The Leipzig School of Computing and Communications (LZSCC) includes a varied group of researchers.
Research results of our team can take on many different forms, ranging from theoretical breakthroughs to proof-of-concept implementations of novel algorithmic approaches to exciting industry-driven real-world applications.
Our academic output is not only reflected by research-driven teaching, but also frequently published in highly-regarded scholarly journals and internationally recognized research conferences. Currently, members of LZSCC conduct research on the following five key topics:
We expand the theoretical and practical horizons of Artificial Intelligence (AI) through the integration of diverse, complementary research trajectories, illuminating novel pathways toward understanding and innovation. Our key research areas include Machine Learning, Deep Learning, Generative AI, and Symbolic AI. We explore multimodal Deep Learning across text, audio, images, and conversations, with a strong commitment to AI for social good, addressing sustainability challenges in fields such as recycling, agriculture, and disaster response. We develop novel Graph Machine Learning algorithms and apply robust, theoretically grounded methods—drawing from decision theory, game theory, and imprecise probabilities—to enhance the reliability, robustness and interpretability of ML systems. Additionally, we innovate in Generative AI and Hybrid AI systems, including neuro-symbolic architectures powered by large language models. Our work also bridges formal verification and AI, leveraging traditional techniques (such as proof assistants and SMT solvers) as well as modern machine learning heuristics to create correct-by-construction software for high-stakes domains like healthcare and economic design. Together, our interdisciplinary approach aims to build AI that is not only powerful and scalable but also trustworthy and aligned with societal values.
Funded project:
Selected recent papers:
Cyber security research focuses on protecting digital systems, networks, and data from unauthorized access and attacks. It involves developing techniques to identify vulnerabilities, create robust defences, and respond to threats. Researchers study encryption methods, design secure software, analyze malware, and explore human factors in security. They also investigate emerging challenges like protecting IoT devices and countering sophisticated cyber-attacks that could impact critical infrastructure and personal privacy.
Research Projects:
[2024-2029] EPSRC: Programme Grant, SCULI: Securing Convergent Ultra Large-Scale Infrastructures [Suri, link]
[2024-2029] EPSRC: National Edge AI Hub [Suri, link]
[2024-2026] GCHQ: Cyber-MALCULT Countering Malicious Online Security Cultures [Suri]
[2024-2026] Leverhulme Trust: Securing Self-Aware Autonomous Power Networks [Suri, link]
[2023-2024] GCHQ: AISEC AI Security Vulnerabilities [Suri]
[2023-2023] INNOVATE UK: PINCH: End-to-end Cyber Security Technology to Better Understand the Risks of Deep Learning Model Stealing [Suri]
[2022-2023] DSTL: The Security Gene: Discovering Embedded Commonalities within Adversarial Machine Learning [Suri]
[2021-2024] EPSRC: TAS-S: EPSRC Trustworthy Autonomous System Node on Security [Suri, link]
[2019-2023] EC-H2020: CONCORDIA: Cyber Security Competence for Research and Innovation [Suri, link]
Formal methods research focuses on using mathematical techniques to design and verify computer systems and software. It aims to create error-free, reliable programs by applying rigorous logical reasoning. Researchers develop tools and languages to precisely specify system behavior, prove correctness, and detect flaws. This approach is crucial for critical systems where failures could have severe consequences, like in aerospace or medical devices.
The Interdisciplinary Computing Group is a team of researchers working at the intersection of computational methods, AI, complex systems, and human-machine interaction. Our focus spans multiple domains, including decision theory, game theory, social choice, machine learning, artificial life, unconventional computing, and system security. We aim to develop robust, reliable, and trustworthy computational models that address real-world challenges by integrating innovative approaches from diverse disciplines. Whether it involves enhancing machine learning under uncertainty, exploring emergent behaviours in artificial life, advancing human-machine collaboration, or designing secure and efficient new computing architectures, our group is committed to pushing the frontiers of interdisciplinary computing to create impactful technological solutions.
Funded project:
Smart and Proactive Multi-RAT Traffic Steering for V2X, funded by MSCA Staff Exchanges (https://www.research.lancs.ac.uk/portal/en/upmprojects/smart-and-proactive-multirat-traffic-steering-for-v2x(63abcc79-d35a-49b8-bb28-e7f735bd4b68).html)
Selected recent papers:
Software engineering research focuses on the systematic development, maintenance, and evolution of reliable, efficient, and high-quality software systems. A core focus lies in quality assurance, which encompasses testing, static and dynamic analysis, and performance monitoring to ensure software correctness and robustness. Within this domain, benchmarking plays a crucial role by providing reproducible metrics and standardized scenarios to evaluate and compare tools, techniques, and system behavior across different environments and over time. Recent research increasingly integrates artificial intelligence (AI) and large language models (LLMs) into the software development lifecycle. These techniques support tasks such as automated code generation, bug detection, requirement extraction from user feedback, and intelligent performance regression analysis. By learning from vast amounts of source code and software artifacts, AI and LLM-based tools enhance developer productivity and assist in identifying subtle issues that traditional methods might miss. Together, these areas contribute to the vision of intelligent, self-optimizing development pipelines that combine empirical rigor with automation to improve software quality and adaptability at scale.
Selected recent papers:
Reichelt, David Georg, and Stefan Kühne. “How to detect performance changes in software history: Performance analysis of software system versions.” Companion of the 2018 ACM/SPEC international conference on performance engineering. 2018.
Jie-Jun Hu-Bolz is an Assistant Professor in computing. She was previously a postdoctoral fellow at Max-Planck Institute for Human Development (Berlin) and the University of Essex (UK). Her research interests include incentive mechanisms design and game theory in various scenarios, such as IoT, mobile crowdsensing, SDN, blockchain, and large-scale dynamic systems. Her work primarily uses sophisticated mathematical methods to conceptualise multi-agent systems and investigate their complex dynamics. She has frequently published in IEEE Transactions. Currently, she is a PI of the EU-MSCA-SE Program.
