligence systems, such as social networks, knowledge sharing systems and e-learning. His current primary research interest is in countering online disinformation and misinformation.

Ondrej Svačina Business Development Manager and Senior Consultant, Softec More info
Close Ondrej Svačina Business Development Manager and Senior Consultant, Softec

Ondrej Svačina is a Business Development Manager and Senior Consultant at SOFTEC. He is responsible for managing development of new products and services which often incorporate state of the art technologies, like machine learning and Internet of things.

Jakub Ševcech Research consultant, KInIT More info
Close Jakub Ševcech Research consultant, KInIT

Jakub Ševcech is a Research Consultant at KInIT. He focuses on information security, software engineering, machine learning, stream data processing, anomaly detection and explainable machine learning models. He holds a PhD in Intelligent Information Systems from the Slovak University of Technology.

Jakub Šimko Expert researcher, KInIT More info
Close Jakub Šimko Expert researcher, KInIT

Jakub Šimko is an expert researcher at KInIT, where he also leads the Web and User Data Processing team. Jakub focuses on the intersection of human computation, machine learning and user modeling. He has recently been working on social media algorithm auditing, misinformation modeling and promotes interdisciplinary approaches to computer science research. He graduated from Slovak University of Technology in Bratislava, where, after receiving his PhD, he worked for 7 years as a researcher and teacher. He co-authored more than 30 internationally recognized publications, together receiving more than 350 citations.

Marián Šimko Expert researcher, KInIT More info
Close Marián Šimko Expert researcher, KInIT

Marián Šimko is an expert researcher at KInIT. Marián focuses on natural language processing, information extraction, low-resource language processing and trustworthiness of neural models. He is a former vice-dean for Master’s study and alumni co-operation at the Slovak University of Technology.

Industrial partners

For more than 30 years, ESET® has been developing industry-leading IT security solutions that protect businesses and consumers worldwide.

Our R&D activities focus on all aspects of cybersecurity – detection of threats, research of malware, APTs, exploits, phishing, spam, monitoring of botnets and targeted attacks, protection of mobile platforms, IoT environment and network infrastructure as well as zero-trust security. We do a lot of data science and machine learning on very complex and extensive datasets.

Research Blog: https://www.welivesecurity.com/

Luigi’s Box is a Bratislava-based company, providing an intuitive site-search and product discovery SaaS to its customers, primarily in e-commerce. With two of the co-founders having PhD degrees in Software Engineering, R&D is at the core of Luigi’s Box DNA. We strive to bring our clients smart, high-quality tools which are personalized, context-aware, and scalable.

Road transportation and environmental quality are two big research areas at Softec. We have designed our own IoT stations for winter road maintenance and air/noise pollution monitoring. The PhD study should focus on meaningful interpretation and enrichment of the measured data, as well as optimisation and prescriptive analytics with high added value, such as routing of smart road maintenance vehicles, spatial extrapolation of road condition forecasts and air pollution forecasting.

We are ready to provide data from our network of IoT stations measuring meteorological and environmental quality and conditions such as air and road temperatures, air humidity and pressure, air pollutant concentrations, noise level, etc.

benova web

I love that my topic is directly linked to real-world problems and projects that KInIT is working on, either in collaboration with industry or in research projects. I’m working on something that will have a real impact.

Ivana Beňová

Your future
starts at KInIT

Transform your curiosity into excellence. If you are interested in doing a PhD degree at KInIT, let us know and contact our admissions team. We can discuss your academic background and research ideas to ensure we have supervisory expertise to support you.

Get in touch How to apply?