جست‌وجوی استاد و پژوهشگر

• Computer-Aided Design
• Energy-Efficient Systems
• VLSI Design
• +۳ مورد دیگر

Sarma Vrudhula is a Professor at the School of Computing and Augmented Intelligence, Arizona State University (ASU). He holds a Ph.D. in Electrical Engineering from the University of Southern California (1985). Previously, he was a professor at the University of Arizona and served as the founding director of the NSF UA/ASU Center for Low Power Electronics. He is an IEEE Fellow recognized for contributions to low-power and energy-efficient digital circuit design. Educations: Ph.D. Electrical Engineering, University of Southern California (1985) M.S. Electrical Engineering, University of Southern California (1980) Bachelor's in Mathematics (Computer Science and Mathematical Statistics), University of Waterloo, Canada (1976) Research Interests: His work focuses on design automation, energy management in digital systems, statistical analysis of process variations, threshold logic circuits, and emerging technologies. He has pioneered methodologies for low-power VLSI design, thermal management of multi-core processors, and hardware implementations of threshold logic using spintronic devices. Publications: His recent research includes scalable energy-efficient architectures for AI, in-memory computing, and reconfigurable threshold logic gates. Key topics span energy efficiency in edge computing, neuromorphic systems, and sustainable VLSI design. Awards: IEEE Fellow (2005) Best Paper Award (2008) for macro cell characterization methodology Service & Grants: He led the NSF IUCRC Consortium for Embedded Systems and served on editorial boards. His grants include projects on threshold logic synthesis, energy-aware embedded systems, and hardware acceleration for neural networks. Courses Taught: Algorithmic Foundations of CAD for Digital Systems Computer Architecture Discrete Mathematics for Engineers

Chenchen Liuمشاهده پروفایل

استادیار

• High-performance computing for machine learning
• Brain-inspired computing
• Hardware-software co-design
• +۴ مورد دیگر

Dr. Chenchen Liu is an Assistant Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). She is affiliated with the Computing Compass Laboratory and holds a Hans Fischer Fellowship at the Technical University of Munich Institute for Advanced Study (TUM-IAS) under the focus group Enabling Neuromorphic Computing for Multi-Tenant AI. Her work bridges hardware-software co-design with neuromorphic computing. Ph.D., Electrical and Computer Engineering, University of Pittsburgh (2017) M.S., Electrical and Computer Engineering, Peking University (2013) Her research focuses on high-performance computing for machine learning through novel computer architecture and system designs, brain-inspired computing, machine learning security, non-volatile memory, and VLSI design. Key areas include: Neuromorphic hardware resilience and optimization Memristor-based neural network architectures Runtime scheduling for multi-tenant AI Security in neuromorphic computing Energy-efficient memory systems Recent publications explore memristor defect tolerance, ReRAM-based CNN training efficiency, and spiking network quantization. The work emphasizes hardware-software co-design for AI acceleration. NSF Career Award (2023) Best Poster Award, Machine Learning and Systems Conference (2022) Best Paper Award, IEEE Symposium on VLSI (2014) She contributes to academic service as TPC Chair/Track Chair for DAC, GLVLSI, Cloud Summit conferences and serves as Associate Editor for IEEE Transactions on Circuits and Systems (TCAS-1) and Neurocomputing journal.

Joel Grodsteinمشاهده پروفایل

مدرس

• VLSI Design
• Computer Architecture
• Computer-Aided Design (CAD)
• +۴ مورد دیگر

Joel Grodstein is a Lecturer in the Department of Electrical Engineering at Tufts University's College of Engineering. He holds a BSEE from Case Western Reserve University (1981) and an MSCS from the University of Utah (1986). His research spans VLSI design, computer architecture, and interdisciplinary bioelectricity studies, focusing on the intersection of hardware-software systems and biological applications. He teaches courses on real-time embedded systems, parallel computing, bioelectricity, and digital design verification. His career includes roles at Digital Equipment Corporation, Compaq, Intel, and now academia. His recent work bridges computational modeling of bioelectric networks with traditional hardware design, as seen in collaborations with Mike Levin's lab at Tufts. Courses like EE 123 (Bioelectricity) and new offerings like EE 152 (Real-Time Embedded Systems) reflect this dual focus. Publications emphasize symbolic timing analysis, CAD tools for VLSI, and bioelectric systems. He has advised no listed students but collaborates actively with industry partners (e.g., NVIDIA for EE 165). His lab work involves biophysical modeling and synthetic biology projects, as detailed in his recent bioelectricity-related papers.

• VLSI Design
• Computer-Aided Design
• Electronic Design Automation
• +۴ مورد دیگر

Matthew R. Guthaus is a Professor in the Electrical and Computer Engineering Department at the University of California Santa Cruz's Baskin School of Engineering. With an active research career spanning over two decades, his work focuses on VLSI design, Electronic Design Automation, and memory systems. His research interests include VLSI Design, Computer-Aided Design, Electronic Design Automation, Memory Design, Clock Distribution, Resonant Clocking, and Machine Learning for EDA. Guthaus has made significant contributions to open-source EDA tools, most notably OpenRAM, which has become an important resource for memory compiler development in academia and industry. Analysis of his recent publications (2021-2025) reveals a strategic evolution in his research, incorporating machine learning techniques into traditional EDA problems while maintaining strong foundations in circuit design. His work spans from fundamental circuit design to high-level system considerations, with particular emphasis on open-source frameworks that enable broader collaboration in the semiconductor community. Guthaus has been actively involved in national initiatives addressing semiconductor research and workforce development, as evidenced by his contributions to NSF workshops on integrated circuits. His recent publications show continued innovation in memory design, clock distribution, and the application of AI to traditional EDA challenges. His research has practical applications in low-power circuit design, high-performance computing systems, and the growing field of neuromorphic computing. The consistent publication record through 2025 demonstrates ongoing active research and leadership in the EDA community.

Ryan Robucciمشاهده پروفایل

دانشیار

• Hardware Security
• Analog and Mixed-Signal VLSI
• CMOS Image Sensors
• +۷ مورد دیگر

Dr. Ryan Robucci is an Associate Professor in the Department of Computer Science and Electrical Engineering at the University of Maryland Baltimore County (UMBC). He holds a Ph.D. from Georgia Institute of Technology (2009) and a B.S. from UMBC (2002). His research focuses on analog/mixed-signal VLSI, hardware security, embedded systems, and biologically-inspired systems. Key areas include side-channel attack mitigation, low-power wearable devices, and FPGA-driven embedded systems. Education: Ph.D. in Electrical and Computer Engineering, Georgia Tech, 2009 M.S.E.E., Georgia Tech, 2004 B.S. in Computer Engineering, UMBC, 2002 Research Interests: Hardware Security: Side-channel resistance, PUFs, and IC fraud detection Analog/Digital Hybrid Systems: Low-power sensors and reconfigurable circuits Embedded Systems: Wearable health monitoring and FPGA design His publications span hardware security, sensor networks, and embedded systems. He leads the Covail Lab, exploring ultra-low-power analog-digital systems and assistive technologies. Teaching includes courses on digital signal processing, FPGA design, and embedded systems.

Ramtin Zandمشاهده پروفایل

استادیار

• Hardware Design for Machine Learning Systems
• Neuromorphic Computing
• Emerging Nanoscale Electronics
• +۷ مورد دیگر

Ramtin Zand is an Assistant Professor in the Department of Computer Science and Engineering at the University of South Carolina's Molinaroli College of Engineering and Computing. His research focuses on hardware design for machine learning systems, neuromorphic computing, emerging nanoscale electronics (e.g., spintronic devices), and energy-efficient VLSI circuits. He leads the ICAS Lab, which explores reconfigurable architectures and low-power computing solutions. Education: Ph.D., Computer Engineering, University of Central Florida (2019) M.S., Electrical Engineering, Sharif University of Technology (2012) Research Interests: Dr. Zand’s work spans hardware-software co-design for AI, neuromorphic systems, and novel devices like MRAM and memristors. He emphasizes practical applications such as manufacturing anomaly detection, edge computing, and energy-efficient neural network deployment. Recent Trends in Articles: His publications emphasize hybrid PIM architectures, LLM-driven hardware design, and neuromorphic edge systems. Themes include optimizing communication efficiency, leveraging memristive crossbars, and bridging vision transformers with embedded platforms. Grants/Funding: Funded by AFRL for smart manufacturing projects ONR grants for perception systems research NSF support for in-memory computing Labs/Teams: The ICAS Lab collaborates on projects such as neuromorphic accelerators and low-power sensor systems. Recent milestones include DAC 2025 awards and CVPR 2025 demonstrations.

Chunhong Chenمشاهده پروفایل

استاد

• Digital Integrated Circuit Synthesis
• VLSI Computer-Aided Design
• High-Performance Low-Power Systems
• +۱ مورد دیگر

Dr. Chunhong Chen is a Professor in the Department of Electrical and Computer Engineering at the University of Windsor's Faculty of Engineering. He holds a PhD from Fudan University and is a P. Eng of Ontario and IEEE Senior Member. His research focuses on digital integrated circuit synthesis, VLSI CAD, high-performance low-power systems, and nanoelectronic circuit design. Education: PhD, Fudan University Recent work includes hybrid signal probability estimation methods, statistical delay modeling for nanoelectronics, and area-efficient cryptographic circuit design using hybrid SET-MOS technology. His research emphasizes practical applications in low-power systems and nanoscale device optimization. Dr. Chen's publications demonstrate expertise in both foundational theory and applied engineering solutions. He leads the Research Centre for Integrated Microsystems (RCIM), focusing on advanced microsystem integration challenges.

Ramesh Karriمشاهده پروفایل

استاد

• Hardware Cybersecurity
• Trustworthy Integrated Circuits
• Cyber-Physical Systems Security
• +۶ مورد دیگر

Ramesh Karri is a Professor and Chair of the Electrical and Computer Engineering Department at New York University Tandon School of Engineering. He co-founded the NYU Center for Cyber Security (CCS) in 2009 and co-directed it from 2016-2024. He is also a Fellow of the IEEE and has led initiatives such as the Embedded Systems Challenge (ESC), a red-blue team cybersecurity competition. Education: Ph.D. in Computer Science and Engineering, University of California, San Diego (1993) B.E. in Electrical and Computer Engineering, Andhra University (1985) Research Focus: His work centers on hardware cybersecurity, including trustworthy integrated circuits, cyber-physical systems, nano-enabled security, and biochip security. He pioneered security-aware CAD tools and has developed metrics for hardware trojan detection through initiatives like the Trust-Hub. Awards & Leadership: Recipient of the Humboldt Fellowship and NSF CAREER Award Editor-in-Chief of ACM Journal of Emerging Computing Technologies Leadership roles at IEEE conferences (ICCD, HOST, DFTS) Grants & Labs: Directs the Center for Advanced Technology in Telecommunications (CATT) and NYU CCS. His labs focus on additive manufacturing security, digital microfluidic systems, and AI-driven hardware design.

Weiping Shiمشاهده پروفایل

استاد

• VLSI Design
• Integrated Circuit Design
• Computer-Aided Design (CAD)
• +۵ مورد دیگر

Weiping Shi is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and M.S./B.S. degrees from Xian Jiaotong University. His research focuses on VLSI CAD, including physical design optimization, parasitic extraction, fault diagnosis, and electromagnetic analysis. He is particularly known for contributions to capacitance extraction algorithms, buffer insertion techniques, and power system simulation methods. Dr. Shi’s awards include the IBM Faculty Award, two Best Paper Awards at Design Automation Conferences, and election as an IEEE Fellow. His work bridges theoretical algorithm development with practical applications in semiconductor design and power systems. He maintains an active research lab and contributes to industry-relevant solutions for integrated circuit challenges. His office is located in 333K Wisenbaker Engineering Building (WEB), and he can be reached at wshi@tamu.edu . Education: Ph.D., University of Illinois at Urbana-Champaign M.S., Xian Jiaotong University B.S., Xian Jiaotong University Awards & Honors: IBM Faculty Award Best Paper Award, Design Automation Conference Best Paper Award, Asia and South Pacific Design Automation Conference IEEE Fellow Research Focus: Computer-Aided Design (CAD) of VLSI systems 3D capacitance and inductance extraction Optimal buffer insertion algorithms Electromagnetic transient simulation Defect diagnosis and process variation analysis Advancing Technology: His recent work integrates machine learning for defect classification and hierarchical algorithms for power system simulation, reflecting a commitment to both foundational research and industrial impact.

• VLSI Design
• Digital Circuit Testing
• Test Data Compression
• +۴ مورد دیگر

Χρ. Καβουσιανός is a Professor at the Department of Computer Engineering & Informatics, University of Patras (Polytechnic School). His research focuses on VLSI design, digital circuit testing, and test data compression. He holds a Diploma (1996) and PhD (2000) from the same department, with notable scholarships including the Maritsas Doctoral Scholarship (1999). Key roles include leading the Heraclitus II and Pythagoras II research programs as principal investigator. Collaborations include Duke University (USA) with Prof. Krishnendu Chakrabarty. Research highlights: Pioneered techniques in scan-based testing, test set embedding, and low-power testing. Over 45 publications in top journals/conferences (e.g., IEEE TCAD, DAC). Notable work includes defect-oriented testing methods and Huffman-based compression schemes. Has supervised 9 advisees across doctoral/postgraduate levels. Holds a 2010 US patent application on power switch design.

David Hung-Chang Duمشاهده پروفایل

استاد

• Intelligent Storage Systems
• Sensor/Vehicular Networks
• Cyber Physical Systems
• +۵ مورد دیگر

David Hung-Chang Du is a Professor and Qwest Chair Professor in the Department of Computer Science and Engineering at the University of Minnesota, Twin Cities. He served as the Center Director of the NSF multi-university I/UCRC Center of Research in Intelligent Storage (CRIS) from 2009-2021, which was sponsored by 18 companies with $3.35M industrial membership funding. Prior to that, he organized an Intelligent Storage Consortium at Digital Technology Center with industrial funding from 2002-2009. He was also a Program Director at the National Science Foundation CISE/CNS Division from March 2006 to August 2008. Dr. Du received his B.S. degree in Mathematics from National Tsing-Hua University (Taiwan) in 1974 and M.S. and Ph.D. degrees from University of Washington (Seattle) in 1980 and 1981 respectively. He joined University of Minnesota as a faculty member in 1981 and has been there for over 40 years. He has also been a visiting professor in Germany, Korea, Singapore, Hong Kong and Taiwan. Dr. Du's research focuses on intelligent storage systems, sensor/vehicular networks, and cyber physical systems. His early career work included parallel processing and database design, followed by Computer-Aided Design for VLSI circuits in the 1980s and 1990s. From the late 1980s to present, he has worked on computer networking and its related applications including multimedia computing. Starting from 2000, his research has focused on new memory and storage technologies for handling extremely large volumes of available data and long-term data preservation. His current research focuses on hyper-converging infrastructure, recognizing that the Internet has become the largest existing computer system where data collection, storage, and networking must be integrated. His recent publications (2019-2022) demonstrate continued innovation across multiple domains including DNA storage technologies, hybrid storage systems, key-value store optimization, and edge computing. These works reflect his ability to adapt to emerging technological landscapes while maintaining focus on fundamental storage and systems challenges. The publications show strong industry collaboration, particularly with major technology companies like Facebook, where his team has characterized and optimized key database workloads. IEEE Fellow (since 1998) Fellow of the Minnesota Supercomputer Institute ACM Recognition of Service Award (2013) IEEE Certificate of Appreciation (2012, 2007) NSF Director's Award for Collaborative Integration (2008) Best Paper Award, International Conference on Internet of Vehicles (2015) Best Paper Award, International Conference on Computer Design (1998) Dr. Du has been highly active in professional service, serving as Editor of IEEE Transactions on Computers (1993-1998), member of several editorial boards, and holding leadership positions in numerous conferences including General Chair for IEEE Security and Privacy Symposium (2009), Program Committee Co-Chair for International Conference on Parallel Processing (2009), and General Chair for IEEE International Conference on Distributed Computing Systems (2010-2011). He has graduated 67 Ph.D. students (58 as single adviser and 9 jointly supervised) and 109 Master's students over his 40+ year career. His research has been supported by substantial grants from NSF, industry partners, and other funding sources totaling millions of dollars.

Maciej Ciesielskiمشاهده پروفایل

استاد

• Computer Aided Design (CAD) for VLSI circuits
• Logic synthesis and optimization
• Formal verification of integrated circuits
• +۲ مورد دیگر

Maciej Ciesielski is a Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Amherst and serves as Associate Department Head. His research focuses on Computer Aided Design (CAD) for VLSI circuits , Logic synthesis and optimization , and Formal verification of integrated circuits . Education : PhD in Electrical Engineering (1983, University of Rochester), MS in Electrical Engineering (1974, Warsaw Technical University). He leads research in computing and cybersecurity, with a strong emphasis on computer engineering. His work bridges theoretical and applied advancements in digital system design. Scientific Awards and Affiliations : IEEE Fellow (2020) Residential First-Year Experience Student Choice Award (2013) Doctorate Honoris Causa, Universite de Bretagne Sud (2008) Institute of Electrical and Electronics Engineers (IEEE)

Irina Ilioaeaمشاهده پروفایل

استادیار

• Commutative Algebra
• Combinatorial Commutative Algebra
• Computational Algebra
• +۲ مورد دیگر

Dr. Irina Ilioaea serves as an Assistant Professor in the Department of Mathematics at Louisiana State University Shreveport (LSUS) within the College of Arts and Science, joining the institution in 2020 after completing her doctoral studies. Her academic foundation bridges theoretical mathematics with practical engineering applications. Her educational credentials include: PhD in Mathematics from Georgia State University (2020) MS in Algebra from University of Bucharest BS in Mathematics from University of Bucharest Her research centers on Commutative Algebra , Combinatorial Commutative Algebra , and Computational Algebra , with specialized focus on tight closure theory and Frobenius complexity theory . These investigations create critical linkages between abstract algebraic structures and real-world problems in digital circuit design and computational verification systems. Analysis of her publications reveals consistent interdisciplinary collaboration between mathematics and computer engineering, particularly in developing algebraic solutions for VLSI design challenges and constraint programming frameworks through finite field applications. Her scholarly contributions have been recognized through: Mathematics Achievement Award (2019) from Georgia State University Fred Massey Endowed Award (2018) for research and teaching excellence Dr. Ilioaea currently teaches undergraduate mathematics courses including Math 121L (Problem Solving Lab), Math 150 (Precalculus), Math 201 (Discrete Mathematics), and Math 407/607 (College Geometry) with structured office hours for student engagement. No public information indicates research grants or graduate student advising activities.

• Optical Networks-on-Chip
• Electronic Design Automation
• Wavelength-Routed Optical Networks
• +۳ مورد دیگر

Dr. Alexandre Truppel is a researcher at the Chair of Electronic Design Automation, Technical University of Munich (TUM), working under Prof. Ulf Schlichtmann. His affiliation spans the university's core engineering research units, focusing on advanced methodologies for integrated circuit design and photonic interconnect systems within the Department of Electrical and Computer Engineering framework. Truppel's research centers on Optical Networks-on-Chip (ONoC) and Wavelength-Routed Architectures , with deep specialization in crosstalk modeling, bandwidth optimization, and physical layout co-design. His work addresses critical bottlenecks in on-chip communication through algorithmic innovations like Pareto simulated annealing, significantly advancing signal integrity and power efficiency in photonic interconnects for high-performance computing. Analysis of his 2019-2025 publications reveals a cohesive trajectory from foundational PSION topology optimization (2019) to sophisticated multi-objective frameworks (2025), consistently targeting laser power reduction and crosstalk mitigation. The research demonstrates escalating methodological rigor while maintaining focus on practical EDA implementation for next-generation optical NoCs. As part of TUM's Chair of Electronic Design Automation, Truppel operates within a high-impact research ecosystem specializing in analog/mixed-signal EDA, neural network accelerators, and emerging microfabrication technologies. The group's collaborative environment drives innovation in optical interconnects through funded projects and industry partnerships, positioning Truppel's work at the forefront of photonic integration challenges.

• Integrated Circuit Design
• Analog Circuits
• CAD for VLSI
• +۲ مورد دیگر

Günhan Dündar is a Professor in the Department of Electrical and Electronics Engineering at Bogazici University. His research specializes in analog/mixed-signal integrated circuit design and computer-aided tools for VLSI systems. Key contributions include aging-robust circuit design automation, low-power CMOS architectures, and security primitives like physically unclonable functions (PUFs). The Dündar Lab explores reconfigurable systems for reliability enhancement in nanoscale technologies. Collaborations address hardware vulnerabilities in IoT devices and optimization of ring oscillator networks for cryptographic applications. Teaching emphasizes design automation, semiconductor physics, and hardware implementation methodologies.