جست‌وجوی استاد و پژوهشگر

Caiwen Dingمشاهده پروفایل

استادیار

• Algorithm-system co-design of machine learning/artificial intelligence
• Computer architecture and heterogeneous computing
• Privacy-preserving machine learning
• +۴ مورد دیگر

Caiwen Ding is an Assistant Professor at the School of Computing , University of Connecticut. His research focuses on algorithm-system co-design of machine learning/artificial intelligence , computer architecture , privacy-preserving machine learning , and neuromorphic computing . He actively explores computer vision , natural language processing , and machine learning applications in electronic design automation (EDA) . Education: Ph.D., Northeastern University (Boston), 2019 Research Trends: His work bridges machine learning with hardware optimization (FPGAs/GPUs) and addresses cross-disciplinary challenges in secure computation and AI-driven EDA tools. Collaborative efforts span computer architecture , heterogeneous computing , and cognitive modeling for neural systems. Scientific Awards & Honors NSF CAREER Award Amazon Research Award CISCO Research Award Best Paper Award at AAAI DCAA 2023 Outstanding student paper award at HPEC 2023 Best Paper Award Nomination at DATE 2018 and DATE 2021 Publicity paper at DAC 2022 Stanford’s World’s Top 2% Scientists (2023) UConn Research Excellence Awards (2021, 2023) UConn Provost’s Excellent in Teaching Award (2021) Contact: caiwen.ding@uconn.edu | Phone: (860) 486-0006

Xuan Zhangمشاهده پروفایل

دانشیار

• Artificial Intelligence Hardware
• Machine Vision Sensors
• Electronic Design Automation
• +۳ مورد دیگر

Xuan Zhang serves as Associate Professor in Electrical and Computer Engineering at Northeastern University, leading the Sensory AI Lab since joining in January 2024. Her research bridges computer architecture, integrated circuits, and artificial intelligence to develop miniaturized AI systems for autonomous physical platforms. She earned her PhD in Electrical and Computer Engineering from Cornell University in 2012. Her educational background forms the foundation for her interdisciplinary work spanning hardware and software co-design. Dr. Zhang's research focuses on artificial intelligence hardware, machine vision sensors, and security for autonomous systems. She pioneers techniques for efficient in-sensor computing, analog circuit optimization via machine learning, and hardware-level privacy preservation. Her work addresses critical challenges in energy efficiency, robustness, and security for edge AI deployment, particularly in resource-constrained environments like medical devices and autonomous vehicles. Analysis of her 2023-2025 publications reveals three dominant trends: (1) hardware-accelerated privacy mechanisms for sensors, (2) machine learning-driven analog circuit design automation, and (3) energy-efficient architectures for neural network inference. These works consistently target real-world applications in healthcare, autonomous systems, and semiconductor design. Her accolades include the prestigious NSF CAREER Award (2020) and leadership in a $10 million federal semiconductor initiative. She contributes to national efforts in AI-powered chip design through the National Center for the Advancement of Semiconductor Technology. Dr. Zhang advises graduate researchers in the Sensory AI Lab, securing significant funding for projects spanning hardware security, in-sensor computing, and autonomous system assurance. Her lab collaborates with federal agencies and industry partners on cutting-edge semiconductor research. The Sensory AI Lab operates at the hardware-software interface, developing novel architectures for intelligent edge devices. Current projects include optical privacy preservation, robust analog design tools, and energy modeling frameworks for in-sensor visual computing systems.

Yu [Kevin] Caoمشاهده پروفایل

استاد

• Microelectronics
• Computer Engineering
• Semiconductor Technology
• +۵ مورد دیگر

Yu [Kevin] Cao is the Louis John Schnell Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on microelectronics co-design for energy-efficient computing, spanning integrated circuit design, semiconductor physics, and machine learning methodologies. He leads the Microelectronics Co-design Research Group and actively collaborates with institutions like Georgia Institute of Technology, Sandia National Laboratories, and Notre Dame. His research interests include AI hardware acceleration , in-memory computing , cryogenic CMOS design , and 3D integration of heterogeneous chiplets . Current initiatives explore reconfigurable on-package systems for AI, spiking neural networks on neuromorphic hardware, and low-temperature logic technologies. Recent publications and projects highlight advancements in AI accelerators , RRAM-based compute-in-memory , graph convolutional networks , and 3D integration . His group develops tools like MN-SIM 2.0 for memristor modeling and investigates novel materials for neuromorphic systems. Grants include collaborative NSF funding for chiplet-based AI systems, CoCoSys center funding from SRC, and DOE/Sandia projects on neuromorphic hardware. Future work emphasizes scalable co-design frameworks for intelligent systems and heterogeneous integration challenges.

• Embedded Systems
• Hardware/Software Co-design
• Energy-efficient AI Accelerators
• +۷ مورد دیگر

Oliver Bringmann is a full Professor and head of the Chair of Embedded Systems at the University of Tübingen, Germany, and a member of the board of directors at the FZI Research Center for Information Technology. His research integrates embedded-system design, energy-efficient AI accelerators, dependable automotive perception, and medical AI for capsule endoscopy. Education & Career Ph.D. in Computer Science, University of Tübingen, 2001 Diploma in Computer Science, University of Karlsruhe (KIT) Head, Chair of Embedded Systems, University of Tübingen (since 2012) Deputy spokesperson & spokesperson, Dept. of Computer Science, University of Tübingen (2014-2022) Board of Directors, FZI Research Center for Information Technology Research Interests Bringmann’s group pioneers hardware/software co-design for ultra-low-power Edge-AI , developing RISC-V based accelerators, compiler-aware neural-architecture search, and real-time perception systems for autonomous driving and medical devices. Key topics include: Energy-efficient AI architectures (“Edge AI”) and custom accelerator generation Robust collective perception under adverse weather (LiDAR, camera, V2X fusion) Timing/power-predictable embedded software and system-on-chip design automation Hardware-assisted security and safety for automotive & IoT systems AI-driven capsule endoscopy localization and anomaly detection Recent Publication Trends His 2024-2025 articles reveal a strong shift toward robust multimodal perception for automated driving (snow, fog, collective LiDAR fusion) and Edge-AI medical devices (capsule endoscopy with multi-task CNNs). Core contributions span dataset generation (SCOPE, SnowyLane), safety metrics (LSM), and fast performance modeling for DNN accelerators. Professional Service & Projects Executive/Steering Committees: IEEE/ACM DATE, CODES+ISSS, CASES, ITSS conferences EU CATRENE EDA roadmap chapter lead (Embedded Software & ESL-to-RTL) Principal investigator in Scale4Edge, OCEAN12, enerDAG and other national projects on energy-efficient sensorics and secure energy trading. His group maintains extensive collaborations with automotive and semiconductor industry, focusing on dependable, energy-aware embedded intelligence.

• Electronic Design Automation (EDA)
• Computer Architecture
• Embedded Systems
• +۵ مورد دیگر

Prof. Wolfgang Ecker is a Professor at the Technical University of Munich (TUM), affiliated with the Chair of Design Automation within the TUM School of Computation, Information and Technology . His research focuses on Electronic Design Automation (EDA), RISC-V processor architectures, and hardware-software co-design. He leads projects advancing EDA tools for embedded systems, neural network acceleration, and formal verification methodologies. Ecker's work bridges machine learning techniques with traditional EDA challenges, addressing topics like energy-efficient AI inference and automated documentation generation. His contributions span compiler optimization, FPGA implementations, and fault analysis in digital systems. Recent research highlights include contributions to the TRISTAN project for RISC-V ecosystem development, model-driven architecture frameworks, and AI-driven timing analysis. He actively collaborates on open-source EDA tools and explores Rust-based embedded systems development. Ecker’s lab emphasizes practical applications in edge computing and automotive microcontroller safety, with a strong emphasis on interdisciplinary collaboration across TUM’s CIT School. His publications (15 most recent listed) reflect a focus on EDA tool innovation, processor design, and leveraging machine learning for hardware optimization. While no specific awards are mentioned, his involvement in ERC-funded projects and leadership in international collaborations underscores his academic impact.

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.

• Electronic Design Automation
• Energy Efficient Electronic Systems
• Low Power Design
• +۱۲ مورد دیگر

Andrea Calimera is a Full Professor in the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where he is also a member of the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory. He is actively involved in teaching and research, contributing to doctoral programs and undergraduate and graduate courses in computer engineering and data science. Full Professor (L.240), Polytechnic University of Turin Department of Control and Computer Science (DAUIN) Member, SmartData@PoliTO - Big Data and Data Science Laboratory Member, College of Computer, Film and Mechatronics Engineering His research interests center on electronic design automation, energy-efficient electronic systems, and low-power design, with strong connections to artificial intelligence, embedded systems, and IoT. His work bridges hardware and software optimization for intelligent edge devices. The recent publications (2023–2025) reflect a focused trend on federated learning, secure and efficient AI deployment on edge devices, and low-power embedded systems. Topics include robust evaluation in federated learning, resource management under label skew, homomorphic encryption for private tensor operations, pipeline optimization for keyword spotting, and side-channel attacks via DVFS for neural network fingerprinting—highlighting expertise in both performance and security of AI systems on constrained hardware. Andrea Calimera supervises PhD and master's students and leads research projects funded by competitive and commercial grants. He has contributed to national and international patents on low-power depth estimation and single-image signal processing. Scientific Director, SENSEI Project (2017–2019): Energy-efficient machine learning on chip for IoT Scientific Director, Commercial Project (2020–2022): Design tools for AI on energy-efficient embedded mobile devices Supervision of PhD student Erich Malan (ongoing, since 2022) on distributed and federated learning over IoT networks Supervision of Bachelor's student Chen Xie (2020–2024) on synthesis of smart sensors He teaches courses such as High-Level Synthesis (PhD), Synthesis and Optimization of Digital Systems, Machine Learning for IoT, and Efficient Computing for Artificial Intelligence across Computer Engineering and Data Science programs. His research group is EDA - Electronic Design Automation (DAUIN), which focuses on hardware-software co-design for intelligent systems.

• Electronic Design Automation
• Neural Networks
• Accelerators
• +۳ مورد دیگر

Wenhao Sun is a researcher at the Chair of Design Automation at the Technical University of Munich (TUM). His work focuses on advancing neural network design and electronic design automation (EDA), particularly in areas like accuracy enhancement and class-based quantization for AI models. Research: Neural Networks, Accelerators, Analog EDA, Timing Analysis Contact: wenhao.sun@tum.de Recent publications highlight his contributions to optimizing neural networks for hardware efficiency, with two papers presented at the 2023 Design, Automation and Test in Europe (DATE) conference. His work bridges the gap between machine learning and EDA, focusing on incremental and quantization-based improvements.

• Artificial Intelligence
• Machine Learning
• Internet of Things
• +۱۳ مورد دیگر

Edoardo Patti is an Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He is actively involved in research and teaching, focusing on artificial intelligence, machine learning, IoT, smart grids, energy communities, co-simulation, and digital twin technologies. He is a member of the EDA (Electronic Design Automation) research group and the Interdepartmental Center IAM@PoliTo for Integrated Additive Manufacturing. His research interests span Artificial Intelligence, Machine Learning, Internet of Things, Smart Grids, Energy Communities, Co-simulation, Distributed Software Infrastructure, Digital Twin, Physics-Informed Neural Networks, and Reinforcement Learning . His work is closely aligned with Sustainable Development Goals such as Affordable and Clean Energy (Goal 7), Sustainable Cities (Goal 11), and Climate Action (Goal 13). The latest publications reveal a strong trend in AI-driven modeling for energy systems, including digital twins for wave energy converters, urban electric mobility simulation, real-time battery health estimation, and co-simulation platforms for integrated energy systems. His work combines machine learning with physical models and large-scale distributed systems to address sustainability challenges. He supervises several PhD students, including Maria Adelaide Loffa, Matia Torlini, Rafael Natalio Fontana Crespo, Pietro Rando Mazzarino, Claudia De Vizia, and Marco Massano. He has led numerous research projects funded by EU (H2020), PNRR, regional initiatives, and corporate contracts, particularly in additive manufacturing, smart grids, and IoT platforms. He is involved in multiple research groups and centers: EDA - Electronic Design Automation (DAUIN) Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing

Alberto L. Sangiovanni-Vincentelliمشاهده پروفایل

استاد

• Cyber-Physical Systems
• Design Automation
• Embedded Systems
• +۷ مورد دیگر

Alberto L. Sangiovanni-Vincentelli holds the Edgar L. and Harold H. Buttner Chair of Electrical Engineering and Computer Sciences at the University of California, Berkeley. He is a pioneer in Electronic Design Automation (EDA) and co-founder of Cadence and Synopsys. His research focuses on Cyber-Physical Systems (CPS), embedded systems, hybrid systems, and formal methods for AI. He has authored over 800 papers and 17 books, with major contributions to design automation and methodologies. Education: Dr. Ing., EECS, Politecnico di Milano (1971) Research interests include design methodologies, CPS, and AI integration. He has received numerous awards, including the IEEE James Clerk Maxwell Medal (2008) and ACM/IEEE A. Richard Newton Technical Impact Award (2009). He serves on multiple corporate boards and advisory councils, including the Strategic Committee of the Italian Strategic Fund and the Executive Committee of the Italian Institute of Technology. His work spans academia, industry, and policy, with a focus on innovation ecosystems and CPS design automation. Awards: Over 20 major honors, including Fellowships from IEEE and ACM.

• Electronic Design Automation (EDA)
• Analog/RF Circuit Simulation
• Multi-physics Analysis
• +۲ مورد دیگر

Dr. QUAN Chen is an Associate Professor at the School of Microelectronics, Southern University of Science and Technology (SUSTech), holding this position since May 2025 after serving as Assistant Professor (2019-2025) and Research Assistant Professor at the University of Hong Kong (2012-2018). A Shenzhen high-level overseas talent, he earned his PhD from the University of Hong Kong and conducts cutting-edge research in electronic design automation. His academic credentials include: Ph.D. from The University of Hong Kong (2010) Master's degree from The University of Hong Kong (2007) Bachelor's degree from Sun Yat-Sen University (2005) Dr. Chen's research pioneers advanced EDA algorithms for large-scale analog/RF circuit simulation, post-Moore multi-physics analysis, and AI-assisted design technologies. His work addresses critical challenges in nanodevice modeling and quantum computing circuits, resulting in over 50 publications in top venues like IEEE TCAD and DAC, plus four Chinese patents. Analysis of his recent publications reveals dominant trends in exponential integrator methods for transient simulation, model order reduction techniques, and physics-informed machine learning for reliability analysis. His work bridges numerical mathematics with practical EDA applications across analog circuits, quantum hardware, and emerging memory technologies. Key recognitions include: Wu Wenjun Artificial Intelligence Science and Technology Award, Second Prize (2020) ICCAD Best Paper Award Nomination (2012) Dr. Chen actively recruits Postdoctoral Fellows, Research Assistants, and Graduate Students while leading major funded projects including NSFC key/general programs and Guangdong Provincial R&D initiatives. His industry partnerships with Huawei, Empyrean, and Guowei Group translate theoretical advances into real-world EDA solutions. He directs a specialized research group at SUSTech focused on computational methods for next-generation circuit design, fostering innovation in simulation algorithms and multi-physics analysis through academic-industry collaboration.

• 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.

• AI at the Edge
• Low Power Embedded Systems
• Reliable and Secure Computing
• +۸ مورد دیگر

Emanuel Popovici is a Senior Lecturer in Electrical and Electronic Engineering at University College Cork (UCC), Ireland. He holds a Dipl. Ing. in Computer Engineering from the University Politehnica Timisoara, Romania, and a PhD in Microelectronics from UCC. His research focuses on AI at the edge, low-power embedded systems, and secure computing, with applications in healthcare, energy, and IoT. Notable projects include award-winning work in neonatal EEG monitoring, smart beehive systems, and energy-efficient wireless nodes. He has authored over 250 papers and received prestigious awards like the Qualcomm Faculty Award (2024) and three IEEE/IBM Smarter Planet Challenge titles. Education: Dipl. Ing. in Computer Engineering, University Politehnica Timisoara (Hardware Design focus) PhD in Microelectronics, UCC (National Microelectronics Research Centre) Research interests span AI-driven hardware, secure communications, and interdisciplinary collaborations across engineering, medicine, and environmental science. His work emphasizes practical solutions for real-world challenges, such as energy-efficient sensor networks and medical diagnostic tools. Scientific achievements include over 50 awards, including the Reed and Mallick Medal (2020) for urban planning contributions. His lab pioneered innovations like the neonatal EEG sonification system and blockchain-based IoT security frameworks. Collaborative projects span disciplines like anatomy, medicine, physics, and business. His group's work on ultra-low-power wireless nodes and FPGA-based cryptographic processors highlights their focus on energy efficiency and reliability.

• Power Electronics
• RF Circuit Design
• Automated Design Methodologies
• +۷ مورد دیگر

Prof. Fábio Moreira de Passos is an Assistant Professor at Instituto Superior Técnico, University of Lisbon, and an Integrated Researcher at INESC-ID. He holds a Ph.D. from Universidad de Sevilla (2018) and has extensive industry experience with Analog Devices, Renesas, and SiliconGate. His research focuses on automated design methodologies for RF and mm-Wave circuits, energy harvesting for neural implants, and high-dynamic-range amplifiers for UAV systems. He has published over 50 papers, including 6 Q1 journal articles, and won prestigious awards like the EDAA Outstanding Dissertation Award (2019). Academic Affiliations: Instituto Superior Técnico (IST), INESC-ID Research Units: Nano-electronic Circuits and Systems (INESC-ID) Professional Memberships: IEEE Senior Member, IEEE Microwave Theory and Technology Society His research emphasizes industry-oriented applications such as automotive radar transceivers and battery-less neural implants. He has completed 14 chip tape-outs in technologies down to 16nm FinFET, with notable designs including a 77GHz mm-Wave receiver for ADAS systems. Current projects include generative AI for passive device design and automated synthesis frameworks for RF systems. Awards include Marie Curie Fellowship (2021-2023), IEEE Best Paper Awards (2018, 2022), and the 2016 EDA Competition. He co-leads the LAY(RF)^2 project and actively collaborates with global institutions like IMSE-CNM and University of Macao.

• Electronic Design Automation
• High-Level Synthesis
• Machine Learning Applications
• +۳ مورد دیگر

Kangwei Xu is a researcher at the Chair of Design Automation (Lehrstuhl für Entwurfsautomatisierung) under Prof. Ulf Schlichtmann at the Technical University of Munich (TUM), actively advancing Electronic Design Automation through AI-driven methodologies. His core research interests include: Electronic Design Automation (EDA) High-Level Synthesis Machine Learning for EDA Neural Network Accelerators Timing Analysis Hardware Reliability Analysis of his 2024-2025 publications reveals a decisive trend in leveraging Large Language Models to revolutionize hardware design flows. Key innovations span automated C/C++ code refactoring (HLSRewriter), behavioral discrepancy testing (HLSTester), and neural network logic optimization, demonstrating how AI integration significantly enhances efficiency and accuracy in EDA toolchains while addressing longstanding challenges in synthesis and verification. Scientific Awards: No awards documented in available sources. Advising and Grants: Public records indicate no formal student advisement roles or individually attributed research grants; his work operates within the broader funded projects of TUM's Design Automation chair. Labs and Teams: Xu contributes to TUM's interdisciplinary Design Automation research group, which spans Analog EDA, Electronic System Level design, Emerging Technologies, Microfluidics, Optical NoC, Novel Microfabrication, Timing Analysis, Neural Networks and Accelerators, and Reliability, maintaining strong industry collaborations and cutting-edge experimental facilities.