جست‌وجوی استاد و پژوهشگر

Lars Schützeمشاهده پروفایل

پژوهشگر

• Compiler Construction
• Homomorphic Encryption
• Quantum-Classical Computing
• +۳ مورد دیگر

Dr. Lars Schütze is a researcher at the Chair for Compiler Construction within the Faculty of Computer Science at Dresden University of Technology (TU Dresden). Holding a PhD in Computer Science from TU Dresden, he currently serves as a PostDoc specializing in domain-specific compilers for verifiable Full Homomorphic Encryption (vFHE) and hybrid quantum-classical computing systems. His academic credentials from TU Dresden include: Bachelor's degree in Computer Science Master's degree in Computer Science PhD in Computer Science (awarded February 2025) Schütze's research centers on advanced compiler design for emerging computational paradigms. His foundational work explores context-oriented and role-based programming languages, focusing on runtime optimization and dispatch mechanisms. Recent efforts pivot toward post-quantum security through homomorphic encryption compilers and hybrid quantum-classical computing frameworks. His research bridges theoretical language design with practical compiler implementation, emphasizing verifiable security and performance efficiency in next-generation computing environments. Publication analysis reveals a clear evolution from context-oriented programming (2017-2020) toward cryptographic compiler development (2022-2025). Early work optimized role-based dispatch systems, while recent publications establish compiler frameworks for Fully Homomorphic Encryption using MLIR infrastructure. His research consistently addresses performance bottlenecks in dynamic language features while transitioning toward quantum-resistant cryptography solutions. Scientific Awards: No awards documented in source materials Dr. Schütze supervises student theses in Homomorphic Encryption and Quantum-Classical Computing frameworks, offering projects spanning Bachelor to Master levels. His research is funded through institutional projects including (verifiable) Full Homomorphic Encryption and Hybrid Quantum-Classical Computation, though specific grant details remain undisclosed. He actively develops compiler infrastructure for encrypted computation and quantum-classical orchestration. As core personnel in TU Dresden's Chair for Compiler Construction, Schütze contributes to the RoSI project (role-based software infrastructures) and leads current initiatives in vFHE. His team collaborates on building domain-specific compiler toolchains that address quantum computing threats through post-quantum cryptographic solutions while advancing hybrid execution models for emerging hardware architectures.

Gerd Bumillerمشاهده پروفایل

استاد

• Digital Signal Processing
• Power Line Communication (PLC)
• Smart Grids
• +۳ مورد دیگر

Gerd Bumiller is a Professor at the Institute of Computer Science, Ruhr West University of Applied Sciences, specializing in Energy and Information Technology. He is an expert in digital signal processing, measurement technology, and powerline communication. Affiliation : Ruhr West University of Applied Sciences Research Focus : Power Line Communication (PLC), Smart Grids, Power Quality, Grid Impedance, Signal Processing Research Interests : Bumiller's work spans Power Quality Measurement (phase information analysis, harmonic power flow), Impedance Measurement Systems for power lines, and Smart Grid Communication Protocols . His projects include GridForm (grid-forming converters), EASIDE (impedance measurement), and NERD-NRW (IT security for smart metering). Publications : His recent work focuses on spectral power quality analysis, harmonic modeling, and impedance measurement optimization in power systems. Key venues include IEEE Transactions on Instrumentation and Measurement and ISPLC conferences. Leadership : Former Secretary (2006-2010, 2014-2018) and Chair (2018-2022) of the IEEE Technical Committee for Power Line Communication (TC-PLC). He also served as program director for Energy Informatics (2018-2022) and held an endowed professorship (2012-2015).

• Quality of Experience (QoE) of Internet applications
• Artificial intelligence and machine learning for communication networks
• Measurement and analytics of encrypted network traffic
• +۵ مورد دیگر

Prof. Dr. Michael Seufert is a Full Professor and Chairholder at the University of Augsburg since October 2023, leading the Chair of Networked Systems and Communication Networks within the Faculty of Applied Computer Science. Previously, he served as a Private Lecturer at the University of Würzburg and completed his Habilitation in Computer Science there in 2023. His academic journey includes a PhD in Computer Science from the University of Würzburg (2017) with a thesis on Quality of Experience and Access Network Traffic Management of HTTP Adaptive Video Streaming, which earned him two prestigious Best Dissertation Awards. His research focuses on Quality of Experience (QoE) of Internet applications, artificial intelligence and machine learning for communication networks, measurement and analytics of encrypted network traffic, and data-driven proactive user-centric network management solutions. His work bridges theoretical foundations with practical applications in network monitoring, security, and performance optimization. Recent research has increasingly incorporated machine learning techniques to address challenges in network management, traffic analysis, and user experience assessment. Prof. Seufert's publication record shows a clear evolution from foundational work on QoE modeling and HTTP adaptive streaming toward more sophisticated applications of machine learning in network operations. His recent publications (2023-2025) demonstrate a strong emphasis on practical ML applications for network monitoring, security, and optimization, with particular attention to real-world constraints and performance requirements. The research spans multiple subfields including encrypted traffic analysis, explainable AI for network management, edge computing, and quality assessment for emerging applications. CNOM Young Professional Award of the IEEE Communications Society (ComSoc) Technical Committee on Network Operations & Management (CNOM) (2024) Best Dissertation Award 2018 of KuVS (Communication and Distributed Systems) special interest group Best Dissertation Award of the IEEE Communications Society (ComSoc) Technical Committee on Network Operations & Management (CNOM) Prof. Seufert actively supervises research projects and advises students through thesis projects and practical modules. His teaching portfolio includes Communication Systems, Management of Communication Networks, Practical Introduction to Internet-Technologies, and seminars on Networked Systems. He leads a research group focused on networked systems and communication networks, with current projects exploring machine learning applications in networking, as evidenced by the organization of the 3rd International Workshop on Machine Learning in Networking (MaLeNe 2025).

• Networking
• Machine Learning
• Wireless Sensor Networks
• +۴ مورد دیگر

S. Boumerdassi is a researcher at the CEDRIC Laboratory of Conservatoire National des Arts et Métiers (CNAM) . He has been actively involved in interdisciplinary research spanning networking , machine learning , image encryption , and IoT systems since the early 2000s. His work has focused on security protocols, energy efficiency in data centers, and anomaly detection frameworks. Key Research Areas: Networking: VANETs, LoRaWAN, network anomalies, and edge computing. Security: Blockchain oracles, cryptographic protocols, and wireless sensor network security. Machine Learning: Applications in network optimization and sustainable agriculture. Image Encryption: Chaotic maps, DCT coefficients, and grain algorithms. Recent Publications highlight his contributions to federated learning, anomaly detection, and mobility models. Leadership and Collaboration He has co-organized conferences like Mobile, Secure, and Programmable Networking and contributed to edited volumes on machine learning and IoT. His collaborations include researchers from Springer, IEEE, and institutions across France, Italy, Spain, and Canada.

Daniel Hellwigمشاهده پروفایل

استاد

• Blockchain
• Supply Chain Management
• Production Management
• +۲ مورد دیگر

Dr. Daniel Hellwig serves as Professor and Chair of Production Management within the Supply Chain Management Group at WHU - Otto Beisheim School of Management in Vallendar, Germany. His academic expertise is complemented by his professional role as Principal at Kepler Cannon in New York since 2014, demonstrating a strong integration of theoretical research and practical business applications in the field of supply chain innovation. His educational background includes: M.Eng. from University of California at Berkeley (2013) B.S.E. from The Wharton School, University of Pennsylvania (2011) Dr. Hellwig's research focuses on blockchain technology applications in supply chain management, with particular emphasis on distributed ledger systems for trade finance, resource allocation, and supply chain resilience. His work bridges technical blockchain implementation with business process optimization, exploring how cryptographic techniques like fully homomorphic encryption can enable secure information sharing among competing retailers. He has developed innovative simulation frameworks such as the DISASTER token trading game to study supply chain behavior during crises, demonstrating practical applications of theoretical concepts. His publication trajectory reveals an evolution from foundational blockchain concepts to increasingly sophisticated supply chain applications. Early works established fundamental blockchain knowledge through comprehensive guides, while recent research focuses on behavioral aspects of blockchain-enabled markets and privacy-preserving technologies for supply chain collaboration. This progression demonstrates both technical depth and growing business relevance in his scholarly contributions. As an educator, Dr. Hellwig has been a consistent guest lecturer at WHU since 2018, teaching blockchain programming and introduction courses at both bachelor's and master's levels. His teaching approach emphasizes practical implementation alongside theoretical understanding, reflecting his dual expertise in academic research and professional consulting. He frequently collaborates with Goran Karlic and Professor Arnd Huchzermeier, suggesting strong research partnerships within WHU's Supply Chain Management Group.

• Secure Computation
• Privacy-Preserving Machine Learning
• Homomorphic Encryption
• +۴ مورد دیگر

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.

• Computer Networks
• Telecommunications
• Cloud Computing
• +۷ مورد دیگر

Raouf Boutaba is a Professor at the University of Waterloo, Canada, with a focus on networking and telecommunications research. His work spans computer networks, cloud computing, network security, and software-defined networking (SDN). He has contributed to advancements in 5G/6G networks, network slicing, energy-efficient architectures, and machine learning applications in networking. His research addresses challenges in network monitoring, resilient communication systems, and optimizing network performance through reinforcement learning and intelligent algorithms. He collaborates extensively with researchers globally, publishing in top-tier journals and conferences such as IEEE Transactions on Networking and NOMS. Boutaba’s work often bridges theoretical contributions with practical implementations, emphasizing real-world applicability in telecommunications and distributed systems. His recent focus includes open RAN architectures, edge computing, and securing encrypted traffic in evolving network environments.

Reinhard Heckelمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Medical Image Processing
• +۳ مورد دیگر

Reinhard Heckel is a Professor of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM). His career includes positions as a Tenure-Track Assistant Professor at Rice University (2017–2019) and postdoctoral fellow at UC Berkeley's Berkeley Artificial Intelligence Research Lab. He holds a PhD from ETH Zurich (2014) and conducted doctoral research at Stanford University’s Statistics Department. Recognitions include being named one of Germany's 'Top 40 under 40' (2022) and the Werner von Siemens Ring Foundation Award (2022). Education & Professional Background: PhD in Computer Science, ETH Zurich (2014) Visiting Doctoral Fellow, Stanford University (Statistics Department) Postdoctoral Fellowship, UC Berkeley (EECS Department) Research Focus: His work bridges theoretical foundations and practical applications in machine learning, including: Algorithm development for deep learning and medical image processing Mathematical foundations of machine learning DNA data storage technology (error correction, synthesis methods) Computational imaging and inverse problem solutions Awards & Highlights: 2022: Capital 40 under 40, Werner von Siemens Ring Foundation Award 2015: ETH Zurich Medal for Doctoral Thesis, IBM Invention Achievement Award Grants & Collaboration: His research has been supported by grants focusing on DNA storage scalability and MRI reconstruction. He collaborates with institutions like IBM Research and the Berkeley AI Lab. Key projects include developing DNA synthesis methods and AI-driven medical imaging tools. Labs & Teams: Leads TUM's machine learning initiatives in computational imaging and biological data storage systems. Active in interdisciplinary teams bridging computer science, bioengineering, and statistics.

Farinaz Koushanfarمشاهده پروفایل

استاد

• Hardware Security
• Integrated Circuit Security
• Physical Unclonable Functions
• +۵ مورد دیگر

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.

• Group Theory
• Finite Geometry
• Combinatorial Designs
• +۳ مورد دیگر

Professor Tran van Trung is a Professor of Mathematics at the Institut für Experimentelle Mathematik (IEM) within the Faculty of Mathematics at the University of Duisburg-Essen. His academic career includes positions at the University of Saigon and Ruprecht-Karls-Universität Heidelberg, where he earned several advanced degrees, including his Habilitation in 1988. He is affiliated with the IEM, focusing on experimental and theoretical aspects of mathematics. Tran van Trung holds a Licentiate in Sciences (1970) from the University of Saigon. He then pursued his academic career at the Ruprecht-Karls-Universität Heidelberg, where he obtained a Diplom-Mathematik (1974), Doctor rerum naturalium (1976), and later achieved Habilitation with the Venia Legendi in 1988. His research focuses on Group Theory, Finite Geometry, Combinatorial Designs, Cryptology, and applications of Discrete Mathematics to Cryptology and Information Theory. He explores advanced topics such as group-based cryptographic systems, resolvable t-designs, and cryptographic protocols leveraging combinatorial structures. While specific details about his advised students or grants are not explicitly listed, his contributions to cryptography and discrete mathematics suggest involvement in research projects and academic mentorship. His work has implications for both theoretical advancements and practical applications in information security. No specific labs or teams are mentioned in the provided information.

Rahamatullah Khondokerمشاهده پروفایل

استاد

• Cybersecurity
• Digitalization
• Automotive Security
• +۱۱ مورد دیگر

Prof. Dr.-Ing. Rahamatullah Khondoker is a Professor of Business Information Systems with a focus on IT Security and Digitalization at THM University of Applied Sciences . He leads the CYSECDIGITAL research group, specializing in cybersecurity solutions for digitalization, autonomous mobility, and smart systems. PhD candidates under his supervision include Adnan Kujovic, Kumar Saurabh, Md. Zahirul Islam, and Khandaker Mohammad Mohiuddin. Projects funded by Erasmus+, DAAD, and THM Strategic Fund include ZDayIIoTSec and CyModeB . He has collaborated with institutions like IIT Delhi, BUET, and IIIT Allahabad, and signed MOUs with universities in Bangladesh, India, and Thailand. Research Focus : His work bridges Cybersecurity and Digitalization , with key contributions in SDN/NFV security, automotive cybersecurity (ISO/SAE 21434), IIoT, and AI-driven threat modeling frameworks like STRIDE and TMAP. Recent Publications highlight trends in automotive cybersecurity , IoT security audits , zero-day exploit detection , and AI applications for intrusion detection . His research spans threat intelligence integration, blockchain for supply chains, and machine learning in precision agriculture. Scientific Contributions : Nominated for best paper awards at AINTEC 2014 and IEEE NetSoft 2016. Co-authored foundational works on SDN/NFV security (Springer, 2018) and Industrie 4.0 security (Springer, 2017). Industry Collaboration : Partnerships with Continental, ANSYS, DRASS, and others in automotive, IIoT, and cybersecurity domains. He also leads the Automotive Cybersecurity Academy and advises on digitalization policy.

• Memory Encryption
• Symmetric Ciphers
• Digital Hardware Design
• +۴ مورد دیگر

Jens Nöpel serves as a researcher at the Department of Security in Information Technology at Technical University of Munich, working under Chair Professor Georg Sigl. His office is located at Theresienstr. 90 in Munich, where he conducts research in hardware security and cryptographic implementations. His primary research interests focus on memory encryption and integrity verification systems , symmetric cipher implementations , and digital hardware design for security applications. Nöpel specializes in developing and evaluating hardware security mechanisms, particularly those implemented on FPGAs for memory protection systems. His work bridges theoretical cryptography with practical hardware implementation challenges. As an active supervisor, Nöpel offers multiple research opportunities for students across various academic levels. He regularly supervises Bachelor's and Master's theses along with research internships focused on hardware security implementations. His student projects typically involve extending or implementing symmetric ciphers, message authentication codes, and error correction functionality for memory encryption systems. Nöpel maintains an active research laboratory environment where students work with hardware description languages to develop security-focused hardware modules. His research group focuses on practical evaluation of security mechanisms through FPGA implementation and performance analysis, particularly examining metrics relevant to memory encryption and integrity verification systems.

• Microarchitectural Side-Channel Attacks
• System Security
• CPU Security
• +۶ مورد دیگر

Dr. Michael Schwarz is a tenured faculty member at the CISPA Helmholtz Center for Information Security in Saarbrücken, Germany, where he leads the RootSec research group. Since 2020 he has been investigating microarchitectural side-channel attacks, system security, CPU security, transient-execution vulnerabilities, and corresponding defense mechanisms. He previously served as a post-doctoral researcher (2019–2020) and PhD student (2016–2019) at Graz University of Technology, after earning two master’s degrees in computer science and software engineering with a security focus. Education PhD, Graz University of Technology, 2019 — “Software-based Side-Channel Attacks and Defenses in Restricted Environments” (Advisor: Daniel Gruss) MSc, Computer Science, Graz University of Technology MSc, Software Engineering, Graz University of Technology Research Interests Dr. Schwarz’s work centers on microarchitectural side-channel attacks and system security , spanning: CPU security and transient-execution vulnerabilities (Meltdown, Spectre, Fallout, LVI, ZombieLoad, ÆPIC Leak, CacheWarp) Detection, mitigation, and formal guarantees against microarchitectural leakage Hardware–software co-design for secure operating systems and trusted execution environments Reverse engineering of CPU internals, cache architectures, and prefetchers Browser and web-platform security, including sandbox bypasses and fingerprinting Compiler security and constant-time programming enforcement Scientific Awards & Recognition 2022 Busy Beaver Award for “Foundations of Cybersecurity II” 2021 Busy Beaver Award for “Side-Channel Attacks and Defenses” 2020 EuroSys Roger Needham PhD Award 2019 IEEE S&P Distinguished Paper Award (Spectre) 2019 NSA Best Scientific Cybersecurity Paper Competition Honorable Mention (Meltdown) 2019 Open Exploit Award (Meltdown & Spectre) 2018 CSAW Best Paper Award (Meltdown) 2018 Pwnie Awards: Best Privilege Escalation Bug & Most Innovative Research 2023 USENIX Security Noteworthy Reviewer Award 2023 WOOT Best Paper Award (CustomProcessingUnit) 2022 Pwnie Award for Best Desktop Bug (ÆPIC) 2022 AI 2000 Security & Privacy Honorable Mention 2021 CCSW Best Paper Award & CSAW Best Paper 3rd Place Advising & Collaborations Dr. Schwarz currently mentors numerous PhD students and post-docs within the RootSec group. His collaborations extend across CISPA, Graz University of Technology, and an international network visible through extensive multi-author publications at top-tier venues including USENIX Security, IEEE S&P, ACM CCS, NDSS, ESORICS, DIMVA, ASPLOS, WWW, FC, and specialized microarchitecture security conferences. Labs & Teams He heads the RootSec Research Group at CISPA, whose core mission is to uncover, analyze, and mitigate microarchitectural and system-level security threats. The group maintains close links with industry partners, open-source communities, and policy makers to ensure that research findings are rapidly translated into practical defenses deployed in modern operating systems and processors.

• Microarchitectural Attacks
• Side-Channel Attacks
• System Security
• +۳ مورد دیگر

Dr. Michael Schwarz is a tenured professor at CISPA Helmholtz Center for Information Security in Saarbrücken, Germany. His research focuses on microarchitectural side-channel attacks and system security , with notable contributions to vulnerabilities like Meltdown , Spectre , and ZombieLoad . He leads the RootSec Research Group and is a regular speaker at top-tier conferences including Black Hat and IEEE S&P. PhD in Computer Science from Graz University of Technology (2019) Two master’s degrees in Computer Science and Software Engineering His work spans CPU security , transient execution attacks , and side-channel defenses , with 15 recent publications on topics like cache timing analysis, browser sandbox breaches, and compiler-induced vulnerabilities. Awards include multiple Busy Beaver Awards , Pwnie Awards , and recognition for foundational papers like Meltdown . Collaborated with Daniel Gruss (PhD advisor) Advises on hardware-software security interactions Publications reveal a trend toward microarchitectural attack surfaces and practical exploitation frameworks , often leveraging hardware features (e.g., CPU caches, prefetchers) for security analysis. He has also contributed to secure memory allocation and browser-based fingerprinting research.

• French Language Practice
• Romance Studies
• Computer Vision
• +۳ مورد دیگر

Viridiane Fay is a researcher in the Institute of Modern Languages and Romance Studies at the University of Würzburg , specializing in French language practice and applied computational methods. During lecture periods, she holds office hours every Tuesday from 1 p.m. to 2 p.m., with appointments available during semester breaks. Role: French Editor, Language Practice Location: Am Hubland, 97074 Würzburg Contact: viridiane.fay@uni-wuerzburg.de Her research spans two distinct domains: French language pedagogy and computer vision/AI . While her institutional affiliation focuses on Romance Studies, her publication record reveals expertise in: Medical and underwater image processing Transformer-based architectures Federated learning and network optimization Watermarking and security algorithms Multi-modal AI applications Neuroscience signal analysis Recent work includes lightweight segmentation models (e.g., ContextFormer), drone/aerial imagery detection frameworks (CH-YOLO-Lite), and privacy-preserving AI systems (Find). Despite limited biographical details in the scrape, her 30+ publications since 2017 suggest significant technical contributions.