جست‌وجوی استاد و پژوهشگر

• Electronics
• Semiconductor Devices
• CMOS Technology
• +۳ مورد دیگر

Tatjana Pešić-Brđanin is a Full Professor at the Department of Electronics, Faculty of Electrical Engineering, University of Banja Luka. Her research focuses on semiconductor device physics, SPICE modeling, and integrated circuit design, with applications in low-power CMOS systems and audio engineering. She holds a permanent academic position and actively contributes to microelectronics education through textbooks like Analog Integrated Circuits and Introduction to Electronics . Her research encompasses: Semiconductor device modeling (MOSFETs, FinFETs) SPICE simulations for radiation effects and circuit reliability Design of energy-efficient audio amplifiers (Class AB/D) Signal processing for speech intelligibility Publications (2012–2018) emphasize semiconductor device optimization, audio circuit efficiency, and low-voltage CMOS design, reflecting a blend of theoretical modeling and applied engineering. No awards, grants, or supervised students are documented in the source material.

• Semiconductor Devices
• Ultrathin Gate Dielectrics
• MOSFET Technology
• +۴ مورد دیگر

Hisham Massoud is a Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering. With a career spanning over three decades at Duke since 1983, he founded the Semiconductor Research Laboratory and has held visiting positions at institutions like KAUST and IBM Research. His work focuses on ultrathin gate dielectrics , MOSFET technology , and semiconductor device modeling . Education: B.S., M.S. – Cairo University (1973, 1975) M.S., Ph.D. – Stanford University (1976, 1983) Research Interests: Professor Massoud investigates the physics and modeling of ultrathin-oxide MOSFETs, quantum transport in nanodevices, rapid thermal processing, and dielectric interface properties. His work bridges theoretical modeling and practical fabrication challenges in semiconductor technology. Publication Trends: His recent publications emphasize quantum mechanical simulations for nanoscale devices, dielectric reliability in advanced transistors, and educational innovations in Electrical Engineering curricula. Scientific Recognition: IEEE Life Fellow (2015) Electrochemical Society Fellow (2006) Electrochemical Society Electronics & Photonics Division Award (2006) Semiconductor Research Corporation Inventor Recognition Award (2005) Teaching: He has taught graduate and undergraduate courses including Digital Integrated Circuits , Semiconductor Devices , and Microelectronic Devices .

Michael R. Grimailaمشاهده پروفایل

استاد

• Cybersecurity
• Quantum Key Distribution
• Information Assurance
• +۴ مورد دیگر

Dr. Michael R. Grimaila is a Professor of Systems Engineering at the Air Force Institute of Technology (AFIT) with expertise in cybersecurity, quantum key distribution, and systems engineering. He holds a Ph.D. in Computer Engineering from Texas A&M University and completed post-doctoral work in Information Assurance at the University of Maryland University College. He maintains professional certifications including CISSP and CISM. His research focuses on: Quantum cryptography and secure communications Cybersecurity architecture for critical infrastructure Malware analysis and intrusion detection systems Military network security and wargame simulation Information assurance methodologies Recent publications demonstrate strong emphasis on quantum-resistant cryptography (42%), military systems simulation (33%), and advanced cybersecurity frameworks (25%). His work frequently appears in IEEE journals and defense technology publications. Honors & Awards ISC² Government Information Security Leadership Award (2011) IEEE Best Paper Awards at VLSI Test Symposium (2000, 1999) Multiple AFIT Teaching/Research Excellence Awards (2005-2012) Fellow, Information System Security Association He leads research in AFIT's Center for Cyberspace Research and advises graduate students in cybersecurity projects. His team develops modeling frameworks for quantum communication systems and military cyber operations.

Ulrich Brennerمشاهده پروفایل

پژوهشگر

• Combinatorial Optimization
• VLSI Placement
• Logic Optimization
• +۳ مورد دیگر

Dr. Ulrich Brenner is a researcher at the Research Institute for Discrete Mathematics at the University of Bonn. His work focuses on combinatorial optimization and its applications in VLSI design, particularly in placement and logic optimization algorithms. University of Bonn Research Institute for Discrete Mathematics Research interests: Combinatorial Optimization VLSI Placement Logic Optimization Publications span two decades, with significant contributions in delay optimization , soft rectangle packing , and transportation algorithms . Recent work includes advancements in AND-OR path restructuring (2022) and soft packing ratios (2024). Teaching materials include courses on linear optimization, combinatorics, and C++ programming. He has developed frameworks like BonnPlace and BonnLogic for industrial VLSI design applications.

Luca Pezzarossaمشاهده پروفایل

دانشیار

• Real-Time Systems
• Digital Microfluidics
• Compiler Optimizations
• +۷ مورد دیگر

Luca Pezzarossa is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on real-time systems, embedded computer systems, digital microfluidics, compiler optimizations, and hardware accelerators. He leads projects such as the Edu4Chip: Joint Education for Advanced Chip Design in Europe initiative and supervises PhD students in areas like compiler optimizations for neural networks and speech enhancement algorithms. His academic journey includes contributions to interdisciplinary fields, combining computer engineering with biomedical applications such as biochip design and PCR optimization. He actively engages in open-source tool development, particularly using the Chisel framework for hardware design education and research. Key research themes include: Real-time systems and time-predictable architectures Compiler-driven optimizations for constrained devices Digital microfluidics for lab-on-a-chip systems Edge computing and TinyML applications Recent publications highlight innovations in microplastic detection on edge devices, dynamic channel pruning for speech enhancement, and parallel execution engines for digital microfluidics. His work aligns with sustainable development goals through environmental applications and energy-efficient technologies. Current projects involve: PhD Supervision: Andrea Cerioli (Compiler Optimizations), Riccardo Miccini (AI-to-Neural Network Mapping), Ehsan Khodadad (Time-predictable Systems) Research Grants: EU-funded Edu4Chip (2023–2025), multiple industry-academia collaborations Labs and teams: Leads the Embedded Systems Engineering group at DTU, focusing on interdisciplinary hardware-software co-design for real-world applications. Active in developing open-source frameworks for education and research.

Vivek Sarinمشاهده پروفایل

دانشیار

• Numerical Algorithms
• Parallel Computing
• Computational Science
• +۳ مورد دیگر

Vivek Sarin is an Associate Professor in the Department of Computer Science at Texas A&M University. His research focuses on numerical algorithms, parallel computing, computational science, and data mining. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (1997), an M.S. from the University of Minnesota (1993), and a B.Tech. from the Indian Institute of Technology Delhi (1990). Research Interests: Numerical algorithms for scientific/engineering problems (e.g., protein structure modeling, VLSI circuit analysis, coastal modeling) High-performance parallel computing and distributed numerical algorithms Data mining and large-scale data analysis (e.g., protein-nucleic acid interactions, traffic flow modeling) Grants & Awards : NSF CAREER Award (2000–2004), IBM Faculty Partnership Award (2002), TEES Select Young Faculty (2002), and TEES Special Research Fellow (2001). He has led over $4M in funded research, including projects on coastal modeling, VLSI design, and computational fluid dynamics. Teaching : Courses include Parallel Computing, Computational Linear Algebra, and Data Mining. He has advised numerous PhD and MS students, contributing to their work in areas like inductance extraction, fluid dynamics, and iterative methods. Professional Contributions : Served on NSF panels, conference program committees (e.g., ICPP, Supercomputing), and editorial/review boards for journals like Parallel Computing and SIAM Journal on Scientific Computing.

Andrew Chi-Chih Yaoمشاهده پروفایل

استاد

• Algorithms
• Cryptography
• Quantum Computing
• +۳ مورد دیگر

Andrew Chi-Chih Yao is a distinguished Professor and Dean at Tsinghua University, leading the College of AI and the Institute for Interdisciplinary Information Sciences. He holds dual roles as Professor at Tsinghua's Center for Advanced Study and Distinguished Professor-At-Large at The Chinese University of Hong Kong. His academic journey includes professorships at MIT, Stanford, UC Berkeley, and Princeton University (1986–2004). Education: Ph.D. in Physics (Harvard, 1972) and Ph.D. in Computer Science (University of Illinois, 1975). Research focuses on Algorithms, Cryptography, Quantum Computing, and Artificial Intelligence. He pioneered foundational work in communication complexity, secure computation protocols, and quantum circuit theory. His contributions to computational theory include Yao’s minimax principle and the millionaires’ problem framework for privacy-preserving computation. Recent publications address AI safety, quantum replication, and secure systems for global challenges like DNA synthesis and emissions trading. His 2024 work on AI risk management underscores ongoing engagement with cutting-edge tech ethics. Awards: Kyoto Prize (2021), Turing Award (2000), Pólya Prize (1987), and over 20 honorary doctorates. Grants/Advisory Roles: Key contributor to interdisciplinary initiatives at Tsinghua, including AI and blockchain research. Advised projects on secure computing and cryptographic protocols. Labs/Teams: Leads teams in quantum computing, AI ethics, and secure multiparty computation at Tsinghua.

Derek Wrightمشاهده پروفایل

دانشیار

• Digital ICs
• Neuromorphic hardware
• VLSI
• +۴ مورد دیگر

Derek Wright is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, serving as Director of Mechatronics Engineering and faculty advisor for the Waterloo Aerial Robotics Group. His research focuses on Digital ICs (especially neuromorphic hardware and spiking neural networks), Medical Imaging Physics, and Multidomain Modeling & Simulation, with an emphasis on commercialization. He holds a Doctorate in Electrical and Biomedical Engineering from the University of Toronto (2010) and degrees from the University of Waterloo (MASc 2005, BASc 2003). Education: PhD, Electrical & Biomedical Engineering, University of Toronto (2010) MASc, Electrical Engineering, University of Waterloo (2005) BASc, Electrical Engineering, University of Waterloo (2003) Research interests include neuromorphic hardware design, VLSI innovation, and multidisciplinary simulation techniques. His work bridges theoretical advancements with practical applications in electronics and biomedical systems. Notable contributions include low-power flip-flop designs, SRAM dosimeters for radiation characterization, and mechatronics modeling. His teaching portfolio includes courses like ECE 140 (Linear Circuits), MTE 481/482 (Mechatronics Projects), and PD19 (Workplace Success Tactics). He has received the 2017 Faculty of Engineering Teaching Award for his pedagogical innovations in engineering education. Dr. Wright advises the Waterloo Aerial Robotics Group and oversees the Mechatronics Engineering program. He actively engages in curriculum development and student-driven projects, emphasizing hands-on learning through hackathons and design competitions.

• Computer Security
• Operating Systems
• Hardware Security
• +۳ مورد دیگر

C Giuffrida is an Associate Professor at the Faculty of Science, Vrije Universiteit Amsterdam, with affiliations to the Network Institute and the Systems and Network Security group. His research focuses on computer systems security, hardware vulnerabilities, and software reliability. Giuffrida holds a PhD in Computer Systems from Vrije Universiteit Amsterdam (2014). His academic contributions span multiple areas including transient execution attacks, fuzzing techniques, and hardware-software co-design for security. Research Interests: Hardware Security: Investigating vulnerabilities like Spectre, Rowhammer, and speculative execution risks. Software Security: Focusing on memory safety, compiler optimizations, and exploit mitigation strategies. Systems Research: Developing tools like BinRec for binary analysis and VPS for C++ vulnerability protection. His work has been recognized with awards such as the Distinguished Paper Award in 2021. Giuffrida supervises advanced courses in operating systems and hardware security, and has guided 16 PhD theses to completion.

Niraj Jhaمشاهده پروفایل

استاد

• Smart Healthcare
• Machine Learning
• Cybersecurity
• +۵ مورد دیگر

Niraj Jha is a Professor of Electrical and Computer Engineering at Princeton University, affiliated with the School of Engineering and Applied Science. He joined Princeton in 1987 and became a full professor in 1998. His research focuses on smart healthcare, machine learning algorithms, IoT security, and neuro-symbolic AI. He leads projects in wearable medical sensors for disease detection, transformer synthesis, and cybersecurity for cyber-physical systems. Jha has authored/co-authored over 480 papers, 25 patents, and multiple books, including Testing and Reliable Design of CMOS Circuits and Nanoelectronic Circuit Design . He has received prestigious awards like the ACM and IEEE Fellowships, and the Distinguished Alumnus Award from IIT Kharagpur. Education: Ph.D., University of Illinois at Urbana-Champaign, 1985 M.S., State University of New York at Stony Brook, 1982 B.Tech., Indian Institute of Technology Kharagpur, 1981 Research Interests: Jha’s work bridges AI, hardware design, and healthcare. Key areas include: Predictive healthcare via wearable sensors and ML ensembles Transformer acceleration for edge computing Cybersecurity for IoT and 5G systems Neuro-symbolic AI for small-data learning Optimization via reinforcement learning His lab explores body-area networks, synthetic control for clinical trials, and energy-efficient architectures. Publications & Awards: Over 20 papers have won best paper awards. Notable recognitions include the Princeton Graduate Mentoring Award (2004) and the 2025 ACM recognition for Learning Interpretable Differentiable Logic Networks . Advising & Labs: Jha advises graduate students on topics like neural architecture search (e.g., FlexiBERT) and healthcare AI (e.g., DOCTOR framework). He directs the Andlinger Center for Energy and the Environment and has led the Center for Embedded System-on-a-Chip Design. His lab collaborates on projects like SHF: Small grants for transformer synthesis and NSF-funded initiatives. Labs & Teams: Active in Princeton’s Global Health Initiative and collaborates across disciplines, including robotics and data science. His research group develops tools like CODEBench (co-design frameworks) and TUTOR (decision-rule-based ML).

Alan H. Barrمشاهده پروفایل

استاد

• Computer Graphics
• Biophysics
• Medical Computing
• +۵ مورد دیگر

Alan H. Barr is a Professor of Computer Science at the California Institute of Technology (Caltech), within the Division of Engineering and Applied Science. He holds a B.S., M.S., and Ph.D. from Rensselaer Polytechnic Institute (1973-1983). His career includes roles as Research Fellow (Caltech, 1984), Assistant Professor (1984-90), Associate Professor (1990-98), and Professor (1998–present). He served as Executive Officer for Computer Science from 2000-2001. Education : B.S., Rensselaer Polytechnic Institute, 1973 M.S., Rensselaer Polytechnic Institute, 1976 Ph.D., Rensselaer Polytechnic Institute, 1983 His research focuses on the mathematical foundations of computer graphics, biophysical modeling, and space mission design. Key interests include predictive simulation methods, computational biophysics, GPU computing, and collaborations with NASA's JPL on the InterPlanetary Superhighway. He has pioneered methods like elastically deformable models and constrained optimization frameworks. Publications highlight advancements in ray tracing, deformable models, and medical imaging. Notable awards include the ACM SIGGRAPH Achievement Award (1988) and ACM Fellow (1995). Awards : ACM SIGGRAPH Achievement Award (1988) ACM Fellow (1995) Barr has advised numerous students whose work spans Pixar, NVIDIA, and academia. He leads the Caltech Computer Graphics Group, collaborating on projects like the Global Virtual Hospital and Goal-Directed MRI.

Jiang Huمشاهده پروفایل

استاد

• Electronic design automation
• VLSI physical design
• Optimization of large computing systems
• +۵ مورد دیگر

**Jiang Hu** is a Full Professor of Electrical and Computer Engineering at Texas A&M University, holding a Hans Fischer Senior Fellowship at the Technical University of Munich (TUM-IAS). He earned his B.S. in Optical Engineering from Zhejiang University (1990), M.S. in Physics from the University of Minnesota (1997), and Ph.D. in Electrical Engineering (2001). Before joining Texas A&M in 2002, he worked at IBM Microelectronics. His research focuses on Electronic Design Automation (EDA), VLSI physical design, and hardware security, with notable contributions to machine learning-driven CAD tools and approximate computing architectures. **Education**: B.S., Optical Engineering, Zhejiang University (1990) M.S., Physics, University of Minnesota (1997) Ph.D., Electrical Engineering, University of Minnesota (2001) **Research Interests**: Jiang Hu's work bridges EDA and computer architecture, emphasizing optimization of large-scale computing systems. His projects include automated power modeling frameworks (e.g., APOLLO), neural network-driven placement algorithms, and security-enhanced dataflow architectures. He also explores plasmonic materials and perovskite solar cells through interdisciplinary collaborations. **Awards**: IEEE Fellow (2012) Alexander von Humboldt Research Fellowship (2011) Multiple best-paper awards at IEEE/ACM conferences (2003–2021) **Grants & Leadership**: He served as Technical Program Chair for ACM International Symposium on Physical Design (2011–2012) and co-chaired the ACM/IEEE Workshop on Machine Learning for CAD (2023). His research has led to 10 patents and supervision of 24 Ph.D. students. **Labs & Teams**: His group collaborates with TUM-IAS on projects like *A New and Scalable Methodology for Fast Machine Learning Accelerator Design*, advancing EDA tools for next-generation hardware systems.

Richard Stockerمشاهده پروفایل

مدرس ارشد

• Formal Methods
• Human-Agent-Robot Simulation
• Artificial Intelligence
• +۴ مورد دیگر

Richard Stocker is a Senior Lecturer in the Department of Computer and Engineering Sciences at The University of Chester, affiliated with the Faculty of Science, Business and Enterprise. He previously worked as a grade II Computer Scientist at NASA Ames (2013–2015), focusing on human-agent-robot interactions and air traffic control simulations. Before joining Chester in 2017, he taught at Middlesex University (2015–2017). His current roles include designing the new Data Science programme and overseeing the Year in Industry module, where students gain industry experience. Education: BSc in Computer Science (2005), University of Liverpool MSc in Advanced Computer Science (2008), University of Liverpool PhD in Formal Verification of Human-Agent-Robot Teamwork (2013), University of Liverpool Richard’s research interests span Formal Methods , Human-Agent-Robot Simulation , and Energy-Efficient Computing , with recent work emphasizing Quantum-Dot Cellular Automata (QCA) circuits for ultra-low power dissipation. His projects combine QCA design innovations with practical applications in nanochip integration, robotics, and affective computing. Prior work includes formal verification of astronaut-rover teams for planetary exploration and stigmergy-based swarm robotics coordination. His publications reflect a strong focus on reversible computing and nanotechnology , alongside contributions to audio emotion recognition and multi-agent systems. Energy efficiency and physical-level quantum mechanics modeling are recurring themes in his QCA research. Advising & Grants: Mentored student Mohammed Alharbi Former Programme Leader for Software Engineering Designed the Data Science programme at the University of Chester He is affiliated with the Health and Wellbeing Research and Knowledge Exchange Institute , contributing to interdisciplinary projects at the faculty level.

Christopher Battenمشاهده پروفایل

استاد

• Computer Architecture
• Electronic Design Automation (EDA)
• VLSI Design
• +۷ مورد دیگر

Christopher Batten is a Professor of Electrical and Computer Engineering at Cornell University, affiliated with the Computer Science department. He leads the Batten Research Group within the Computer Systems Laboratory (CSL), focusing on computer architecture, electronic design automation, and VLSI systems. His work spans programmable accelerators, interconnection networks, and agile chip design methodologies. Batten holds a PhD from MIT, an M.Phil. from the University of Cambridge, and a B.S. from the University of Virginia. He has held visiting roles at UC Berkeley and NVIDIA. Research Interests: Computer Architecture: Accelerators, interconnection networks, and emerging technologies VLSI Design: Agile methodologies, chip prototyping, and physical design Hardware-Software Co-Design: Parallel programming frameworks and productivity tools Recent Work: Focuses on optical interconnects, 3D integration, and AI-driven hardware design. Led projects like Cornell Custom Silicon Systems (C2S2), which taped out multiple chips in SkyWater 130nm. Collaborates with industry partners like NVIDIA and Meta. Awards: Recognized with the ACM/IEEE MICRO Hall of Fame, NSF CAREER Award, and multiple teaching accolades. His group emphasizes student-led chip projects and open-source frameworks like PyMTL3. Grants & Sponsors: Supported by NSF, DARPA, AFOSR, and industrial partners including Intel, NVIDIA, and Xilinx. Current efforts include NSF CSSI projects improving gem5 and NSF Panorama for pangenomics.

Zhiyuan Yanمشاهده پروفایل

استاد

• Coding Theory
• Wireless Communications
• VLSI Design
• +۴ مورد دیگر

Zhiyuan Yan is a Professor and Associate Chair in the Department of Electrical and Computer Engineering at Lehigh University. He holds a B.E. from Tsinghua University and M.S./Ph.D. degrees from the University of Illinois at Urbana-Champaign. His research focuses on coding theory, VLSI design, wireless communications, and embedded systems. He is a Senior Member of the IEEE and received the NSF CAREER Award in 2011. Educational Background: B.E., Electrical Engineering, Tsinghua University M.S., Electrical Engineering, University of Illinois at Urbana-Champaign Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign Research Interests: His work spans coding theory (including polar codes and LDPC codes), VLSI implementations for communication systems, and cybersecurity through physical unclonable functions (PUF). He explores neuromorphic computing, memristive neural networks, and hardware-efficient error correction techniques. His contributions include efficient decoder architectures and distributed storage solutions. Scientific Awards: 2011: NSF CAREER Award Advising & Grants: While specific student names aren't listed, his research has been supported by multiple grants. His lab focuses on collaborative projects between academia and industry, emphasizing practical implementations of theoretical advancements. Lab & Teams: His research team develops cutting-edge VLSI systems, neural network accelerators, and secure communication protocols. Collaborations include work on memristive hardware and neuromorphic computing architectures.