جست‌وجوی استاد و پژوهشگر

• Computer Systems
• Cybersecurity
• Systems Security
• +۱۲ مورد دیگر

Jingjie Li is a Lecturer (Assistant Professor) in the School of Informatics at the University of Edinburgh, where he conducts interdisciplinary research at the intersection of computer systems, cybersecurity, and human-computer interaction. He is a member of the Institute for Computing Systems Architecture and the School of Informatics Ethics Committee. Ph.D. in Computer Engineering, University of Wisconsin-Madison (2017–2023) B.Eng. (R&D) with First-Class Honours, Australian National University (2015–2017) B.Sc., Beijing Institute of Technology (2013–2015) His research focuses on user-centric security and privacy , measuring human behavior in digital systems , and efficient human-machine interfaces . He investigates risks in emerging technologies such as smart homes, AR/VR, and AI systems, aiming to make them safer and more human-centric. His work combines technical innovation with behavioral insights to design practical privacy controls, measure digital risks, and build efficient computing platforms. The recent publications highlight a strong trend in privacy transparency , AI explainability , smart home and AR/VR security , and hardware-software co-design . His work frequently appears in top-tier venues including IEEE S&P, USENIX Security, ACM CHI, and ISCA, reflecting a consistent focus on both technical depth and human factors. Notable scientific awards include: ACM CHI Best Paper Award (2019) Facebook Trustworthy Products in AR, VR, and Smart Devices Award (2021) CPS Rising Star, NSF (2022) Generative AI Laboratory Seedcorn Award, University of Edinburgh (2024) Qualcomm Innovation Fellowship Finalist (2019, 2021) Jingjie Li actively supervises PhD students including Jiuming Jiang and Karen Jiamin Zheng, and co-supervises Lawrence Piao and Temima Hrle. He has received research support through fellowships such as the UW–Madison Chancellor’s Opportunity Fellowship and has collaborated globally with institutions including Max Planck Institute, Visa Research, and CSIRO. He serves on the program committees of major conferences like ACM CCS, USENIX Security, and ACM CHI. He leads a dynamic research team focused on systems security and human-centered computing, hosting undergraduate researchers and mentoring students through projects in privacy, AI transparency, and hardware security. His lab fosters interdisciplinary collaboration and real-world impact through community engagement, such as the 'Hack Your Age' workshop with intergenerational participants.

Patanjali Sristiمشاهده پروفایل

استادیار

• Hardware Security
• Trustworthy Hardware
• AI and IoT Security
• +۶ مورد دیگر

Patanjali Sristi is an Assistant Professor at Augusta University's School of Computer and Cyber Sciences, specifically within the Department of Cybersecurity Engineering. Located at 100 Grace Hopper Lane in Augusta, Georgia, Dr. Sristi joined the university in January 2025 after previously working as a Postdoctoral Researcher at the University of Florida with Dr. Swarup Bhunia. Their academic journey began with a B.Tech in Electrical and Electronics Engineering from Pondicherry University in 2011, followed by both MS and Ph.D. in Computer Engineering from the Indian Institute of Technology (IIT Madras). Dr. Sristi's educational background demonstrates a strong foundation in electrical engineering and computer science, with advanced specialization in hardware security. Their Ph.D. research at IIT Madras was supervised by Dr. Kamakoti Veezhinathan, focusing on critical aspects of hardware security that would form the basis of their future research career. Dr. Sristi's research program centers on addressing one fundamental question: "How can we design, measure and build efficient and affordable security assurances for a given hardware design in the context of an untrusted supply chain while respecting the design constraints at each level of abstraction?" This research vision spans three interconnected domains: AI for System Design: Developing data models and AI techniques for next-generation hardware systems AI for Hardware Security: Creating AI models for vulnerability detection, countermeasure evaluation, and mitigation of supply chain threats Cybersecurity for AI: Establishing metrics and algorithms for secure development, deployment, and operation of AI systems Dr. Sristi's scholarly output reveals a consistent focus on hardware security challenges within the modern distributed electronics supply chain. Their work demonstrates a progression from foundational research on hardware trojans and side-channel attacks toward comprehensive frameworks addressing the emerging "zero trust" paradigm in hardware security. A notable trend is the integration of AI/ML techniques with traditional hardware security approaches, reflecting the evolving nature of security threats and countermeasures. Their publications span prestigious venues including IEEE Transactions on VLSI Systems, IEEE Transactions on Computers, and various IEEE conferences, indicating strong recognition within the hardware security community. While specific awards aren't detailed in the available information, Dr. Sristi's research impact is evident through multiple US patents (including US Patent 11,899,827 and US Patent App. 17/392,376) and invitations to deliver talks at prominent organizations including Sony Finishing School, Northrop Grumman, and IEEE events. Their work on Netflix Privacy Analysis was featured in Wired, demonstrating real-world relevance and impact. Dr. Sristi actively engages with students through courses including CSCI 8940 (Dissertation Research), CSCI 8720 (Problems in Computer & Cyber), and CSCI 7900 (Research Colloquium). Their research program appears well-supported through collaborations with major institutions and industry partners, as evidenced by workshops conducted for the Indian Army in conjunction with Pravartak and IIT Madras. These partnerships suggest substantial research funding and collaborative opportunities that enhance the educational experience for students. Though specific lab information isn't provided in the available text, Dr. Sristi's research scope suggests involvement with hardware security laboratories equipped for VLSI design, testing, and security evaluation. Their work on IoT security, hardware trojans, and supply chain security would require facilities for physical device testing, side-channel analysis, and hardware emulation. The focus on "zero trust" implementation for hardware security indicates a research environment that bridges theoretical security models with practical implementation challenges.

Rafael Pereira Piresمشاهده پروفایل

مدرس

• Distributed Systems
• Machine Learning
• Privacy-Preserving Technologies
• +۳ مورد دیگر

Rafael Pereira Pires is a Lecturer and researcher at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Scalable Computing Systems Laboratory (SACS) and IC-SIN units. His research focuses on systems solutions at the intersection of privacy, efficiency, and machine learning in distributed environments. Education PhD in Computer Science (2019, University of Neuchâtel, Switzerland) Professional Master in Mechatronics (2014, IFSC, Brazil) Master in Computer Science (2009, UFSC, Brazil) His work explores privacy-preserving decentralized learning , trusted execution environments , and resource-efficient distributed systems . Recent publications address techniques like model fragmentation, approximate caching, and secure aggregation in decentralized learning contexts. Key trends in his 2023-2025 publications include: Advancements in federated learning and Mixture-of-Experts (MoE) models Applications of Trusted Execution Environments (SGX) to decentralized systems Optimization techniques for energy-aware and low-cost learning Scientific recognition includes the 2019 Léon Du Pasquier et Louis Perrier award for his PhD thesis. He has contributed to open-source tools like DecentralizePy and served as reviewer/PC member for top conferences including NeurIPS , Middleware , and ICDCS .

Balázs Vargaمشاهده پروفایل

پژوهشگر ارشد

• Road Traffic Modeling and Control
• AI-based Traffic Flow Prediction
• Urban Traffic Simulation
• +۳ مورد دیگر

Dr. Balázs Varga is a Research Fellow at the Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics (BME). He holds a PhD in Transportation and Vehicle Sciences (2021) and an MSc in Vehicle Engineering (2015) from BME. His industry experience includes roles at AVL Hungary as a Software and Function Developer (2016–2018) and academic positions at Chalmers University of Technology (2015) and SZTAKI (2012–2014). Current Role: Research Fellow (2021–present) Teaching: Programming, Control Theory, Traffic Modeling (English language course) Research Interests: Varga specializes in road traffic modeling and control, focusing on AI-based traffic estimation and dynamic traffic management. His work integrates machine learning with mesoscopic and microscopic traffic simulation tools like SUMO to optimize urban mobility and reduce emissions. Projects: He leads the 2020–2024 national development project 'Dynamic, adaptive traffic control services and evaluation tools based on digitally connected data sources' (2019-1.1.1-PIACI KFI). This initiative leverages connected data sources for real-time traffic control and policy evaluation. Key Publications Trends: His recent articles explore topics such as graph neural networks for sensor placement, multiobjective control of emissions, and mixed-reality V2X testing. These works emphasize data-driven approaches, emission reduction, and simulation frameworks for autonomous vehicles.

• High-performance computing
• Graph algorithms
• Graph machine learning
• +۱ مورد دیگر

Ariful Azad serves as an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads research at the intersection of high-performance computing and graph analytics. His work focuses on developing scalable algorithms for graph machine learning with applications in bioinformatics and security informatics. Educational Background: Ph.D. in Computer Science, Purdue University (2014) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2006) Research Focus: Dr. Azad specializes in high-performance graph algorithms , particularly for distributed-memory systems. His pioneering work includes the Combinatorial BLAS library and novel approaches for graph neural networks (GNNs), with emphasis on explainability through Shapley values and optimization of sparse matrix operations. His bioinformatics research tackles large-scale metagenomics challenges through projects like Exabiome. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Scalable GNN explanation frameworks using distributed Shapley values, (2) High-performance sparse linear algebra for graph embeddings and knowledge graphs, and (3) Bioinformatics applications in metagenomics and network alignment. His work consistently bridges theoretical algorithm development with practical implementations for exascale systems. Scientific Recognition: NSF CAREER Award (2024) for foundational contributions to scalable graph algorithms Indiana University Trustee's Teaching Award (2024) U.S. Department of Energy Early Career Award (2021) Research Leadership: As principal investigator for multiple federal grants, Dr. Azad directs projects advancing graph analytics at extreme scales. His work on Weapons of Mass Destruction knowledge graphs demonstrates applied security research, while Exabiome represents significant contributions to computational biology. He actively develops open-source tools like PLANETALIGN for network analysis benchmarking, fostering reproducibility in computational science.

• Cybersecurity
• Game Theory
• Quantum Cryptography
• +۵ مورد دیگر

Stefan Rass is a Professor at the Institute of Networks and Security within the Faculty of Engineering & Natural Sciences at Johannes Kepler University Linz (JKU), where he leads the LIT Secure and Correct Systems Lab. As Principal Investigator for FFG-funded projects including reSilienz (digital supply chain resilience, 2023–2025) and ITPUK (AI signature verification, 2022–2024), he bridges theoretical game theory with practical cybersecurity solutions for critical infrastructures and robotics systems. His research spans game-theoretic security models (patrolling games, defense-in-depth strategies), quantum cryptography (QKD network architectures), and cyber deception frameworks like Honeyquest for measuring honeypot effectiveness. Recent work addresses robotics security benchmarking (RobotPerf), cryptographic instruction chaining for control flow protection, and risk assessment methodologies for interdependent infrastructures. His mathematical decision-making approach integrates bounded rationality and stochastic modeling to solve real-world security challenges. Professor Rass actively shapes the field through program committee roles (ARES 2023), peer reviews, and invited talks on security transparency. His current projects focus on cost-benefit-aware monitoring for cyber-physical systems and quantum key distribution standardization, reflecting Austria’s strategic priorities in digital resilience. The LIT Secure and Correct Systems Lab under his direction develops foundational theories while deploying tools for industrial applications, particularly in critical infrastructure protection and secure robotics workflows.

• Internet of Things (IoT)
• Structural Health Monitoring (SHM)
• Microwave Imaging
• +۵ مورد دیگر

Igor Bisio is a Full Professor at the Department of Naval, Electrical, Electronics, and Telecommunications Engineering (DITEN) at Università di Genova. His research focuses on IoT-driven structural health monitoring, microwave imaging for biomedical applications, and wireless surveillance systems. Teaches courses on telecommunications, IoT, and machine learning Pioneers low-cost IoT solutions for SHM and post-stroke rehabilitation Develops microwave tomography techniques for pediatric stroke diagnostics Advances privacy-preserving Wi-Fi-based crowd monitoring Recent research trends include edge AI integration, compressive sensing for vibration analysis, and multi-class object tracking in aerial scenes. He also explores UAV-based monitoring systems, WiFi fingerprinting for localization, and Banach space inversion models for electromagnetic imaging. His work spans interdisciplinary domains combining electrical engineering, biomedical applications, and wireless network security.

Xiaoyan Hongمشاهده پروفایل

دانشیار

• Distributed Systems
• Internet of Things (IoT)
• Wireless Communications
• +۴ مورد دیگر

Dr. Xiaoyan Hong is an Associate Professor in the Department of Computer Science at The University of Alabama's College of Engineering. Her research focuses on mobile/wireless networks, vehicular networks, and delay-tolerant systems. Ph.D., Computer Science, University of California-Los Angeles (2003) M.S., Computer Science, Zhejiang University (2000) Research spans Internet of Things (IoT) , Connected Vehicles , and Underwater Wireless Networks . Key projects include NSF-funded underwater robot communication infrastructure and smart traffic light systems. Recent work explores Named Data Networking (NDN) in vehicular environments, Task Synchronization for autonomous vehicles, and V2I Communication for traffic optimization. NSF Research Experience for Undergraduates (REU) grant recipient $1.5M NSF grant for underwater robotics networking Her research integrates with multiple engineering centers, including the Center for Advanced Vehicle Technologies and Center for Transportation Operations .

• Robotics
• Control Systems
• Mechatronics
• +۴ مورد دیگر

Dr. Almas Shintemirov is a Research Fellow at Aalto University's Department of Electrical Engineering and Automation, specializing in robotics, control systems, and human-robot interaction. His research focuses on intelligent robotics, with emphasis on Real-time motion prediction for collaborative robots Nonlinear control algorithms for safe human-robot interaction Open-source robotic hardware design Deep learning applications in autonomous systems

Christos Xenakisمشاهده پروفایل

استاد

• Systems Security
• Network Security
• Mobile and Wireless Communications Security
• +۶ مورد دیگر

Prof. Christos Xenakis is a Professor at the Department of Digital Systems of the University of Piraeus, where he has been a faculty member since 2007. He is also the director of the Postgraduate Degree Programme on "Digital Systems Security" and a member of the Systems Security Laboratory at the University of Piraeus. Prof. Xenakis received his B.Sc. in Computer Science in 1993, M.Sc. in Telecommunication and Computer Networks in 1996, and Ph.D. in 2004, all from the Department of Informatics and Telecommunications at the University of Athens. His research interests focus on systems, networks and application security . He has participated in numerous European (ACTS, ESPRIT, IST, AAL, DGHOME, Marie Curie, Horizon2020) and National research programs, coordinating several Horizon2020 projects including CUREX, SealedGRID, INCOGNITO and SECONDO. He has also served as Principal Investigator for Horizon Europe projects like ENTRUST and ERATOSTHENES. Prof. Xenakis has authored over 90 publications in peer-reviewed journals and international conferences, focusing on cybersecurity topics such as blockchain security, smart contracts, self-sovereign identity, adversarial machine learning, smart grid security, cyber insurance, phishing detection, wireless security, and authentication protocols. He has served on editorial boards of several scientific journals including Computers & Security , Computer Communications , and IET Information Security . He is also a member of the steering committee of the European Cyber Security Challenge and leader of the Hellenic Cyber Security Team.

Ke (Cory) Wangمشاهده پروفایل

استادیار

• Computer Architecture
• Interconnection Networks
• Machine Learning
• +۸ مورد دیگر

Dr. Ke (Cory) Wang serves as Assistant Professor at the University of North Carolina at Charlotte in the Department of Electrical and Computer Engineering, where he conducts research in computer architecture and parallel systems. Ph.D. in Computer Engineering (2022), George Washington University M.Sc. in Electrical Engineering (2015), Worcester Polytechnic Institute B.Sc. in Computer Science (2013), Peking University His research focuses on machine learning-enabled computer architecture with emphasis on network-on-chip design , domain-specific accelerators , and cross-layer optimization for manycore systems. Specific areas include anomaly detection, graph neural network acceleration, and fault-tolerant communication frameworks. Recent publications demonstrate increasing integration of deep learning techniques with traditional architecture design, particularly for graph convolutional networks and heterogeneous systems . His work addresses multi-objective optimization across performance , energy efficiency , and security dimensions . Key scientific recognitions include: National Science Foundation award (CSR: Small: Cross-layer Design, 2023) National Science Foundation award (CRII: SHF: Flexible Design Framework, 2023) Dr. Wang leads the Intelligent Computer Architecture & Systems Laboratory , which develops advanced frameworks for network-on-chip optimization and AI-driven system design.

Sebastian Angelمشاهده پروفایل

دانشیار

• Systems
• Security
• Privacy
• +۴ مورد دیگر

Sebastian Angel is an Associate Professor and Chair of Undergraduate Curriculum in the Department of Computer and Information Science at the University of Pennsylvania . He leads research in systems, security, privacy, and networking, with a focus on privacy-preserving systems, accountability in online services, consistency in distributed systems, and next-generation operating systems. Research Areas: Systems, Security, Privacy, Networking, Distributed Systems, Operating Systems Labs & Teams: Distributed Systems Laboratory, Security and Privacy Laboratory, Warren Center for Network and Data Sciences Education: Ph.D. in Computer Science from the University of Texas at Austin (2018), ACM SIGOPS Dennis M. Ritchie Dissertation Award, Bert Kay Best Dissertation Award Recent Publications (2025–2022) span serverless computing, zero-knowledge proofs, distributed transactions, privacy-preserving ad tech, and verifiable execution. His 2025 work includes serverless workflow optimization and structural logic verification, while 2024 focuses on caching frameworks and stateful serverless. Earlier 2023–2022 projects include secure federated learning, confidential cloud services, and private information retrieval. Scientific Awards: NSF CAREER Award (2021) JPMorgan Faculty Award (2021) ACM SIGOPS Dennis M. Ritchie Dissertation Award (2018) Bert Kay Best Dissertation Award (2018) ACM SIGMOD Research Highlights Award (2024) VLDB 2024 Best Paper Award Nominee Advising: Current PhD Students: Elizabeth Margolin, Jess Woods, Yuxuan Zhang Current Masters Students: Felix Adena, Sydnie Shea Cohen Alumni: Eleftherios Ioannidis (2025), Yiping Ma (2025), Haoran Zhang (2024), Ke Zhong (2024), Selin Butun (2025), Martin Sander (2025), Seungmin Han (2024), Andrew Beams (2022), Yifeng Mao (2021), Varad Deshpande (2020) Current Courses: CIS 4510/5510: Computer and Network Security (Fall 2025). He also organizes conferences like S&P, OSDI, SOSP, PETS, and EuroSys.

• Edge-Cloud Computing
• Resource Optimization
• Applied Machine Learning
• +۵ مورد دیگر

Jia Hu is an Associate Professor in Computer Science at the University of Exeter. He holds a PhD in Computer Science from the University of Bradford (2010), and M.Eng/B.Eng degrees in Electronic Engineering from Huazhong University of Science and Technology. His research specializes in edge-cloud computing, federated learning, and AI-driven optimization for networks and IoT systems. Research Interests: Hu's work spans resource optimization, applied machine learning (particularly in distributed settings), network security, blockchain integration, and intelligent systems for electric vehicles and Industry 4.0. His recent projects focus on federated edge AI, 6G-enabled industrial IoT, and real-time federated learning via hardware-algorithm co-design. Publications: His 150+ papers emphasize federated learning, edge computing, and reinforcement learning applications. Recent works (2020–2025) show a strong trend toward optimizing AI at the network edge, with themes like digital twins, blockchain security, and EV-integrated systems dominating. Awards & Recognition: Best Paper Awards: IEEE SOSE'16, IUCC'14 Outstanding Service & Leadership Awards for IEEE conferences Top 4% contributor to EPSRC Peer Review Fellow of the Higher Education Academy (HEA) Grants & Projects: Secured €4.7M+ funding from EU Horizon, EPSRC, and Royal Society for projects including: SAILING (Secure AI for Smart Internet-of-Energy, €3.6M) REFINE (Real-time Air Quality Monitoring with UAVs, €897K) SustainAIRA6G (Energy-Efficient AI for 6G Networks, £118K) Advising: Supervised 12 PhD students to completion; currently mentoring 7 students in federated learning, edge computing, and AIoT.

• Cybersecurity
• Cryptography
• Information Security
• +۷ مورد دیگر

Dr. Viktor Melnyk serves as an Assistant Professor in the Department of Applied Computer Science at the Institute of Mathematics, Informatics and Landscape Architecture within the Faculty of Natural Sciences and Technology at John Paul II Catholic University of Lublin. His academic activities span teaching, research, and extensive student supervision across multiple years. His research interests focus on cybersecurity, cryptography, and FPGA-based systems, with particular expertise in wireless network security (especially IEEE 802.15.4), hardware acceleration, and machine learning applications in electronic design. His work bridges theoretical computer science with practical security implementations, evident in his numerous publications and student thesis topics. Analysis of Dr. Melnyk's recent publications reveals a consistent focus on hardware security implementations, particularly for low-rate wireless networks and reconfigurable computing systems. His research trajectory shows increasing integration of machine learning techniques with traditional security protocols, reflecting broader trends in the field. The publications demonstrate both theoretical depth and practical implementation focus, with many papers addressing specific hardware implementations. Dr. Melnyk has served as an Independent External Expert for evaluating grant proposals in the European COST Open Call competition, indicating professional recognition of his expertise. His contributions to the academic community extend to reviewing numerous scientific articles across various domains including biomedical data processing, cyber-physical systems, and cloud computing. His academic advising is exceptionally active, with documentation of supervising over 100 diploma theses from 2015 through 2025 across both Bachelor's and Master's levels. The thesis topics consistently align with his research interests, covering cybersecurity, cryptography, network security, and hardware implementations. He teaches courses including 'Operating Systems,' 'Network Data Protection Technologies,' and 'Multimedia Systems,' with teaching materials adapted for remote delivery during the pandemic period.

Zhao Zhigangمشاهده پروفایل

دانشیار

• Hyperspectral Imaging
• Machine Learning
• Embedded Systems
• +۵ مورد دیگر

Zhao Zhigang is an Associate Professor at the School of New Materials and New Energy, Shenzhen University of Technology, where he has been employed since May 2017. Previously, he served as a Lecturer at the School of Optoelectronic Engineering, Shenzhen University (2013-2017) and completed postdoctoral research at Shenzhen University (2010-2012) after earning his PhD from Huazhong University of Science and Technology. His academic journey began with undergraduate and master's studies at PLA Ordnance Engineering College (now Army Engineering University). His educational background includes: PhD in Optical Engineering, Huazhong University of Science and Technology (2005-2010) Master's in Optical Engineering, PLA Ordnance Engineering College (2002-2005) Bachelor's in Military Optoelectronic Engineering, PLA Ordnance Engineering College (1995-1999) Zhao's research focuses on hyperspectral imaging systems and machine learning applications for material classification. His work emphasizes embedded image data acquisition and processing using ARM and FPGA platforms, with significant contributions to micro-hyperspectral imaging technology. His research spans three primary areas: hyperspectral image processing on ARM/FPGA systems, machine learning applications in spectral analysis, and embedded AI implementations on FPGA/Zynq platforms. This interdisciplinary work bridges optical engineering, computer vision, and hardware design. Analysis of his recent publications reveals a strong emphasis on hyperspectral data compression techniques , machine learning applications for spectral analysis , and embedded system implementations . His work demonstrates a consistent focus on practical applications of hyperspectral imaging in fields ranging from food quality assessment to battery health monitoring, with increasing incorporation of deep learning techniques in recent years. His scientific recognition includes: Multiple teaching awards at Shenzhen University of Technology (2019-2024) Shenzhen City high-level professional talent designation (2016) Numerous national competition awards as student supervisor (2016-2023) Outstanding Paper Award at Shenzhen Optical Society (2010) Zhao has secured substantial research funding as Principal Investigator, including horizontal projects (2023-2024), Shenzhen Postdoctoral Research Funding (2019-2020), and Shenzhen Basic Research Projects. He has successfully guided students in academic competitions, resulting in five national first prizes. His research group maintains strong industry connections through multiple school-enterprise cooperation projects focused on practical applications of hyperspectral imaging technology. His laboratory work centers on FPGA-based embedded systems for hyperspectral imaging, with recent projects developing micro-hyperspectral spectrometers for UAV platforms, real-time video processing systems, and specialized hardware for spectral data acquisition and compression. These efforts demonstrate a clear trajectory from fundamental optical engineering toward practical applications of machine learning in spectral analysis.