Members

Alex Kummer

Head of Research Lab

Alex is a Research Fellow at the Department of Process Engineering at University of Pannonia. He graduated with bachelor's (2014) degree and with master's degree (2016) in Chemical Engineering on Process Engineering faculty. He spent two years (2015-2017) at Nitrogénművek as a process engineer helping to start up and operate the new granulation and neutralizer plants. He recieved his PhD degree (2021) for his research results on process safety and process control related to runaway reactions. He became a Data and Systems Scientist and Industry 4.0 Solution Development Engineer in 2021. His current research focuses on the application of artificial intelligence in systems engineering.

János Abonyi

Head of Research Group

Janos is a full professor at the Department of Process Engineering at the University of Pannonia in computer science and chemical engineering. He received MEng and PhD degrees in chemical engineering in 1997 and 2000 from the University of Veszprem, Hungary. In 2008, he earned his Habilitation in the field of Process Engineering, and the DSc degree from the Hungarian Academy of Sciences in 2011. In the period of 1999-2000 he was employed at the Control Laboratory of the Delft University of Technology (in the Netherlands). Dr. Abonyi has co-authored more than 250 journal papers and chapters in books and has published five research monographs and one Hungarian textbook about data mining. His research interests include complexity, process engineering, quality engineering, data mining and business process redesign. 

Tamás Kegyes

Tamás is a researcher at the Faculty of Information Technology at University of Pannonia. He graduated with master's degree in mathematics at the Budapest University of Technology and Economics (2005) and also master's degree in actuarial sciences at Corvinus University of Budapest (2018). He started his PhD studies in 2020 at University of Pannonia. His research interested covers the areas of Machine Learning, Predictive Modelling and their industrial and economical applications. His current research focuses on Reinforcement Learning and Machine Learning applications for Supporting Industry 4.0.

Kinga Szatmári

Kinga is a PhD student at the Department of Process Engineering at University of Pannonia, supervised by Dr. Alex Kummer and Dr. Sándor Németh. She graduated with bachelor's degree (2021) and master's degree (2023) in chemical engineering. Her research focuses on how to apply reinforcement learning algorithms in chemical industries. Her current research includes resilience-based reinforcement learning and explainable reinforcement learning in chemical process safety. 

Éva Kenyeres

Éva is a PhD student at the Department of Process Engineering at University of Pannonia, supervised by Dr. Alex Kummer and Dr. János Abonyi. She graduated with bachelor’s (2022) and master’s (2024) degree in chemical engineering at the University of Pannonia. Her area of research focuses on the handling and utilization of uncertain information by algorithms based on stochastic models, including particle filter and importance sampling, and their potential application within an industrial environment. Her current research also deals with sustainability-related problems such as waste management contributing to the circular economy goals.  

Balázs Fricz

Balázs is a PhD student at the Department of Process Engineering at University of Pannonia, supervised by Dr. Alex Kummer and Dr. Lajos Nagy. He graduated with bachelor’s (2023) and master’s (2025) degree in Chemical Engineering at the University of Pannonia. His area of research focuses on the application of reinforcement learning to chemical processes, with validation through real life experiments on laboratory scale equipment. 

Martin Ferenczi

Martin  is pursuing his master's degree in Data Science at the University of Pannonia supervised by Dr. Alex Kummer and Dr. János Abonyi. He graduated with bachelor's (2023) degree in Computer Science at the University of Pannonia. His current research focuses on ensemble deep learning-based classification and parameter identification. His research interest is the application of artificial intelligence in systems engineering.

Krisztián Kovács

Krisztián is a Computer Science Operational Engineer student at the Faculty of Information Technology at the University of Pannonia. He has been working in software development for many years and has been involved in several projects in different areas such as web development, application development and data analysis. With the continuous evolution of the industry, he remains committed to learning and implementing new technologies to create more efficient and innovative software solutions. His research focuses on anomaly detection utilizing GAN-based deep learning techniques under the supervision of Dr. Alex Kummer. Anomaly detection is crucial as it enables the identification of unusual patterns or events within data, deviating from the norm. This capability is vital across various applications such as fraud detection, cybersecurity, fault detection in industrial systems, and medical diagnosis. By effectively identifying anomalies, organizations can mitigate risks, enhance the reliability and security of systems, and streamline decision-making processes.