جست‌وجوی استاد و پژوهشگر

Maire O'Neillمشاهده پروفایل

استاد

• Hardware Security
• Cryptography
• Approximate Computing
• +۷ مورد دیگر

Maire O'Neill is a Professor at the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast, affiliated with the Secure Digital Systems (SDS) group and the Institute of Electronics, Communications & Information Technology (ECIT). Her research focuses on hardware security, cryptography, and secure embedded systems. She has held significant leadership roles and pioneered work in FPGA security, approximate computing, and post-quantum cryptography. Dr. O'Neill's academic journey includes over 20 years of contributions to secure digital systems, with a particular emphasis on cryptographic hardware, side-channel analysis, and IoT security. She has led major research projects such as the EU-funded 'TruDetect' initiative for hardware Trojan detection and the 'Secure IoT Processor Platform' project. Her work bridges theoretical research with practical applications, emphasizing real-world security challenges in electronics and computing. Education: Background in electrical engineering and computer science (details not explicitly stated in the text) Research Interests: Her work spans hardware security primitives, FPGA-based cryptographic solutions, and energy-efficient computing. She explores vulnerabilities like Rowhammer attacks and side-channel leaks while developing defenses through approximate computing and machine learning techniques. Her research addresses emerging threats in IoT, 5G networks, and post-quantum cryptography. Publications Trends: Recent work emphasizes machine learning applications in security (e.g., ML-KEM accelerator designs), hardware Trojan detection, and energy-efficient approximate computing. She frequently publishes in top-tier venues like IEEE Transactions and ACM conferences, with a focus on practical implementations and FPGA demonstrators. Awards: 2007: BFIIN ITEC Platinum Award, British Female Inventor of the Year, European Union Women Innovators 2015: Fellow of the Irish Academy of Engineering 2015: INVENT Award for collaborative innovation Advising & Grants: Supervised 8 PhD students (explicitly stated). Active in securing research grants (18 projects listed), including EU and industry collaborations. Engages in academic service roles like IEEE Distinguished Lecturer and international conference organization. Labs/Teams: Leads the Secure Digital Systems (SDS) group, collaborating with global partners on hardware security and IoT initiatives. Maintains strong ties with industry through projects like NIO New Deal Cyber Bid and TruDetect.

Ahmed Ounمشاهده پروفایل

استادیار

• Cybersecurity
• Hardware-Assisted Security
• VLSI
• +۴ مورد دیگر

Dr. Ahmed Oun is an Assistant Professor in the School of Electrical Engineering and Computer Science (EECS) at Ohio University's Russ College of Engineering and Technology since 2024. Previously, he held faculty roles at Ohio Northern University and worked in the oil and gas industry as a Gas Turbine Controls NPI Engineer, Operation Control Room Supervisor, and Project Manager at General Electric International Inc. He earned his Ph.D. (2022) from the University of Toledo and a Master's degree (2012) from the University of Bridgeport. His research focuses on cybersecurity, hardware-assisted security for embedded systems, VLSI, IoT security, and machine learning applications in protecting computing systems against cyberattacks and hardware Trojans. Key interests include PUF-based authentication, FPGA security, and AI-driven threat detection. Recent publications emphasize AI's role in cybersecurity, IoT device protection, and intrusion detection systems. His work spans both theoretical advancements and applied solutions in secure communication and hardware vulnerability analysis. No scientific awards are explicitly listed, but his contributions include developing novel defense mechanisms and authentication protocols for embedded systems. Dr. Oun has advised no explicitly listed students or grants, though his industry experience and academic roles suggest involvement in collaborative research projects. His work contributes to advancing secure computing infrastructure and resilient IoT ecosystems.

Dunaev Dmitriyمشاهده پروفایل

دانشیار

• Hardware Security
• Physical Unclonable Functions (PUFs)
• Software Obfuscation
• +۳ مورد دیگر

Dmitriy Dunaev is an Associate Professor at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics. His research focuses on hardware security, particularly through Physical Unclonable Functions (PUFs), software obfuscation, and FPGA-based security solutions. Current position: Associate Professor Institution: Budapest University of Technology and Economics Department: Automation and Applied Informatics Research spans cryptographic security enhancement via configurable hardware designs, machine learning vulnerability analysis, and optimization of security protocols in embedded systems. Recent publications investigate temperature sensitivity, bit configuration in PUFs, and FPGA implementation challenges. Contact: dunaev@aut.bme.hu

Husam Al-Magsoosiمشاهده پروفایل

عضو هیئت علمی

• Hardware Security
• Physical Unclonable Functions (PUFs)
• Machine Learning in Security
• +۴ مورد دیگر

Husam Al-Magsoosi is an Assistant Research Fellow at the Budapest University of Technology and Economics , affiliated with the College of Engineering and the Department of Automation and Applied Informatics . His work focuses on hardware security, particularly in the development and analysis of Physical Unclonable Functions (PUFs) . Research Interests : Hardware Security, PUFs, FPGA Design, Machine Learning in Security, Embedded Systems, Wireless Sensor Networks. His recent publications highlight advancements in configurable ring oscillator PUFs, addressing security, performance optimization, and environmental factors like temperature sensitivity. He explores FPGA-based implementations and evaluates vulnerabilities to machine learning attacks. His work also extends to energy-efficient routing protocols for sensor networks and embedded system design. Scientific Awards : No specific awards mentioned in the provided data.

• Computer Science
• Hardware Security
• Machine Learning
• +۷ مورد دیگر

Jack Miskelly is a Research Fellow at the School of Electronics, Electrical Engineering and Computer Science at Queen's University Belfast. His research focuses on advanced hardware security mechanisms, leveraging machine learning and approximate computing techniques to analyze and enhance the robustness of digital systems. Expertise: Machine Learning, FPGA Security, Physical Unclonable Functions (PUFs), Side-Channel Analysis Key Collaborations: Dr. C. Gu, Prof. M. O'Neill, Y. Hao, J. Moore Research Trends: His recent work emphasizes vulnerabilities in modern DRAM systems (Rowhammer attacks), deep learning-based side-channel attacks on PUFs, and energy-efficient FPGA security methodologies. Contact: Email J.Miskelly@qub.ac.uk for collaboration or inquiries.

• Information Theory
• Physical Layer Security
• Quantum Communication
• +۳ مورد دیگر

Johannes Voichtleitner is a Researcher at the Chair of Theoretical Information Technology within the Technical University of Munich (TUM). His work focuses on Physical Layer Security , Quantum Communication , and Communication Engineering , contributing to the development of secure communication protocols and wireless network security frameworks. Research Areas: Physical Layer Security Quantum Communication Wireless Networks Communication Engineering Publications: Recent works include Security in presence of AI (Demo) (2025), Joint Communication and Sensing Meets Physical Layer Security (Demo) (2025), and Statistical Verification of Physical Layer Security (2024), emphasizing secure communication bounds and AI-driven security protocols. Earlier contributions explore Modular Coding Schemes (2021-2023) and Semantic Secrecy in 5G mmWave systems.

• Blockchain Technology
• Non-Fungible Tokens (NFTs)
• Computer Networks
• +۷ مورد دیگر

Shital Joshi is a Teaching Associate Professor in the Department of Computer Science at Oklahoma State University. Her academic career spans roles as a Teaching Assistant Professor (2019–2025) and current position since July 2025. She holds a Ph.D. in Computer Engineering from the University of North Texas (2016). Her research focuses on blockchain technologies, NFT performance modeling, IoT security, energy-efficient networks, and nanoelectronic circuit design. Her work bridges theoretical models with practical implementations, including adaptive blockchain systems, secure telemedicine frameworks, and graphene-based SRAM optimization. Notable contributions include bivariate performance models for NFT chains and energy-efficient routing protocols for wireless sensor networks. She actively participates in academic service, such as the 2023 ABET Symposium. Joshi teaches core computer science courses like Operating Systems, Data Structures, and Computer Systems, emphasizing hands-on learning. Her teaching spans from foundational courses (e.g., Computer Proficiency) to advanced topics (e.g., Memory Management in OS Design).

Suk Jin Leeمشاهده پروفایل

دانشیار

• Internet of Things
• Network Protocols
• Computer Vision
• +۳ مورد دیگر

Suk Jin Lee is an Associate Professor of Information Technology at James Madison University's College of Integrated Science & Engineering. His expertise spans IoT, network protocols, computer vision, machine learning, signal processing, and target tracking. He teaches a wide range of undergraduate and graduate courses, including computer networks, IoT, and algorithms. Lee holds a Ph.D. in Electrical & Computer Engineering from Virginia Commonwealth University (2012), an M.E. and B.E. from Pukyong National University, South Korea. His research focuses on IoT security protocols, embedded vision systems, medical imaging, and biomedical applications. Notable projects include IoT authentication frameworks, MRI super-resolution methods, and automated insulin delivery systems. He has contributed to nanosensor networks and real-time streaming solutions using Raspberry Pi. His work integrates machine learning into medical diagnostics and tumor motion prediction. Lee's teaching innovations include hands-on learning designs for computer organization and curriculum development in IoT education. He has held academic positions at Columbus State University (2016-2023) and Texas A&M University-Texarkana (2013-2016), and conducted postdoctoral research at the University of Maryland Medical Center. His publications highlight interdisciplinary approaches, combining IoT, blockchain, and medical technologies. Current research trends emphasize energy-efficient protocols, AI-driven medical imaging, and IoT security for smart devices. Lee has advised numerous projects in senior design and independent studies, focusing on practical implementations of theoretical concepts. His lab work involves developing frameworks for IoT appliance control and embedded vision systems.

Vijaykrishnan Narayananمشاهده پروفایل

استاد

• Computer Architecture
• VLSI Design
• Energy-Efficient Computing
• +۳ مورد دیگر

Vijaykrishnan Narayanan is a Distinguished Professor in the Department of Computer Science and Engineering at Pennsylvania State University . His research focuses on ferroelectric memory systems , energy-efficient computing , and neuromorphic engineering , with applications in deep learning , edge computing , and secure hardware design . He leads projects such as EFRI BRAID (Neuroscience-Inspired Visual Analytics) and FuSe-TG (Heterogeneous Ferroelectronics for Big Data Analytics). Research Interests Compute-in-Memory (CIM) architectures Ferroelectric Field-effect Transistors (FeFETs) Low-power VLSI design Hardware security mechanisms Scientific Contributions Over 707 research outputs and 26 grants Key contributor to UN Sustainable Development Goals via energy-efficient computing NSF grants for projects like Ferro-CoDE (combinatorial optimization) and INSECT NET (entomology-computer science collaborations)

Alexandros Kapsalisمشاهده پروفایل

پژوهشگر

• Photonic Design
• Novel Optical Devices
• Silicon Photonics
• +۵ مورد دیگر

Dr. Alexandros Kapsalis is a post-doc researcher at the Optical Communications Laboratory of the National and Kapodistrian University of Athens (NKUA), affiliated with the Department of Informatics and Telecommunications. He holds a PhD in Optical Communication Devices from the University of Athens (2010) alongside a B.S. (2002) and M.Sc. (2004) in Telecommunications. Research Interests: Photonic design, novel optical devices, silicon photonics, all-optical signal processing, semiconductor lasers, optomechanical systems, and system automation. Article Trends: Focuses on silicon/silicon germanium waveguides, quantum dot lasers, four-wave mixing, phase regeneration, and mid-infrared conversion. Keywords span Photonics , Telecommunications , and Nonlinear Optics . Service: Serves as a reviewer for IEEE and Elsevier journals, with 20+ publications in national and EU projects.

Kuan-Hsun Chenمشاهده پروفایل

استادیار

• Computer Architecture
• Embedded Systems
• Real-Time Systems
• +۵ مورد دیگر

Kuan-Hsun Chen is an Assistant Professor specializing in Computer Architecture Design for Embedded Systems Real-Time Systems Non-Volatile Memory (NVM) Optimization Machine Learning and Edge AI His research focuses on bridging hardware-software gaps in real-time embedded applications through innovative architectural solutions. Research highlights include: Decision Tree Optimization for Real-Time Inference Wear-Leveling Techniques in NVM Probabilistic Timing Guarantees GPU-Accelerated Graph Algorithms Security-Enhanced NVM Designs He explores theoretical foundations while emphasizing practical implementations in autonomous systems and cyber-physical platforms. Scientific achievements: Best Paper Award (2023) Outstanding Paper Award (2023) Outstanding Reviewer Award (2024) His work spans algorithm design, hardware-software co-optimization, and rigorous system validation through fault injection and simulation frameworks.

• Hardware Security
• Cryptography
• Side-Channel Attacks
• +۳ مورد دیگر

Elena Dubrova is a Professor at the Division of Electronics and Embedded Systems, KTH Royal Institute of Technology. She specializes in hardware security, cryptography, and embedded systems security. Her roles include examiner and course responsible for advanced degree projects in Computer Engineering, Communication Systems, Embedded Systems, and Machine Learning. She also teaches courses such as Design of Fault-Tolerant Systems, Hardware Security, and Internet Security and Privacy. Her research focuses on side-channel attacks, cryptographic algorithm vulnerabilities, and FPGA security. Notable work includes analyzing hardware security flaws in cryptographic implementations (e.g., CRYSTALS-Kyber, AES), RF signal leakage in chips, and mitigating threats in FPGA-based systems. Her contributions span both theoretical advancements and practical countermeasure development. Dr. Dubrova’s articles highlight trends in post-quantum cryptography vulnerabilities, machine learning-assisted security analysis, and the integration of physical unclonable functions (PUFs) for secure authentication. She emphasizes hardware-software co-design for robust security solutions. No scientific awards are explicitly listed in the provided information. Her work involves collaborative projects on cryptographic protocol design and secure embedded system architecture, though specific grants or lab affiliations are not detailed here.

Chau-Wai Wongمشاهده پروفایل

دانشیار

• Machine Learning
• Multimedia Forensics
• Signal and Image Processing
• +۵ مورد دیگر

Chau-Wai Wong is an Associate Professor in the Department of Electrical and Computer Engineering at North Carolina State University, with affiliations to the Forensic Sciences Cluster and Secure Computing Institute. He previously served as a Data Scientist at Origin Wireless, Research Assistant at University of Maryland, and Research Associate at Hong Kong Polytechnic University. His work bridges machine learning with applications in multimedia forensics, signal processing, and computational social science. Ph.D. , Electrical Engineering, University of Maryland (2017) M.Phil. , Electronic and Information Engineering, Hong Kong Polytechnic University (2010) B.Eng. , Electronic and Information Engineering, Hong Kong Polytechnic University (2008) Research spans federated learning (security vulnerabilities, communication efficiency), physically unclonable features (PUF-based authentication), generative models (GANs for hardware modeling), and computational social science (AI chatbots for disaster communication, TikTok behavioral analysis). Recent work explores neural tangent kernels and multi-LLM agent collaboration . Key publications focus on decentralized AI systems (ICML'25), deepfake detection (under review), and social media health analytics (Telematics and Informatics'25). His federated learning research has appeared at ICLR, IEEE T-NNLS, and USENIX Security. NSF CAREER Award IEEE Signal Processing Cup Organizer (2016) Technical Program Committee Chair (IH&MMSec'25) Area Chair (ICME'21–'24) Senior Member, IEEE Advises students in AI security , physiological sensing , and social science automation . Collaborates with teams at University of Maryland, Hong Kong Polytechnic University, and industry partners. Current projects include identity-privacy protection for smart health and UAV-assisted network optimization (IEEE DySPAN'25).

• 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.

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.