Vlad-Costin Andrei is a Researcher at the Chair of Theoretical Information Technology , Technical University of Munich (TUM), specializing in wireless communication systems and digital twinning. He joined the ACES Lab (TUM's Chair of Theoretical Information Technology) in late 2021 after 3.5 years in the aerospace and defense industry. Research Focus: Joint Communications and Sensing (6G), Neuromorphic PHY Layer, Digital Twins, MIMO-OFDM Resilience Projects: 6G-life, 6G Future Lab Affiliation: ACES Lab, TUM His work bridges theoretical foundations with practical implementations, including demonstrations of digital twinning platforms and sensing-assisted receivers. Recent publications emphasize anti-jamming frameworks, federated learning over wireless networks, and trajectory optimization for UAV-enabled ISAC systems. Scientific Awards: Best Paper Award, IEEE Symposium on Joint Communications and Sensing (2023) His research is supported by third-party grants such as BMBF's 6G-life, DFG's Gottfried Wilhelm Leibniz Prize, and multiple collaborative projects.
Artem Barger is a researcher specializing in blockchain technology, distributed systems, and database optimization. With affiliations primarily in blockchain development and academic research, he has contributed extensively to Hyperledger Fabric enhancements and decentralized information systems. Research Interests Optimizing state databases for blockchain platforms Byzantine Fault Tolerance in distributed networks Permissioned blockchain architectures Tokenization of real-world assets AI applications in soft skills evaluation Recent Publications Barger's work focuses on improving blockchain scalability and security through techniques like certification blocks, Patricia Merkle tries, and verifiable randomness. He has also explored tokenization applications in charity and energy sectors.
Ulrike Michaela Meyer is a Professor in the Department of IT Security at RWTH Aachen University. Her research focuses on privacy-preserving technologies, cybersecurity, and machine learning applications in domain generation algorithm (DGA) detection and healthcare systems. Recent Research Trends: In 2024, her work emphasized cryptographic methods for secure data applications, including privacy-preserving kidney exchange protocols and network flow analysis. She also explored robust machine learning frameworks for DGA classification and transfer learning approaches to improve domain detection accuracy. Contact: Email: meyer@itsec.rwth-aachen.de
Thomas Kudraß serves as Professor for Database Systems at the Faculty of Computer Science, Mathematics and Natural Sciences (IMN) at Leipzig University of Applied Sciences (HTWK Leipzig). With over 25 years of academic and industry experience, he teaches courses including Database Basics, Database Application Programming, Data Warehousing, and Big Data Technologies. His leadership roles include Internship Coordinator for Computer Science since 2001, Member of the Academic Senate since 2011, and Contact person for the IMN faculty alumni association since 2005. Dr. Kudraß earned his Dipl.-Ing. in Computer Science from Technical University of Dresden (1985-1990) and completed his doctorate at Darmstadt University of Technology (1992-1997), where his dissertation received the prestigious Jos Schepens Memorial Award. Prior to academia, he worked as an Information Systems Architect at UBS AG Zurich and Database Specialist at Swiss Bank Corporation Basel. His research spans database technologies with focus on heterogeneous database integration, data quality, data privacy, and modern database concepts. Recent work explores cloud-native billing applications for 5G, NoSQL databases, and privacy-preserving record linkage techniques. His publication record shows continuous evolution from traditional database systems to contemporary challenges in big data and distributed systems. Jos Schepens Memorial Award for doctoral dissertation (1997) Active participation in German Informatics Society (GI) specialist groups Organizer of multiple database-related workshops and conferences Professor Kudraß has significantly contributed to academic governance as E-Learning Officer (2000-02), Faculty Council member (2009-12), and liaison lecturer for the German Society for Computer Science (2001-14). He has coordinated numerous student projects and served on program committees for major conferences including INFORMATIK 2017 and BTW series.
Prof. Dr. Andreas Bulling is Full Professor of Computer Science at the University of Stuttgart , leading the Collaborative Artificial Intelligence research group at the Institute for Visualization and Interactive Systems. He is also a founding director of the Stuttgart ELLIS Unit and serves on multiple prestigious boards including IEEE Transactions on Visualization and Computer Graphics . Education: MSc in Computer Science (KIT), PhD in Information Technology (ETH Zurich) Research Interests: Human-Computer Interaction, Eye Tracking, Wearable Computing, Computer Vision, and Privacy-Preserving AI Scientific Leadership: UbiComp Steering Committee member, ACM ETRA General Chair (2020), and extensive editorial/guest editor roles Key Awards: ERC Starting Grant (2018), Henriette Herz Scout (2024), and multiple best paper awards at CHI, ETRA, and UIST Technical Contributions: Developed datasets (LPW, VisRecall++), created novel methods for gaze estimation, mental face reconstruction, and saliency prediction in visualizations His work bridges AI and Human-Computer Interaction with applications in immersive systems and healthcare technologies.
Prof. Georg Neugebauer is a Professor at RWTH Aachen University, specializing in cybersecurity, privacy-preserving protocols, and secure multi-party computation. His research focuses on developing frameworks for secure data reconciliation, enhancing information security management systems, and addressing cybersecurity challenges in AI, industrial systems, and smart environments. Research Interests: Secure Multi-Party Computation (MPC) Privacy-Preserving Systems Cybersecurity Education & Training Artificial Intelligence in Security Management Industrial IoT and Operational Technology (OT) Security Digital Forensics and Incident Response Recent work highlights a shift towards cybersecurity education initiatives (e.g., CampusQuest ), AI-driven security solutions, and addressing vulnerabilities in public AI tools. His frameworks like SMC-MuSe have advanced MPC applications for multi-set operations. His publications span conferences such as ARES, ICISSP, and AHFE, addressing topics from smart building protocol security to forensic triage tools. Collaboration with researchers like Schuba, Höner, and Meyer marks his interdisciplinary approach to solving real-world security challenges.
Roman Vaculín is a Researcher at IBM Research, focusing on interdisciplinary domains where artificial intelligence, blockchain technologies, and data-centric workflows converge. His work spans automated machine learning, time series analysis, and secure computation via cryptographic methods like homomorphic encryption. Affiliation: IBM Research Key research areas: Time Series Analysis, Blockchain, AI Explainability, Business Process Management Across his publications, Vaculín explores: Time Series Modeling: Developing robust frameworks like TsSHAP and end-to-end architectures for forecasting and imputation. Blockchain Applications: Designing trusted AI systems, secure multi-party computation, and verifiable simulations. Automated Machine Learning: Creating toolkits for industrial AI explainability and automation. Privacy-preserving Techniques: Optimizing encrypted inference and secure decision tree protocols. His methodology often integrates formal verification with practical implementations, emphasizing efficiency and interpretability in complex systems. While no formal awards or students are documented in the provided data, his collaborative publications with institutions like IBM Research and academic partners highlight his role in advancing applied AI research.
Hannah Keller is a researcher in the field of cryptography and privacy-preserving technologies. Her work focuses on secure multi-party computation (MPC), differential privacy, and post-quantum cryptography. She has collaborated with institutions on topics such as privacy-preserving aggregation, secure noise sampling, and cryptographic protocols. Notable contributions include research on lattice-based cryptography in PQCrypto 2025 and differential privacy in distributed systems. Her publications address challenges in balancing privacy with computational efficiency in machine learning and data analysis.
Akram Y. Sarhan is a prolific researcher with a focus on cybersecurity , data security , and algorithm design for communication and logistics systems. He has published extensively in PeerJ Comput. Sci. and IEEE Access , addressing challenges in privacy-preserving protocols , blockchain applications , and secure data dissemination under constraints. Key research areas include reinforcement learning , network security , and decentralized systems . His work spans crisis response data management , drone logistics optimization , and blockchain-based identity solutions . Notable trends in his publications involve secure communication protocols for RIS-enabled systems, agent-based health passport frameworks , and heuristic scheduling algorithms for warehouses and networks.
Udaya Kiran Tupakula is an Associate Professor in the Department of Computing within the School of Computer, Data and Mathematical Sciences at Western Sydney University. With over two decades of research experience, his work focuses on cybersecurity with particular emphasis on network security, cloud infrastructure protection, and emerging technologies. He has established himself as a leading researcher through extensive collaborations, particularly with Professor Vijay Varadharajan, and has contributed significantly to securing virtualized environments and next-generation networks. Dr. Tupakula's research spans multiple critical areas in cybersecurity. His early work focused on fundamental security challenges like DDoS mitigation and TCP SYN flood attacks, evolving into more complex domains including Software Defined Networking security, cloud security architectures, and IoT protection. Recent research directions include AI security, privacy-preserving machine learning, and securing 5G infrastructure. His approach combines theoretical frameworks with practical implementations, often developing novel security architectures that address specific vulnerabilities in emerging technologies. Analysis of his publication trends shows a clear progression from traditional network security toward more sophisticated systems. His work has increasingly incorporated machine learning techniques for intrusion detection, expanded into industrial control systems security, and recently embraced AI safety challenges. The breadth of his research demonstrates adaptability to evolving security landscapes while maintaining focus on practical, implementable solutions rather than purely theoretical approaches. Dr. Tupakula has mentored numerous researchers who have become active contributors in the cybersecurity field. His collaborative approach is evident in the extensive co-authorship network spanning multiple institutions and countries. His research has been supported by various grants focusing on critical infrastructure protection, cloud security, and next-generation network security. His laboratory work focuses on practical security testing environments for SDN, cloud infrastructure, and IoT systems. Current research directions include securing AI systems from malicious use, enhancing privacy in federated learning environments, and developing more robust security frameworks for industrial control systems. The applied nature of his research ensures close alignment with real-world security challenges faced by industry and government organizations.
Jan Pennekamp is a postdoctoral researcher at the Chair of Communication and Distributed Systems (COMSYS) within the Department of Computer Science at RWTH Aachen University. He is a member of the Security and Privacy research group and is actively involved in the Cluster of Excellence 'Internet of Production,' where he serves as deputy workstream coordinator. His academic journey includes a B.Sc. and M.Sc. in Computer Science from RWTH Aachen, with exchange studies at Aalto University and an internship at the University of Luxembourg. His research interests center on security and privacy in the Industrial Internet of Things (IIoT), privacy-enhancing technologies (PETs), secure computation, and interdisciplinary applications in healthcare, particularly synthetic data and single-cell genomics. He has led and contributed to numerous research projects, including CALCIPROTECT, RFC, RUST, SUSTAINET-guardian, and SYNCLIVER. His methodological focus includes both technical innovation and rigorous evaluation in real-world scenarios. His recent publications demonstrate a strong trend toward privacy-preserving data sharing in industrial and healthcare contexts, leveraging techniques such as confidential computing, blockchain, and machine learning. His work bridges computer science with industrial engineering and clinical research, emphasizing secure, interoperable, and accountable data ecosystems. Scientific Awards: Klaus Tschira Boost Fund Fellow 2025 Attendee of the 12th Heidelberg Laureate Forum 2025 Young Researcher Award 2022, Cluster of Excellence Internet of Production ICT Young Researcher Award 2021 TDWI Award 2018 (best master thesis) Google Scholarship 2018 Finalist, Artifacts Competition and Impact Award at ACSAC 2022 Outstanding Reviewer Award, TheWebConf/WWW 2024 Jan has advised numerous B.Sc. and M.Sc. theses on topics ranging from privacy-preserving benchmarking to intrusion detection and blockchain-based accountability. He has received research stipends and travel grants from RWC and ACSAC and has held leadership roles in academic service, including organizing conferences and serving on program committees for top venues like IEEE S&P, CCS, and EuroS&P. He is also a Certified ScrumMaster and RWTH Research Manager, reflecting his strong project and team leadership skills. He is actively engaged in interdisciplinary research labs and teams, particularly within the 'Internet of Production' initiative, collaborating with industrial partners and medical researchers to develop secure and privacy-preserving information systems for real-world applications.
Andreas Dengel is a Professor of Computer Science at the Rhineland-Palatinate University of Technology (RPTU) and Managing Director of the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern. He leads the Smart Data & Knowledge Services research area and the DFKI Deep Learning Competence Center, with additional professorship rights at Osaka Metropolitan University since 2009. He earned his doctorate in Computer Science from the University of Stuttgart in 1989 following undergraduate studies in Computer Science with Economics at RPTU (then TU Kaiserslautern). His academic promotions progressed from C3 to W3 Professor at RPTU between 1993-2013. Professor Dengel's research centers on Machine Learning and Pattern Recognition with applications in Earth Observation, document analysis, and semantic technologies. His work bridges theoretical AI with industrial implementation, notably through NVIDIA-certified deep learning frameworks and multi-institutional Earth Observation projects. He pioneered quantified learning approaches for satellite data interpretation and human behavior modeling. Recent publications (2025) demonstrate his focus on diffusion models for geospatial data, multi-view learning for missing Earth Observation data, and gaze-based confidence detection systems. These works highlight cross-disciplinary integration of deep learning with environmental science and human-computer interaction. His extensive honors include: ICDAR Outstanding Achievement Award (2019) Order of the Rising Sun with Gold Rays (2021) Order of Merit of Rhineland-Palatinate (2022) IAPR Fellow distinction NVIDIA Pioneer Award He has secured over €100 million in third-party funding and supervised 500+ theses. His MIND graduate school supports 20 industry-sponsored students, while international exchange programs with Japanese institutions foster cross-border research. As FFPA Chairman and acatech member, he shapes national AI policy including Germany's "Lernende Systeme" platform. His leadership spans DFKI's Deep Learning Competence Center (NVIDIA Excellence Program awardee) and research groups developing Robust Machine Learning for defense systems, Ageing Smart environments, and Earth Digital Twin technologies through EU and national projects.
Divya Ravi is an Assistant Professor in the Theory of Computer Science (TCS) Group at the University of Amsterdam, Netherlands, and a former post-doctoral researcher in the Cryptography and Security Group at Aarhus University, Denmark. Education: PhD in Computer Science from Indian Institute of Science (IISc), Bangalore, India, supervised by Dr. Arpita Patra M.E (Masters) in Computer Science from Computer Science and Automation (CSA), IISc B.E (Bachelors) in Computer Science from BITS Pilani, Hyderabad Campus, India Research Interests: Secure Multi-party Computation Distributed Computing Her work focuses on the feasibility of secure multiparty computation tasks and efficient distributed computing protocol design under varying network and computational models. No scientific awards or advisees are listed in the provided text.
Sen Chen is a Professor at Nankai University, holding positions in both the College of Cryptology and Cyber Science and the College of Computer Science. He leads the Nankai Software Security Laboratory (NKSSecLab) and is a member of Professor Zheli Liu's research group. Previously, he served as a tenured associate professor and research professor at Tianjin University (2021-2024), and as a research assistant professor at Nanyang Technological University (NTU), Singapore. Dr. Chen's research focuses on software security and software supply chain security, with particular emphasis on vulnerability analysis and malware detection. His work spans multiple domains including mobile security, AI security, open-source security, and intelligent software development and testing. His research has led to significant contributions in automated security vulnerability detection, software composition analysis, and security tool development for various platforms including Android, Java, and blockchain systems. Analysis of Dr. Chen's recent publications (2023-2025) reveals a strong focus on software supply chain security, with particular attention to vulnerability detection and remediation in open-source ecosystems. His work demonstrates expertise in applying advanced machine learning techniques to security problems, especially in the context of Android applications and containerized environments. There's a clear trajectory toward addressing emerging challenges in AI security and large language model supply chains, reflecting his ability to adapt research directions to evolving technological landscapes. ACM SIGSOFT Distinguished Paper Award (FSE 2024) ACM SIGSOFT Distinguished Paper Award (ASE 2023) First Place of the 13th Challenge Cup China College Students' Entrepreneurship Competition ACM SIGSOFT Distinguished Paper Award (ICSE 2023) Prototype Research Tool Award 2nd Place (Freestyle) in CCF ChinaSoft 2022 First Place of The 8th China International College Students' 'Internet+' Innovation and Entrepreneurship Competition ACM SIGSOFT Distinguished Paper Award (ASE 2022) ACM China Rising Star Award (ACM Tianjin Council) ACM SIGSOFT Distinguished Paper Award (ICSE 2021) First Class of Progress of Science and Technology Prize of Tianjin, 2020 Dr. Chen has successfully secured funding from multiple prestigious sources including key R&D programs, general and pre-research projects of the National Natural Science Foundation of China, and the Populus euphratica Forest Fund. His theoretical research has been applied by major companies such as State Grid, China Automotive Industry Corporation, and Huawei. He has mentored students to win national gold medals in both the 'Internet Plus' and Challenge Cup programs, demonstrating his commitment to student development and practical application of research. Dr. Chen leads NKSSecLab (Nankai Software Security Laboratory), which focuses on cutting-edge research in software security and supply chain security. The lab has developed several notable tools including SCTruster (a digital trust chain platform for software supply chain security) and LiDetector. The lab maintains strong international collaborations with institutions like Nanyang Technological University in Singapore and has established itself as a leading research group in software security within China.
Volker Lindenstruth is a Senior Fellow at FIAS and Full Professor of Computer Science at Goethe University Frankfurt. He leads the Architecture of High-Performance Computing research group, focusing on energy-efficient architectures, distributed systems, and applications in particle physics. His work spans high-performance computing (HPC) for experiments at CERN and FAIR, including the ALICE and CBM projects. He also contributes to the CMMS initiative for multi-scale biological modeling. Education : Studied physics at TU Darmstadt (diploma 1989), PhD in physics at GSI Darmstadt (1993). Postdoc as Feodor v. Lynen Fellow at LBNL, USA (1993–1995). Research : Specializes in HPC architecture for nuclear physics experiments, GPU-based real-time data processing, and cloud computing. Key projects include the ALICE HLT system and the LOEWE-CSC supercomputer. Collaborates with CERN, FAIR, and industrial partners like e3c Computing GmbH. Awards : World-ranking efficiency award (2014), German Rechenzentrumspreis (2012), Green-IT Best Practise (2011). Secured >35M€ in third-party funding since 2010. Leadership : Chair of FIAS Board (2012–present), Director of Scientific IT at GSI Helmholtzzentrum (2010–present), former head of Technical Computer Science at Heidelberg University (until 2009).
