جست‌وجوی استاد و پژوهشگر

Syed Rafay Hasanمشاهده پروفایل

استاد

• Hardware Security
• Deep Learning
• Internet of Things
• +۴ مورد دیگر

Syed Rafay Hasan is a Professor in the Department of Electrical and Computer Engineering at Tennessee Tech University , Cookeville, TN. He holds a Ph.D. in Electrical Engineering (2009) and M.Eng. (2002) from Concordia University, Montreal, Canada, and a B.Eng. (1997) from NED University, Karachi, Pakistan. Research Focus: Hardware Security , Deep Learning , Internet of Things , Edge Intelligence , and Digital VLSI Design Current Projects: Center for Agile and Intelligent Power Systems (CAIPS) cybersecurity research (subcontractor role with Florida International University, $160,000 budget) Key Contributions: 15+ years of continuous NSF REU Site leadership (2016–2019, renewed 2018) for cybersecurity-focused undergraduate research His recent work examines edge computing vulnerabilities , including adversarial attacks on CNN inference ( StAIn , 2023), hardware intrinsic attacks ( SoWaF , 2021), and runtime Trojan detection ( MacLeR , 2020). Publications span hardware security in 3D ICs, formal verification techniques, and FPGA-based neural network implementations. Scientific Recognition : Best Paper Award (Honorable Mention) in Multimedia Systems and Applications at VTC-2024 Research Trends : 15 most recent articles (2021–2025) analyze edge intelligence security (60%), collaborative CNN models (45%), and hardware Trojan detection (35%) using machine learning and formal verification Students under his supervision have secured positions at Intel , Georgia Tech Research Institute , and Whirlpool Inc . He teaches core courses in computer design (ECE 4120/5120, 11 years), Digital VLSI (ECE 4130, 10 years), and deep learning implementation (ECE 6900). Collaborations include institutions in Canada, UAE, Pakistan, and industry partners like Intel and Air Force Research Lab .

Vanessa Chenمشاهده پروفایل

استادیار

• Ubiquitous Sensing Systems
• Hardware Security for IoT Devices
• Analog/Mixed-Signal Integrated Circuits
• +۴ مورد دیگر

Vanessa Chen is an Assistant Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), part of the College of Engineering. She holds a Ph.D. from CMU (2013) and prior academic appointments at The Ohio State University and Qualcomm. Her research focuses on hardware security for IoT devices, analog/mixed-signal circuits, and ubiquitous sensing systems, with notable contributions in RF fingerprinting and machine learning integration. She leads the Energy-Efficient Circuits and Systems Lab and has received prestigious awards including the NSF CAREER Award (2019). Education : Ph.D., Electrical and Computer Engineering, Carnegie Mellon University (2013) M.S., Electrical Engineering, National Tsing Hua University, Taiwan (2005) B.S., Electrical Engineering, National Tsing Hua University, Taiwan (2003) Research Interests : Chen’s work spans secure analog/RF electronics, brain-machine interfaces, and energy-efficient data conversion systems. She pioneers methods to detect hardware Trojans via electromagnetic side-channels and designs lightweight, energy-efficient transceivers for IoT. Her projects integrate machine learning for real-time threat detection and system optimization. Awards & Grants : NSF CAREER Award (2019) EAGER Grant for Real-Time Learning in Secure RF Electronics (2020) SpecEES Grant for Trusted Wireless Transceivers (2019) Advising & Outreach : Collaborates with industry partners like Apple and Analog Devices. Develops educational initiatives integrating secure sensor design into undergraduate/graduate curricula. Her lab emphasizes interdisciplinary approaches to tackle emerging cybersecurity challenges in IoT and wireless systems.

• Power Electronics
• Power System Stability
• Renewable Energy Integration
• +۳ مورد دیگر

Yunjie Gu is a Lecturer in the Department of Electrical and Electronic Engineering at Imperial College London, affiliated with the Control and Power Research Group. Previously, he served as an EPSRC Innovation Fellow (2018–2020) and worked at GE Global Research in Shanghai, leading projects on renewable energy grid integration. His research focuses on power-electronic-based power systems, including grid stability, impedance modeling, numerical computation methods, and real-time digital twin systems. Key contributions include spinouts like Hoymiles (listed in Shanghai Stock Exchange) and open-source projects such as RT-Thread and Simplus Grid-Tool. His research interests span power electronics, renewable energy systems, computational methods for power system analysis, and real-time computing. He actively explores stability analysis of inverter-based grids, transient stability of grid-following inverters, and data/model-driven system identification. Gu holds seven patents (six Chinese, one US/EU) and mentors PhD students in related disciplines. He advocates for hybrid approaches combining traditional power system analysis with modern computational techniques. Gu's work integrates academic research with industry applications, emphasizing practical solutions for emerging challenges in decarbonized grids. His recent projects include real-time digital twin systems for grid simulation and FPGA-based hardware-in-the-loop testing. He collaborates widely, contributing to both foundational theory and commercial innovations in energy systems.

• Data Compression
• Hardware Design (FPGA, Embedded Systems)
• Biomedical Electronics
• +۷ مورد دیگر

Marco Mattavelli is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI) and the Microsystems Engineering Laboratory (MM). His research focuses on data compression, hardware design for embedded systems, biomedical electronics, and blockchain/metaverse applications. He leads the GRAMM lab and has contributed to standards like MPEG-G for genomic data. His work spans FPGA-based systems, signal processing, and implantable medical devices. Education and affiliations: EPFL faculty since at least 2000, with roles in teaching and research. Collaborations include MPEG standards development, multidisciplinary projects in genomics and biomedical engineering. Research interests emphasize interdisciplinary innovation: genomic data compression, high-performance embedded systems, and leveraging blockchain for digital assets. His lab develops cutting-edge solutions for medical devices and energy-efficient electronics. Publications highlight contributions to neural clustering algorithms, genomic compression, and metaverse data analysis. Over 200 publications in top venues like IEEE Transactions and conferences like DCC, ICASSP, and BioCAS. Grants and labs: Active in securing research funding for projects in biomedical sensors, heterogeneous computing, and genomic standards. The GRAMM lab is central to his research activities.

• Computer Engineering
• DNA Sequencing
• Hardware Security
• +۲ مورد دیگر

Professor Sridevan Parameswaran is Head of the School of Electrical and Computer Engineering at the University of Sydney. He holds affiliations with the University of Sydney Nano Institute and previously served at UNSW and University of Queensland. His research focuses on hardware design for genomic analysis and computing systems. Research interests include computer engineering solutions for DNA sequencing, FPGA-based security systems, and resource-efficient deep learning accelerators. Recent publications demonstrate innovations in genomic data visualization and hardware optimization techniques. Current projects include ARC-funded research on custom computing for DNA analysis and developing efficient DNN accelerators.

Jeremy Herbertمشاهده پروفایل

مدرس

• Imaging
• Sensing
• Biomedical Engineering
• +۳ مورد دیگر

Dr Jeremy Herbert is a Lecturer at the School of Electrical Engineering and Computer Science under the Faculty of Engineering, Architecture and Information Technology at the University of Queensland . His research spans interdisciplinary domains including biomedical engineering, applied physics, and educational technology, with a focus on sensing systems and their applications in health monitoring and signal processing. Herbert's work involves innovative projects such as IoT-enabled health devices, optical interferometry for biomedical diagnostics, and algorithmic approaches to enhance student learning environments. His publications reflect a strong emphasis on real-time data processing, sensor design, and educational interventions. Currently available for supervision, Herbert's research integrates hardware development with health-focused solutions, supported by collaborations across engineering and medical disciplines. More information can be found in his full description below.

Chen Dingمشاهده پروفایل

استاد

• Locality theory and optimization
• Compilers and run-time systems
• Memory management
• +۳ مورد دیگر

Chen Ding is a Professor of Computer Science and Chair of the Department of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from Rice University (2000). His research focuses on program analysis, optimization, and memory management, particularly in the areas of locality theory, compilers, parallel programming, and high-performance computing. He has received prestigious awards including the DOE Early Career Principal Investigator award (2001), NSF CAREER award (2002), and IBM CAS faculty fellowship (2005-2009). Education: PhD in Computer Science, Rice University, 2000 Research Interests: Locality theory and optimization Compilers and runtime systems for parallelism Memory management strategies High-performance computing systems Key Contributions: His work on reuse distance in memory hierarchy and compiler-assisted cache management (CLAM) has been foundational in optimizing data movement and parallelism. Recent research explores data movement complexity for machine learning and generative AI-driven memory workload synthesis. Awards: IPDPS Best Paper Award (2001) DragonStar Lecturer (ICT China, 2008) Grants & Advising: As department chair, he oversees research grants and faculty development. No specific student advising details are listed here.

Konstantinos Nikitopoulosمشاهده پروفایل

استاد

• Advanced Physical Layer Processing Architectures
• Massively Parallel Algorithms for Communication Systems
• Non-Linear Signal Processing for 6G Systems and Beyond
• +۷ مورد دیگر

Konstantinos Nikitopoulos is a Professor at the Institute for Communication Systems, University of Surrey, and Director of the Wireless Systems Lab. He leads research in next-generation wireless communications, including Open RAN, non-linear processing, and neuromorphic computing. His work focuses on advancing MIMO systems, energy-efficient architectures, and practical implementations of 5G/6G standards. He has attracted over £11M in grants, including the HiPer-RAN and NL-COMM projects, and received awards for innovation and teaching. Education : PhD from National and Kapodistrian University of Athens Postdoctoral roles at RWTH Aachen University, UC Irvine, and UCL Research Interests : Non-linear signal processing for 6G systems Massively parallel algorithms and hardware acceleration Open RAN architectures and standard-compliant PHY design Unconventional computing (neuromorphic, quantum annealing) Awards : 2024 Innovator of the Year (University of Surrey) EPSRC First Grant (2021) Incremental Innovation Award (UK Department for Science, 2023) Grants & Projects : HiPer-RAN (£7.8M, DSIT) NL-COMM (£904K, Innovate UK) Tbps Communication System (£520K, Industry) Labs & Teams : Leads the Wireless Systems Lab and contributes to the 5G/6G Innovation Centre’s “Theory and Practice of Advanced Concepts” work area.

Yang Xuمشاهده پروفایل

استاد پژوهشی

• Software-Defined Networking
• Data Center Networks
• High-Speed Networks
• +۹ مورد دیگر

Yang Xu is a Research Professor in the Department of Electrical and Computer Engineering at the New York University Tandon School of Engineering. He previously served as a Visiting Assistant Professor at NYU-Poly (2007-2008) and holds a Ph.D. in Computer Science and Technology from Tsinghua University (2007), a Master of Science in Computer Science and Technology from Tsinghua University (2003), and a Bachelor of Engineering from Beijing University of Posts and Telecommunications (2001). Ph.D. (2007): Tsinghua University, Computer Science and Technology M.Sc. (2003): Tsinghua University, Computer Science and Technology B.Eng. (2001): Beijing University of Posts and Telecommunications His research focuses on network security, software-defined networks, high-speed networking, and hardware security. Key interests include developing solutions for data center networks, mitigating malware and rootkit threats using hardware performance counters, and evaluating side-channel vulnerabilities in FPGAs. He explores the dual impact of machine learning in hardware security, balancing its benefits and risks. His work also addresses cybersecurity challenges in industrial control systems. Dr. Xu’s recent articles highlight advancements in leveraging hardware resources for security, such as HPC-based malware detectors and signature-based ROP detection systems. His research trends emphasize practical cybersecurity transitions, embedded system security, and FPGA vulnerability assessments. No scientific awards or grants are explicitly listed in the provided text. He advises no current students, but his research has influenced embedded, firmware, and network security domains. He is affiliated with the High Speed Networking Lab , which specializes in data center network solutions and security tools. His office is located at 10.069 (2 MetroTech Center, 10th Floor), and he can be reached at yang@nyu.edu or (646) 997-3153.

• Biomedical Signal Processing
• Machine Learning
• Neonatal EEG Analysis
• +۵ مورد دیگر

William Marnane is a Professor of Electrical and Electronic Engineering at University College Cork (UCC). He holds a B.E. from UCC (1984) and a D.Phil. from the University of Oxford (1989). His career includes roles as Lecturer (1989), Senior Lecturer (1999), Dean of Graduate Studies (2013–2016), and Head of the School of Engineering (2016–2019). He has led major research initiatives, including the SFI-funded INFANT Centre and the Claude Shannon Institute. His research focuses on biomedical signal processing, machine learning, and neonatal EEG analysis, with notable contributions to neonatal seizure detection algorithms and fetal health monitoring. He has been awarded the Giner de Los Ríos Visiting Research Fellowship (2007, 2020) and leads projects funded by Wellcome Trust, Science Foundation Ireland, and EU grants. His educational background includes significant contributions to curriculum development and graduate training in engineering and biomedical sciences. He has supervised numerous research projects, including advancements in cryptographic hardware, embedded systems, and low-power signal processing architectures. Research highlights include the ANSeR neonatal seizure detection algorithm (Wellcome Trust-funded), clinical trials on EEG-based seizure recognition, and development of secure TLS coprocessors. He has published over 200 peer-reviewed articles, with key contributions in neonatal EEG analysis, machine learning applications in healthcare, and cryptographic processor design. Current research explores AI for fetal heart rate monitoring and wearable health technologies. Grants and partnerships include leadership roles in EU-funded projects, SFI Strategic Research Clusters, and industry collaborations. He co-directs the INFANT Centre, bridging engineering, medicine, and data science for translational research in neonatal care.

• Cybersecurity
• Hardware Security
• Embedded Systems
• +۵ مورد دیگر

Martin Gonzalez Honorio is an Associate Professor at the Department of Electronics Technology, Universidad Carlos III de Madrid. He serves as Subdirector of Students and Coordination of Final Year Projects. His research focuses on hardware security, embedded systems, and IoT security, with emphasis on physical unclonable functions (PUFs), radiation-hardened designs, and cryptographic primitives. He leads the Grupo Universitario de Tecnologías de Identificación (GUTI) research group and has contributed to over 30 publications since 2011. His work addresses challenges in secure authentication protocols, fault-tolerant electronics, and biomedical signal processing applications. Key areas: Hardware Security, IoT Infrastructure Protection, Radiation Effects on Electronics Notable contributions: SRAM PUF implementations, FPGA radiation hardening techniques, and TRNG designs using biometric data Recent research trends show strong focus on combining machine learning with hardware fingerprinting techniques for device identification, while maintaining emphasis on cryptographic protocol security. His publications reflect advancements in both theoretical foundations and practical implementations of secure embedded systems. No scientific awards or grants are explicitly listed, though his active research output indicates sustained academic engagement. His involvement in teaching and student coordination highlights commitment to academic development alongside technical research.

• Electronics Engineering
• Computer Science
• Fault-Tolerant Systems
• +۷ مورد دیگر

Marta Portela Garcia is an Associate Professor at the University Carlos III de Madrid, where she holds a position in the Academic Department of Electronic Technology. She is affiliated with the Microelectronic Design and Applications (DMA) research group and the University Institute on Gender Studies. Her expertise bridges hardware design, fault tolerance, and embedded systems reliability, with a focus on aerospace and medical technologies. Her research spans fault-tolerant hardware architectures, radiation effects on electronic circuits, affective computing for wearable devices, and cryptographic techniques for secure communication. Recent work includes advancing post-quantum cryptography for sensor networks and improving dependability in edge computing systems. Publications emphasize innovations in circuit hardening, fault injection methodologies, and FPGA-based co-design solutions. Notable contributions include radiation-resistant systems for space applications and emotion recognition via physiological signals. While no formal advisees are listed, her collaborative projects and research groups suggest strong involvement in academic mentorship. Grants and funding details are not explicitly stated in the provided texts. She actively contributes to the DMA research group and the University Institute on Gender Studies, integrating interdisciplinary approaches to solve challenges in microelectronics and technology ethics.

Sunil Khatriمشاهده پروفایل

استاد

• VLSI Design
• Secure Computing Systems
• Machine Learning Hardware
• +۷ مورد دیگر

Sunil Khatri is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Engineering and Systems Group. He holds a Ph.D. from the University of California, Berkeley (1999), an M.S. from the University of Texas at Austin (1989), and a B.S. from the Indian Institute of Technology Kanpur (1987). His academic progression includes roles as Assistant Professor (2000–2004) at the University of Colorado Boulder and Associate Professor (2010–2015) at Texas A&M. Educational Background: Ph.D., University of California, Berkeley, 1999 M.S., University of Texas at Austin, 1989 B.S., Indian Institute of Technology Kanpur, 1987 Research Interests: Intelligent and Secure Computing Systems: Focused on hardware architectures for machine learning (e.g., neuromorphic circuits, NoCs, sub-threshold circuits) and secure hardware design (blockchain, physically unclonable functions). Logic Synthesis and Applications: Includes VLSI CAD, gene regulatory network modeling, noise-based logic, and Boolean satisfiability (SAT) solvers. Interdisciplinary Extensions: Explores routing table compression, radar signal processors, optical networking (DWDM), and LDPC decoders. Research Trends in Publications: Recent work emphasizes neuromorphic hardware acceleration (e.g., CapsPDNet for insulator discharge prediction, FFIR filters for IoT), secure protocols (multi-factor authentication frameworks), and energy-efficient circuits (flash-based designs, low-voltage regulators). His contributions span VLSI, machine learning, and cybersecurity, often leveraging FPGA/GPU acceleration. Grants & Labs: Leads projects in the Computer Engineering and Systems Group, focusing on secure computing, machine learning hardware, and interdisciplinary VLSI applications. Active in grant-driven research with a focus on practical implementations in industry.

Scott Rixnerمشاهده پروفایل

استاد

• Computer Architecture
• Operating Systems
• Virtualization
• +۳ مورد دیگر

Scott Rixner is a Professor of Computer Science at Rice University, leading the MCS@Rice and MDS@Rice online degree programs as Director of Professional Studies. He holds a PhD from MIT and specializes in computer architecture, operating systems, and virtualization. His research focuses on memory systems, networking, and Python runtime systems for embedded devices. He has 11 patents and contributed to open-source projects like Skulpt and Owl. Rixner has received the 2019 School of Engineering Curriculum Innovation Award and 2014 Outstanding Mentor Award. His teaching spans courses in computational thinking, computer systems, and virtualization, with a strong emphasis on curriculum development. Education: PhD in Electrical Engineering (MIT, 2001), MEng and BS in Computer Science (MIT, 1995). Research highlights include memory scheduling, FPGA accelerators for genomics, and transparent superpage management. He co-developed Hyper-Switch, a scalable software virtual switch, and Virtflex for NUMA topology adaptation. His work integrates hardware and software to enhance system performance and simplify software design. Recent projects include embedded Python interpreters (Owl) and collaborative online education tools. He advises on open-source contributions and leads initiatives to modernize computer science curricula. His awards reflect his impact on both technical innovation and student mentorship.

Jim Hoganمشاهده پروفایل

دانشیار

• Bioinformatics
• Visualization
• Machine Learning
• +۴ مورد دیگر

Associate Professor Jim Hogan is an academic leader in the School of Computer Science at Queensland University of Technology (QUT), serving as Academic Lead for Learning and Teaching. He holds a PhD in Computer Science (QUT) and a BSc (Hons) in Mathematics (University of Queensland). His expertise spans Bioinformatics, Visualization, Machine Learning, and Software Engineering (Agile Methodologies). He has secured $2M+ in research funding and authored over 100 publications, including works on sequence analysis, bioinformatics visualization, and cloud computing education. Teaching contributions include pivotal roles in modernizing software engineering curricula (e.g., introducing Agile Methodologies and Australia’s first Cloud Computing course). Awards include the QUT Distinguished Teaching Award and a Federal Carrick Institute Citation. He supervises student projects in bioinformatics, visualization, and computational methods. Collaborations with industry (Microsoft, AWS, CSIRO) and leadership in projects like the ARC Linkage-funded 'Visual Analytics for Next-Gen Sequencing' highlight his interdisciplinary impact. Education: PhD (QUT), BSc(Hons) & BSc (UQ) Awards: QUT Distinguished Teaching Award, Carrick Citation Industry: Agile course materials used to train hundreds of engineers Research focuses on sequence analysis, bioinformatics visualization, and applications in genomics. Recent work includes signature-based clustering for metagenomic data and protein interaction prediction using machine learning. He leads student projects in bioinformatics visualization, cloud computing, and genomic network analysis.