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Prof. Dr.-Ing. Klaus Diepold is a Full Professor and Chair of Data Processing at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He earned his PhD in Electrical Engineering from TUM in 1992. His research focuses on machine learning, multimedia signal processing, moral machines, and affect modeling for cognitive systems. He actively promotes entrepreneurship through the Center for Digital Technology and Management (CDTM). Academic Career: Diepold held visiting professorships at the University of Alberta, Canada, and NICTA, Australia. He served as Dean of Studies at TUM (2005–2010) and Senior Vice President for Diversity and Talent Management (2013–2015). He has contributed to MPEG standards and led projects in telepresence (SFB-453) and cognitive systems (CoTeSys). Research Interests: His work spans ethical AI, reinforcement learning, biomedical applications of machine learning, and interdisciplinary education. Notable projects include CellFace, Sirrel, and the development of AI-driven diagnostic tools using digital holographic microscopy. Awards: He has received the TUM Teaching Prize (2004), Fellowship for Innovation in University Teaching (2012), and the 2024 IEEE German Education Conference Best Paper Award. His work on 'Komputer & Creativität' highlights innovative teaching at the intersection of engineering, AI, and theater. Labs & Teams: His team includes researchers like Sven Gronauer and Alice Hein, focusing on projects like GenAI chatbot agents, clinical applications of computational medicine, and cybersecurity. The Chair of Data Processing collaborates with industry partners like fortiss and Rohde & Schwarz.

• Machine Learning
• Signal Processing
• Ethical AI
• +2 more

Farinaz Koushanfar is a Professor in the Electrical Engineering and Computer Science department at the University of Michigan's College of Engineering. With an impressive h-index of 65 and over 27,000 citations, she has established herself as a leading researcher in hardware security and privacy-preserving computing. Her research interests span hardware security, integrated circuit protection, physical unclonable functions, hardware trojans, logic locking, and privacy-preserving machine learning. Koushanfar's work addresses critical security challenges throughout the semiconductor supply chain, developing innovative solutions for IP protection, anti-counterfeiting, and secure hardware design. Analysis of her recent publications reveals a strong focus on the intersection of hardware security and machine learning, particularly in federated learning security and privacy-preserving deep neural network inference. Her research shows a clear trajectory from foundational hardware security mechanisms toward more complex systems-level security challenges in emerging computing paradigms. Throughout her career, Koushanfar has made significant contributions to both theoretical frameworks and practical implementations in hardware security, with numerous highly influential publications that have shaped the field.

• Hardware Security
• Integrated Circuit Security
• Physical Unclonable Functions
• +5 more

Stanley B. Zdonik is a Professor at Brown University , Providence, USA. His research focuses on Database Systems , Stream Processing , and Data Management , with recent work addressing challenges in Observability , Encrypted Databases , and Time-Series Anomaly Detection . He has contributed to systems like Borealis, C-Store, and SciDB, emphasizing scalability and real-time processing. Key themes in his work include Stream-Oriented Query Languages , High-Frequency Telemetry , and Transactional Systems . His 15 most recent publications span topics in Polystore Architectures , Database Encryption , and Interactive Data Exploration , reflecting a career-long commitment to advancing database theory and practice. As a principal investigator, Zdonik has collaborated with leading researchers like Michael Stonebraker , Nesime Tatbul , and Ugur Çetintemel . His work often intersects with Distributed Systems , Uncertainty Modeling , and Performance Optimization .

• Database Systems
• Stream Processing
• Data Management
• +3 more

Lukas Beierlieb is a Researcher at the Chair of Software Engineering (Computer Science II) under Prof. Samuel Kounev at the University of Würzburg. He holds a Bachelor of Science in Computer Science (2018-2020) and a Master of Science in Computer Science (2020-2022) from the same institution. His research focuses on cryptography, cybersecurity, and privacy-preserving technologies, with notable contributions to homomorphic encryption, secure communication protocols, and performance analysis of cryptographic systems. Research Interests: Homomorphic Encryption and its applications Secure group communication mechanisms Privacy-preserving machine learning techniques Performance benchmarking of cryptographic algorithms He has been awarded the Würzburg Software Engineering Award 2022 for his Bachelor Thesis and holds scholarships from the Studienstiftung des deutschen Volkes. His work frequently appears in journals like the International Journal of Information Security and conferences such as ARES.

• Cryptography
• Cybersecurity
• Secure Communication
• +3 more

Thomas Prantl is affiliated with the Chair of Computer Science II (Software Engineering) at the University of Würzburg, located in Würzburg, Germany. He contributes to both research and teaching within the Department of Computer Science, focusing on cybersecurity, homomorphic encryption, IoT security, and machine learning applications. His work integrates technical innovation with educational methodologies, such as designing lectures on advanced cryptographic techniques. Research Interests: His primary research areas include homomorphic encryption schemes, secure group communication, performance evaluation of cryptographic systems, and applying machine learning to remote sensing and medical data. He actively explores interdisciplinary topics like blockchain technology and IoT security challenges. Teaching and Mentorship: Prantl coordinates exercises for the bachelor’s course 'Softwaretechnik' and supervises students in software engineering projects (Softwarepraktikum). He also contributes to curriculum development through seminar topic provision and student mentorship. Recent Contributions: Key publications in 2025 focus on homomorphic encryption pedagogy, privacy-preserving CNN applications in medicine, and CKKS cryptosystem optimizations. Earlier work (2024) addressed earth observation product evaluation using CNNs and security analysis of attribute-based encryption schemes. Professional Engagement: He has served as Web Chair for ACM/SPEC ICPE 2022, PC member for Cyber Resilience and Antifragility (CyRA) 2021, and Publicity Chair for Self-Aware Computing (SEAC) 2020. His academic qualifications include a Master of Computer Science and teaching certification in Physics/Computer Science.

• Cybersecurity
• Homomorphic Encryption
• IoT Security
• +3 more

Dr. Jan Henrik Ziegeldorf is a researcher at RWTH Aachen University, affiliated with the Institute for Energy Efficient Buildings and Indoor Climate at the EON Energy Research Center (EONERC) and the Communication Systems Group (COMSYS) within the Department of Computer Science. He holds a PhD in Computer Science from RWTH Aachen, earned in 2018, and has been contributing to research in data security and privacy since 2017, initially part-time and then full-time. His research focuses on data security, privacy-preserving technologies, and cryptographic protocols , with applications in blockchain, IoT, cloud computing, and genomics. He has made significant contributions to secure multi-party computation, differential privacy, anonymization, and homomorphic encryption. His work bridges theoretical cryptography with practical system implementations, particularly in constrained environments and decentralized architectures. The recent publications reflect a strong trend in privacy-preserving machine learning, secure blockchain systems, and genomic data protection . These works demonstrate expertise in designing frameworks (e.g., SHIELD, BLOOM) that enable secure computation while maintaining performance, especially in outsourced and distributed settings. His research often targets real-world privacy threats in cloud storage, mobile apps, and decentralized networks. Univention Graduate Award (3rd place) for the best thesis in 2016 FOLKS Award for the best Master's thesis in 2015 Dr. Ziegeldorf has actively advised numerous Master’s and Bachelor’s students on topics ranging from secure localization to blockchain privacy and differential privacy. His service includes program committee roles for IEEE Blockchain conferences and peer review for top-tier journals such as IEEE, ACM, and Elsevier publications. He is involved in collaborative research within the COMSYS group and contributes to interdisciplinary projects linking energy systems, networking, and privacy. He is part of the COMSYS research team at RWTH Aachen, which focuses on communication systems, security, and privacy in networked environments. The group works on both foundational and applied aspects of secure and efficient digital systems, with strong industry and academic collaborations.

• Data Security
• Privacy-Preserving Technologies
• Cryptographic Protocols
• +12 more

Christian Weinert is a researcher affiliated with the Technical University of Darmstadt, specializing in cryptography and computer security. He completed his PhD in 2021 with a dissertation on practical private set intersection protocols for privacy-preserving applications. His research focuses on: Privacy-preserving technologies including private set operations and contact discovery Secure computation frameworks for machine learning applications Cryptographic protocol design and optimization Mobile security and authentication systems Long-term data protection strategies Weinert's publications demonstrate a consistent focus on practical implementations of cryptographic systems, with recent work emphasizing scalable privacy solutions for federated learning, efficient private information retrieval, and secure computation techniques. His research often bridges theoretical cryptography with real-world applications in systems security.

• Cryptography
• Computer Security
• Privacy
• +3 more

M. Sadegh Riazi is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego's Jacobs School of Engineering. He completed his PhD at UCSD in 2020 with a dissertation titled 'Towards A Private New World: Algorithm, Protocol, and Hardware Co-Design for Large-Scale Secure Computation.' Dr. Riazi's research focuses on the critical intersection of cryptography, machine learning, and hardware security, with particular emphasis on making secure computation practical for real-world applications. His work spans secure multi-party computation, homomorphic encryption, privacy-preserving machine learning, and hardware acceleration for cryptographic protocols. He has made significant contributions to optimizing secure computation frameworks for deep learning applications, developing techniques that balance security guarantees with computational efficiency. His publication record shows a clear progression from foundational secure computation techniques to increasingly sophisticated applications in privacy-preserving AI. Notably, his work on HEAX (Homomorphic Encryption Acceleration) and XONN (XNOR-based Oblivious Neural Network) demonstrates practical approaches to making encrypted deep learning feasible. The research trends in his publications indicate a consistent focus on bridging theoretical cryptographic security with practical system implementations. Dr. Riazi has established a strong collaborative network, most prominently with Professor Farinaz Koushanfar's research group at UCSD, with whom he has co-authored numerous papers across multiple domains including biometric security, secure hardware design, and privacy-preserving machine learning. His research has been published in top-tier venues including IEEE Security & Privacy, USENIX Security, ASPLOS, and CCS, reflecting the high impact and quality of his work in both the security and systems communities.

• Secure Computation
• Privacy-Preserving Machine Learning
• Homomorphic Encryption
• +4 more

Zhu Han is a Professor at the University of Houston, TX, USA, with a PhD from the University of Maryland, College Park. He is affiliated with the Chinese Academy of Sciences' Aerospace Information Research Institute in Beijing, China. His research spans wireless networks, 6G communication, and machine learning applications in telecommunications. Key areas: Semantic communication, UAV networks, IoT security, quantum networking. Recent publications focus on federated learning, optical IRS for VLC, and resource allocation in LEO satellite systems. His work integrates AI with network security and edge computing. He collaborates extensively on topics like reconfigurable intelligent surfaces, quantum-assisted optimization, and privacy-preserving protocols. No specific awards or student details are provided in the dataset.

• Wireless Networks
• Machine Learning
• 6G Communication
• +4 more

Ralf Küsters is a Professor at the University of Stuttgart , leading the Institute of Information Security . His research focuses on cryptographic protocols, formal verification, and privacy-enhancing technologies. Research Trends: His recent work addresses critical challenges in e-voting systems, including bulletin board vulnerabilities and zero-knowledge proofs for ballot validity , as well as accountability mechanisms for distributed systems. He also investigates security analysis of web authentication standards like OpenID FAPI 2.0 and develops efficient multi-party computation (MPC) protocols (e.g., Multipars) with reduced communication overhead. Scientific Contributions: Küsters has formalized security models for web infrastructure , exposed attacks on global standards (e.g., audience injection attacks ), and designed provably secure implementations for cryptographic building blocks. His collaborations span institutions in Germany, Australia, and Brazil, with applications in open banking, healthcare, and secure multi-party computation. Technical Leadership: As head of the Institute of Information Security, he drives research on privacy-preserving technologies , secure data aggregation , and standardization-compliant security analysis , bridging theoretical cryptography with practical implementations.

• Cryptography
• Information Security
• Formal Verification
• +3 more

Lukas Horn is a Researcher at the Chair of Software Engineering (Informatik II) at the University of Würzburg under Prof. Samuel Kounev. He holds a Master of Science and Bachelor of Science in Computer Science from the same institution. His research focuses on homomorphic encryption, secure multiparty computation, and privacy-preserving machine learning, with applications in cryptography and performance benchmarking. Education: Master of Science in Computer Science (Oct 2020 - Mar 2025) Bachelor of Science in Computer Science (Oct 2019 - Feb 2023) Research Interests: Design and implementation of homomorphic encryption systems (e.g., CKKS) Secure group communication protocols for IoT Performance analysis of cryptographic algorithms Privacy-preserving machine learning techniques Awards & Scholarships: Würzburg Software Engineering Award 2022 (Best Bachelor Thesis) Exposé Stipendium (Studienstiftung, 2025) Long-term scholarship from Studienstiftung (2021–2025) Teaching Contributions: Lecturer for Systems Benchmarking (SS25) Seminar leader for homomorphic encryption applications (SS25) Teaching Assistant for Fundamentals of Programming, Software Technology, and Theoretical Computer Science Labs & Projects: Active contributor to the Secure Software Systems Group, focusing on interdisciplinary cybersecurity research and education initiatives.

• Homomorphic Encryption
• Secure Multiparty Computation
• Functional Encryption
• +3 more

Daniel Hölker is a researcher at the Department of Engineering and Computer Science at Osnabrück University of Applied Sciences , specializing in Smart Grid technologies and energy systems. His work focuses on distributed energy management, communication technologies for grid optimization, and privacy-preserving algorithms in decentralized systems. Faculty: School of Engineering and Computer Science Academic Rank: Researcher His research interests include: Intelligent energy distribution in Smart Grids Communication protocols for grid stability Algorithms for energy consumption adjustment Virtual power plants and household load shifting Smart metering and remote monitoring systems Daniel’s publications (2012–2018) emphasize distributed algorithms, LTE integration, and privacy-preserving techniques in Smart Grids. Key themes include energy network simulation (SiENA framework), homomorphic encryption for secure load adaptation, and clustering strategies for scalable grid management. His work bridges theoretical algorithms with practical implementation challenges in real-world energy systems.

• Intelligent energy distribution (smart grid)
• Communication in the Smart Grid
• Algorithms for adjusting energy consumption
• +4 more

Carina Heßeling serves as a Lecturer at FernUniversität Hagen's Faculty of Mathematics and Computer Science since August 2023, following the completion of her doctoral degree in Computer Science at the same institution. Her academic journey reflects deep institutional continuity through multiple research and teaching roles. Her educational foundation includes: Bachelor of Science in Computer Science, FernUniversität Hagen (2010-2017) Master of Science in Computer Science, FernUniversität Hagen (2017) PhD in Computer Science (Dr.Ing.), FernUniversität Hagen (2023) Dr. Heßeling's research program centers on advanced cybersecurity mechanisms , with specialized expertise in covert channel engineering and steganographic techniques for sensor networks. Her work pioneers novel approaches to data hiding within numeric representations and sensor transmissions, addressing critical vulnerabilities in network security protocols through mathematical innovation in floating-point arithmetic and data stream manipulation. Analysis of her eight publications (2022-2024) reveals concentrated investigation into covert channel bandwidth optimization, robustness against detection, and exploitation of numerical representation redundancies. The research demonstrates consistent methodological focus on sensor data systems while spanning cryptographic theory, network protocol analysis, and information-theoretic security frameworks. No scientific awards or honors are documented in available sources. Current information indicates no formal student advisement responsibilities or externally funded research grants. Professional activities appear centered on teaching duties and independent research within the university's cybersecurity research ecosystem.

• Cybersecurity
• Network Security
• Steganography
• +3 more

Derian Boer is a Researcher at the Institute of Computer Science at Johannes Gutenberg University Mainz. Specializing in knowledge graphs, secure multi-party computing, and machine learning applications in life sciences/environmental protection. Current research focuses on curATime - curATech projects. Education: Master's in Computer Science (2016-2018), Johannes Gutenberg University Mainz Bachelor's in Business Informatics (2013-2016), Rhine-Main University of Corporate Education Research interests include integrating knowledge graphs with machine learning, secure computing for collaborative AI, and multi-omics predictions. Recent work emphasizes privacy-preserving techniques and federated learning applications. Active in teaching roles including thesis supervision in data mining and machine learning seminars. Publications highlight advancements in secure computation methods, knowledge graph integration with LLMs, and privacy-aware classification systems. Engaged in projects addressing real-world challenges in data-driven prediction and environmental protection through machine learning innovations. Advising activities include supervision of over 10 student theses across various machine learning topics. No grants explicitly listed in provided materials. Affiliated with the Data Mining Group at Mainz University, contributing to interdisciplinary research initiatives in computational logics and Python-based data analysis education.

• Machine Learning
• Knowledge Graphs
• Secure Multi-Party Computing
• +4 more

Prof. Alanwar Amr is a Professor at the Chair of Cyber-Physical Systems within the TUM School of Computation, Information and Technology at Technical University of Munich (TUM). His research focuses on data-driven reachability analysis, security-assured reinforcement learning, and privacy-preserving estimation techniques using advanced mathematical frameworks like zonotopes. He holds a PhD from TUM and has held prior positions as an Assistant Professor at Jacobs University Bremen and a postdoctoral researcher at KTH Royal Institute of Technology. His work bridges formal verification, control theory, and cybersecurity in cyber-physical systems. Education: PhD in Automation and Control from TUM (under Prof. Althoff) Research Associate at UCLA and Engineer at Siemens/Morpho Research Interests: Data-driven formal verification methods Secure and resilient control systems Privacy-preserving set-based estimation Adversarial attack defense mechanisms His recent publications emphasize advancements in zonotope-based techniques for safety verification, privacy in distributed systems, and AI-driven control systems with formal guarantees. Key contributions include logical zonotopes for boolean function analysis and diffusion-based observers for multi-agent systems. Notable awards include the KTH Postdoctoral Fellowship, Qualcomm Innovation Fellowship finalist distinctions (2017/2018), and a best demonstration award at IPSN 2017. His work addresses critical challenges in autonomous systems, cybersecurity, and privacy in IoT environments.

• Cyber-Physical Systems
• Formal Verification
• Data-Driven Reachability Analysis
• +7 more