Prof. Dr. Julius Schöning is a Professor at the Faculty of Engineering and Computer Science , Osnabrück University of Applied Sciences. His research focuses on Artificial Intelligence , Human-Computer Interaction , and Computer Vision within agricultural contexts. 2019–Present: Professor, Hochschule Osnabrück 2018–2019: System Architect, ZF Friedrichshafen AG 2014–2018: Researcher, University of Osnabrück 2009–2013: Project Lead/System Engineer, CLAAS Harsewinkel Education: M.Sc. in Intelligent Embedded Microsystems (Freiburg), B.Eng. in Mechatronics (DHBW Stuttgart) His research spans smart agriculture , quantum NLP , and explainable AI systems , with recent work on vibrotactile warning systems, AI compliance frameworks, and hybrid dataset applications in farming. Publications emphasize interdisciplinary approaches bridging technology and agricultural practice . Scientific honors include: DAAD Stipendium for conference participation Best Paper Award (2018) IEEE GHTC Student Paper Contest Winner (2016) Sonderpreis für gute Lehre (2017) Finalist/Falling Walls Lab (2015)
Yepang Liu is a tenured Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China. He leads the Software Quality Lab and serves as director of the Trustworthy Software Research Center within the Research Institute of Trustworthy Autonomous Systems. His educational background includes a B.Sc. with honors from Nanjing University (2010) and a Ph.D. from the Hong Kong University of Science and Technology (2015), where he was supervised by Prof. Shing-Chi Cheung. Prior to joining SUSTech, he worked as a postdoc at HKUST's CASTLE Lab and Cybersecurity Lab. Liu's research primarily focuses on software testing and analysis, empirical software engineering, AI for SE, software security, and trustworthy AI. His work bridges traditional software engineering with cutting-edge AI technologies, particularly in automated testing, security analysis, and quality assurance for mobile, blockchain, and extended reality applications. Recent projects explore how large language models can enhance bug detection, improve testing automation, and address fairness issues in machine learning systems. His contributions have been recognized with three ACM SIGSOFT Distinguished Paper awards (ICSE 2021, ASE 2016, ICSE 2014) and one Distinguished Artifact award (ICSE 2019). He has also received the ACM SIGSOFT Service Award and Distinguished Reviewer Award for his extensive service to the software engineering community. Top-10 Most Active Early-Stage Software Engineering Researcher (2013-2020) Top-10 Most Popular Instructor Among 2024 Undergraduate Graduates at SUSTech Junior Faculty of the Year (2021) SUSTech Teaching Excellence Award (2021) Outstanding Mentor Award (2020, 2024) Liu actively serves on the editorial boards of Empirical Software Engineering (EMSE) and Journal of Computer Science and Technology (JCST). He has participated in over 80 conference committees including leadership roles in ICSE, FSE, ASE, and ISSTA. His research is supported by the National Natural Science Foundation of China, National Key Research and Development Program, and leading Chinese IT companies. He regularly mentors PhD and MSc students and has guided multiple national competition award-winning teams. The Software Quality Lab under Liu's direction focuses on innovative approaches to software testing, security analysis, and quality assurance across various platforms including mobile, blockchain, and extended reality applications. Current projects emphasize the integration of AI techniques with traditional software engineering practices to address emerging challenges in software quality.
Karl-Erik Årzén is Professor and Head of the Department of Control Engineering at Lund University's Faculty of Engineering. He is also Co-director of the Wallenberg AI, Autonomous Systems and Software Program (WASP) and a key member of ELLIIT, the excellence center in information technology. His roles include leadership in AI and digitalization profile areas at both LTH and Lund University. His research lies at the intersection of control engineering and computer science, with a focus on cyber-physical systems, real-time systems, embedded control, and resource management in cloud and edge computing environments. He has pioneered methods for predictable performance in cloud applications and dynamic resource allocation using control-theoretic approaches. The recent publications highlight a strong trend in control over the cloud and edge, real-time scheduling co-design, distributed camera systems, and reinforcement learning for auto-scaling. Key topics include model predictive control, LQG-based scheduling, bandwidth allocation, and robustness in cyber-physical systems. The work spans theoretical control design and practical implementation in distributed systems. His scientific awards include multiple Best Paper Awards from IEEE and ACM conferences in 2018, 2016, and 2004, recognizing excellence in autonomic computing, edge computing, and real-time systems. Best Paper Award, IEEE International Conference on Autonomic Computing, 2018 Best Paper Award, IEEE International Conference on Edge Computing (EDGE), July 2018 Best Paper Award - RTNS 2016 Best Paper Award - RTCSA 2004 Årzén has supervised over 20 PhD students, including Mikael Johansson, Anton Cervin, Yang Xu, and Per Skarin, and currently supervises Ahmed Al Bayati and Max Nyberg Carlsson. His grant portfolio includes major projects such as WASP, AORTA (VINNOVA), and ELLIIT's 'Robust and Secure Control over the Cloud'. He has also contributed to innovation through tools like TrueTime and Jitterbug. He leads the RobotLab LTH initiative and is involved in the Nordic University Hub on Industrial Internet of Things (HI2OT). His work bridges academia and industry, with collaborations on adaptive control, cloud-native systems, and autonomous robotics.
Prof. Dr. Katja Thoring is a Full Professor of Integrated Product Design at the Technical University of Munich (TUM School of Engineering and Design). She holds a doctorate in Design Research from Delft University of Technology and has previously served as Professor of Integrated Design at Anhalt University of Applied Sciences in Dessau from 2009–2022. Her research bridges product design, architectural space, and technology, focusing on how physical environments stimulate creativity and design processes across functional, emotional, and cognitive dimensions. Key areas include generative AI applications in design, innovative research methodologies, and creative workspace design. She developed methods like the 'Delphi Design Sprint' and contributed to frameworks such as the FOD (Future-Oriented Design) model. Thoring is a member of prominent design societies (DGTF, Design Society, DRS) and a founding member of the Academy of Design Innovation Management (ADIM). Notable awards include the 'Best Paper Award' at ADIM Conference (2017) and recognition as a top early-career researcher (2019). Her work integrates design education innovation, with studies on pedagogical spaces and cross-cultural design thinking. She has published extensively on design knowledge models, creative environments, and future-oriented design strategies.
Michael Pradel is a full professor at the University of Stuttgart, specializing in software engineering, programming languages, and machine learning. He will join CISPA as a faculty member from September 2025 while retaining his Stuttgart position. His research focuses on: Neuro-symbolic software analysis Web application analysis Dynamic analysis and test generation Quantum software testing Machine learning for code Recent publications address: LLM-based program repair (RepairAgent, Treefix) WebAssembly analysis (Wasm-R3, LintQ) Python security and analysis (DyLin, DyPyBench) Quantum program analysis (LintQ) Scientific awards: Ernst-Denert Software Engineering Award Emmy Noether grant (1.3M Euro) ERC Starting Grant (1.5M Euro) 3x ACM SIGSOFT Distinguished Paper Award at FSE ACM Distinguished Member Best Paper/Distinguished Paper Awards at ISSTA, ASE, ASPLOS, MSR Key contributions include: DeepBugs for name-based bug detection Getafix for automated bug fixing LintQ for quantum program analysis DyLin for Python dynamic analysis Neuro-symbolic developer tools
Dr. Carolin Vollenberg serves as a Post-Doctoral Researcher at the Chair of Information Systems & Transformation Management within the Faculty of Computer Science at the University of Duisburg-Essen (UDE). Her academic journey includes a PhD from the University of Muenster (2021-2025), an M.Sc. in Technical Consulting and Management from Hochschule Hamm-Lippstadt (2018-2020), and a B.Eng. in Biomedical Technology from the same institution (2014-2018). Prior to her current position, she worked as a Research Assistant at South Westphalia University of Applied Sciences and gained industry experience at Zapp Systems GmbH. PhD in Business Informatics (2021-2025), University of Muenster M.Sc. Technical Consulting and Management (2018-2020), Hochschule Hamm-Lippstadt B.Eng. Biomedical Technology (2014-2018), Hochschule Hamm-Lippstadt Research Focus: Vollenberg specializes in the governance of lightweight IT systems, digital transformation in public and healthcare sectors, and process mining applications. Her work bridges technical implementation with organizational behavior, particularly examining resistance to automation in sensitive domains like healthcare. She investigates how organizations navigate unintended consequences of technology adoption, with emphasis on RPA (Robotic Process Automation), omnichannel transformation, and data-driven process optimization. Her research methodology combines ethnographic field studies with quantitative process analysis. Publication Trends: Analysis of her 16 publications (2020-2025) reveals strong focus on healthcare IT (45% of works), public sector digitalization (30%), and foundational process management (25%). Recent output shows increasing emphasis on ethical dimensions of process mining and sustainability applications. Her collaborative work spans multiple European institutions with consistent publication in top IS conferences (ICIS, ECIS, HICSS). Best Paper nomination at HICSS-55 (2022) Associate Editor for General Track at Internationale Tagung Wirtschaftsinformatik (WI) 2025 Professional Engagement: Vollenberg actively contributes to academic discourse through editorial roles and peer review. Her industry collaborations with healthcare providers and public sector entities demonstrate applied research impact. Current projects examine virtual nursing transformations and crisis-responsive RPA implementations, reflecting her commitment to solving real-world operational challenges through information systems innovation.
Nuno Santos is an Associate Professor in the Department of Computer Science and Engineering at Instituto Superior Técnico (IST), University of Lisbon, and a senior researcher at INESC-ID Lisbon. He leads the SysSec team, focusing on systems security and privacy. His research spans secure enclaves, network security, and censorship-resistant systems. Education: Ph.D. in Computer Science (2013) from Max Planck Institute for Software Systems (MPI-SWS) in affiliation with Saarland University. Visiting research stints at Vrije Universiteit Amsterdam (2018) and Technical University of Munich (2024). Research Interests: Systems security, privacy, trusted execution environments (TEEs), network security, censorship resistance, AI security, and secure cloud computing. He has pioneered work on mitigating vulnerabilities in TrustZone-based TEEs and enhancing confidential computing. Publications: Over 30+ peer-reviewed articles in top-tier venues like S&P, USENIX Security, CCS, and NDSS. Recent trends focus on AI-driven security (e.g., automated exploit generation, prompt-to-SQL injections) and confidential computing (e.g., AMD SEV-SNP analysis). Awards: IST Outstanding Teaching Award (2019/2020), 2024 Prémio Científico Universidade de Lisboa/Caixa Geral de Depósitos. Advising & Grants: Supervised MSc theses in areas like AI-powered vishing attacks and confidential VMs. Active in conference organization (e.g., USENIX Security’25 Vice Chair, IEEE EuroSP’26 Co-Chair). Labs/Teams: Leads the SysSec team at INESC-ID Lisbon, collaborating on projects like AnyTEE (TEE framework) and FlowLens (network security tool).
Yannic Noller is a Professor at the Faculty of Computer Science at Ruhr University Bochum (RUB), leading the Software Quality group. Previously, he held positions as Assistant Professor at Singapore University of Technology and Design (SUTD) and Research Assistant Professor at National University of Singapore (NUS). His research focuses on automated software engineering, including program repair, machine learning analysis, and software testing. He earned his Ph.D. from Humboldt-Universität zu Berlin under Prof. Lars Grunske, with a thesis on hybrid differential software testing. Education: Ph.D. in Computer Science (2016-2020, Humboldt-Universität), M.Sc. (2013-2016, University of Stuttgart), B.Sc. (2010-2013, University of Stuttgart). Research interests include automated program repair techniques, machine learning model analysis, and intelligent tutoring systems for programming education. Notable contributions include HyDiff (hybrid differential analysis tool) and CPR (concolic program repair). Awards include the Distinguished Artifact Reviewer at ISSTA'2021 and multiple scholarships for academic excellence. Teaching includes courses on software engineering, requirements engineering, and automated software engineering.
Prof. Dr.-Ing. Johannes Henrich Schleifenbaum is a Professor and Chair of Digital Additive Production at RWTH Aachen University, where he leads research in the Profile area Production Engineering (ProdE). His work advances additive manufacturing (AM) through interdisciplinary approaches combining materials science, process engineering, and digital technologies. His research encompasses: Laser powder bed fusion (LPBF) process optimization and defect mitigation Development of novel alloys/composites for AM applications Sustainable manufacturing practices including material recycling Integration of AI/ML for accelerated material and process design Digital tools for automated design and distributed manufacturing Recent publications (2023-2025) demonstrate a strong focus on: Multi-material processing and microstructure control Machine learning-driven alloy development Standardization and scalability of AM processes Advanced simulations for meltpool dynamics and thermal behavior Applications in aerospace, construction, and biochemical engineering He leads the Chair of Digital Additive Production, collaborating with industry partners to translate research into industrial solutions for next-generation manufacturing.
Amine Mhedhbi is an Assistant Professor at Polytechnique Montréal , where he leads the Data & AI Systems Lab . He earned his PhD in 2023 from the University of Waterloo. His work bridges data management , graph databases , and AI systems , with a focus on performance, debuggability, and user interface design for data applications. Education : PhD (University of Waterloo, 2023) Research Interests center on modern analytical data systems , including multimodal data management , language model integration , and graph query optimization . His projects like FLockMTL and GraphflowDB aim to combine semantic analysis, AI, and traditional database operations. Scientific Awards include the NSERC Discovery Grant , the Cheriton School Distinguished Dissertation Award , the VLDB Best Paper Award , and fellowships from Microsoft and Meta . Key Collaborations : Semih Salihoğlu, Jimmy Lin, Elena L. Glassman Labs & Teams : Affiliated with DAIS Lab , IVADO , and co-founded the applied research team at Distyl AI in 2023.
Paul R. Genssler is a Dr.-Ing. researcher at the Chair of AI Processor Design (AI-Pro) within the Technical University of Munich (TUM), actively advancing hardware solutions for artificial intelligence under Prof. Hussam Amrouch. His work bridges computer engineering and emerging technologies, focusing on overcoming fundamental limitations in conventional computing architectures through brain-inspired paradigms. His research spans critical domains in next-generation computing: Hyperdimensional Computing for robust pattern recognition and bioinformatics applications Neuromorphic and In-Memory Computing architectures for energy efficiency Reliability engineering for emerging memory technologies (FeFET, etc.) Quantum computing support systems including cryogenic embedded electronics Machine learning-driven transistor aging prediction and mitigation Analysis of his 15 most recent publications (2023-2024) reveals a dominant trend toward hyperdimensional computing as a unifying framework for addressing reliability challenges in emerging technologies. His work consistently integrates in-memory computing techniques to bypass von Neumann bottlenecks while targeting real-world applications like genome matching and unsupervised learning. A significant portion focuses on error-resilient implementations for unreliable nanoscale devices, demonstrating exceptional cross-stack expertise from transistor physics to algorithm design. As a core member of TUM's AI Processor Design group affiliated with the Munich Institute of Robotics and Machine Intelligence (MIRMI), Genssler collaborates extensively on projects spanning cryogenic quantum control systems, FPGA-based AI resilience, and monolithic 3D integration. The team operates at the intersection of semiconductor physics, computer architecture, and machine learning, with strong industry connections evident through publications at DATE, ASP-DAC, and ICCAD.
Kai Leonhard is an Adjunct Professor at the Chair of Technical Thermodynamics , RWTH Aachen University. His research focuses on computational chemistry, thermodynamics, and molecular modeling, particularly in solvent design and reactive chemical processes. Department: Chair of Technical Thermodynamics Email: kai.leonhard@ltt.rwth-aachen.de Prof. Leonhard's work integrates quantum chemistry with computer-aided molecular and process design (CAMD/CAPD), emphasizing solvation thermodynamics, reaction kinetics, and machine learning applications. His projects span biofuel combustion, microgel synthesis, and sustainable solvent development. Recent publications highlight advancements in COSMO-RS-based solvent screening, reaction network exploration via ChemTraYzer-TAD, and multi-fidelity modeling for partition coefficients. He employs machine learning to enhance predictive thermodynamic models and optimize chemical processes.
Prof. Dr. Ulrich Frank is a full Professor of Business Information Systems and Enterprise Modeling at the University of Duisburg-Essen, Faculty of Computer Science. He serves as Director of IS:link, an international student exchange network he founded. His academic career spans multiple prestigious institutions including University of Mannheim, GMD, IBM Almaden Research Center, University of Koblenz-Landau, and University of Duisburg-Essen since 2004. His educational background includes: Business Administration studies (minor in Applied Computer Science) at University of Cologne Doctorate in Political Science from University of Mannheim (1986) Habilitation at University of Marburg (1993) Prof. Frank's research focuses on multi-perspective enterprise modeling, with particular emphasis on object-oriented and multi-level modeling approaches. His work bridges business administration and computer science, exploring conceptual modeling, knowledge management systems, business process reorganization, and the theoretical foundations of business informatics. He has made significant contributions to modeling languages like FMMLx and XModelerML, advancing multi-level modeling techniques for enterprise information systems. His recent publications demonstrate a strong focus on multi-level modeling languages, with particular attention to UML extensions, language engineering, and the application of large language models in systems engineering. The research trajectory shows consistent development of modeling frameworks that support enterprise architecture, business process modeling, and domain-specific language design, with increasing attention to AI-assisted modeling approaches in recent years. Prof. Frank has received significant recognition through editorial positions at leading journals: Co-Editor of Enterprise Modelling and Information Systems Architectures Co-Editor of Business & Information Systems Engineering Co-Editor of Information Systems and E-Business Management Co-Editor of Software and Systems Modeling Member of the Standing Committee of the European Conference on Information Systems (ECIS) Prof. Frank has served in numerous academic leadership roles including as Chairman of the Examination Board for Business Information Systems, member of the Research Commission at University of Duisburg-Essen, and Appointment Commissioner (2021-2023). He has reviewed for major funding organizations including the German Research Foundation, Federal Ministry of Education and Research, and Swiss National Science Foundation. As founder and senior consultant of IS:link, Prof. Frank leads an international student exchange network connecting universities worldwide. His research group focuses on multi-perspective enterprise modeling (MEMO), developing frameworks and tools for business information systems design and implementation.
Johannes Schöning is a Professor of Human-Computer Interaction (HCI) at the University of St. Gallen and leads the Ubiquitous Media Technology Lab . His research focuses on developing user interfaces that empower individuals and communities through data-driven decision-making, with interdisciplinary applications in geographic information science, public health, medical contexts, and extreme environments like space missions. He emphasizes methodological rigor from AI, computer graphics, and cognitive psychology. Organizes AlpCHI 2026 Chair of ACM Eugene Lawler Award Committee Editorial Board, AI Perspectives (Springer Nature) Research Trends : His publications from 2025–2024 reveal a focus on Mixed Reality (autoethnography, weight perception), Accessibility (visual impairment support), Environmental HCI (CO2 eco-feedback), and Geospatial Technologies (navigation externalities, map analytics). Interdisciplinary work appears in journals like Nature and PLOS ONE . Scientific Recognition : ACM Distinguished Member Best Paper & Accessibility Awards (Interact 2019, MobileHCI 2015) Junior Fellow, Gesellschaft für Informatik (2013) Academic Service : Active in conference leadership (SIGCHI Switzerland Chair 2021–2023, ISS 2016 Program Chair) and reviewing for top venues including ACM CHI , Ubicomp , and IEEE VR . Regular reviewer for European science foundations and DFG/BMBF proposals.
Prof. Dr. Matthias Weidlich is a faculty member at Humboldt University of Berlin within the Institute of Computer Science under the Faculty of Mathematics and Natural Sciences . His research focuses on Process Mining , Complex Event Processing , and Data Privacy with applications in Business Process Management and Scientific Workflows . Research Interests: Business Process Management and Process Mining Complex Event Processing and Stream Data Analysis Data Privacy and Security in Process Systems Scientific Workflow Systems and User Behavior Heterogeneous Network Embeddings Algorithm Design and Optimization Recent Publications (2023-2025) demonstrate expertise in: Efficient stream processing techniques Privacy-preserving process mining frameworks Scientific workflow analysis tools Graph neural network applications Multi-modal data integration Adaptive querying systems Contact: Office: Unter den Linden 6, 10099 Berlin Phone: 030 2093-41277 Email: matthias.weidlich@hu-berlin.de Web: hu.berlin/data
