USZ has been one of the best universities in Hungary for years, according to the international QS World University Rankings. Outstanding professors have worked at the university, including the Nobel Laureate Albert Szent-Györgyi (1937), who was the first to isolate Vitamin C, extracting it from Szeged paprika. Our student body grew to 21000, in which the number of international students exceeds 4000 coming from 115 countries. Artificial Intelligence research is mainly concentrated in three research units: the Department of Software Engineering, the Department of Algorithms and Artificial Intelligence and the Research Group on Artificial Intelligence.
Apart from quality education these groups have also been doing high impact academic research on various fields of AI including speech technology, natural language processing, software engineering, security, deep learning, self-organizing systems and theory and methodology of machine learning. Applications include several healthcare domains where state-of-the-art NLP and deep learning based image processing technologies are applied. SZTE has active research in analyzing deep learning algorithms and examining the adversarial robustness of machine learning algorithms. The experience gained in developing state-of-the-art technologies has great value in the industry, hence SZTE has a healthy relationship with domestic and international industry partners. In these projects cutting edge AI technologies are developed and utilized for a wide range of tasks.
EMUSE – Cheese microbial Ecosystems MUltiScale ModElling: mechanistic and data driven approaches integration, 2021-2024, Marie Curie Innovative Training Network
The E-MUSE training programme aims at developing young researchers’ skills at the crossroad of artificial intelligence and life sciences. The challenge is to acquire a shared language bridging life science questions and original modelling approaches. The research programme of the E-MUSE network is to develop innovative modelling methodologies to understand a complex microbial ecosystem and identify levers to control and/or predict its evolution. To deal with biological complexity, biologists, mathematicians, and computer scientists have to work together to develop innovative methodologies. E-MUSE’s transdisciplinary network gathers academic and industrial partners to equip Early Stage Researchers (ESRs) with scientific, research and transferable skills to become leaders in academic research or industry. They will be at the cutting edge of the modelling methodologies that we apply to model structural and dynamic features of microbial communities, to identify key processes and biomarkers for specific applications.
ProsperAMnet - Progressing Service Performance and Export Results of Advanced Manufacturers Network, 2019-2022, Interreg CE
Many smart specialisation strategies of central European regions recognise the need of strengthening advanced manufacturers. Especially small- and medium-sized companies face substantial competitive pressure. One way out is to offer additional services, which, however, requires big structural changes, especially for service exporters. The ProsperAMnet project will offer know-how and create a transnational network to collect experiences from the local level. The project jointly develops innovative tools and approaches to support advanced manufacturers and will build their capacities. Additionally, partners will develop strategic action plans and recommendations for the future.
SASMob - Smart Alliance for Sustainable Mobility, 2018 - 2021, Urban Innovative Action
The SASMob project builds a data-driven and responsive IT-system through the partnership of public entities, private businesses and transport providers in Szeged to progress towards environmentally friendly urban mobility. The project will encourage cross-sector cooperation between businesses and the City of Szeged to co-design and tailor sustainable commuting solutions for employees, the biggest car-dependent mobility group. It will be called the SASMob Pledge. It will develop a data management process to analyse the complex urban mobility behaviour through data collected by smart phone applications which will be called the SASMob Response.
REPARA - Reengineering and Enabling Performance And poweR of Applications, 2013 - 2016, FP7 Collaborative project
To keep satisfying the ever-growing demand for computing power, there is a shift from homogeneous machines relying on one single kind of fast processing element (the CPU) such as typical PCs some years ago, programmed mostly sequentially, to heterogeneous architectures combining different kinds of processors (such as CPUs, GPUs and DSPs) each specialized for certain tasks, and programmed in a highly parallel fashion yet poorly optimising the available resources towards performance and low energy consumption. The REPARA project joins forces of experts in software engineering methodology, development tools, computer hardware design and analysis, all working hand-in-hand with industrial end-users to achieve a unified programming model for heterogeneous computers developing also the required automated software support tools. Relative to the baseline of a sequential algorithm executed on a current general-purpose processor, REPARA expects to achieve at least a 50% reduction of energy consumption combined with a performance improvement of at least by a factor of two.
SETIT - Security Enhancing Technologies for the Internet of Things, 2018 - 2022, 2018-1.2.1-NKP
In the SETIT project, our goal is to improve the security of IoT systems. For this purpose, we work on security enhancing technologies (mechanisms, tools, and methods) applicable in the IoT context. More specifically, we focus on 3 research areas within the project. In the “Application level security for embedded devices used in IoT systems” part of the project, we work on detecting software vulnerabilities using different program analysis techniques. This includes detecting vulnerabilities in the applications themselves, as well as in third party software components and libraries used by the applications. In the “Platform level security for embedded devices used in IoT systems” part of the project, we work on securing the boot process, hardening the OS, and continuously monitoring the integrity of the software running on the device. We also work on secure remote software update, as well as implementing trusted services on the device such as secure data storage and communications. In addition, we develop a penetration testing (ethical hacking) methodology customized for IoT systems. The last part of the project focuses on “Algebraic background and cryptographic algorithms that support areas 1 and 2”. In this part, we design and analyze cryptographic algorithms and protocols for IoT applications that fit better the resource constrained environment of IoT systems than traditional cryptographic mechanisms used on the Internet. We also aim at better understanding the algebraic properties underlying some of those cryptographic tools.
6720 Szeged Dugonics tér 13. Hungary