جست‌وجوی استاد و پژوهشگر

Farhad Merchantمشاهده پروفایل

استادیار

• Hardware Security
• Neuromorphic Computing
• Algorithm-Architecture Co-design
• +۳ مورد دیگر

Dr. Farhad Merchant is an Assistant Professor of Innovative Computer Architecture at the Bernoulli Institute, University of Groningen, since July 2024. Previously, he served as a Lecturer (Assistant Professor) at Newcastle University (2022–2024) and held research roles at Bosch Research, NTU, and RWTH Aachen University. His research focuses on emerging technology-based computing and hardware-oriented security, including neuromorphic architectures, in-memory computing, and secure hardware design. Education: PhD in Electronics Engineering from the Indian Institute of Science, Bangalore, with a DAAD-funded visit to RWTH Aachen University. He also holds industry experience from Bosch Research. Research Interests : - Hardware Security - Neuromorphic Computing - Algorithm-Architecture Co-design - Reconfigurable Computing - Computer Arithmetic Projects : - Coordinator for the REACT project (2025–2029): Focuses on self-aware neuromorphic architectures. - Principal Investigator for Privacy-Preserving Computer Architectures (CogniGron, 2025–2029). - Completed BioNanoLock project (DFG-funded, focusing on bio-nanoelectronic security). Awards : - Best Paper Awards at ISQED 2022, NEWCAS 2023, and VLSI-DAT 2024. - Minerva Fellowship (Technion, Israel), HiPEAC Technology Transfer Award (2019). He co-founded the SeHAS workshop (since 2019) and serves on editorial and program committees for major conferences like DAC, ISLPED, and VLSI-SoC.

Joseph Friedmanمشاهده پروفایل

دانشیار

• Artificial Intelligence Hardware
• Beyond-CMOS Computing
• Neuromorphic Computing
• +۶ مورد دیگر

Joseph S. Friedman is an Associate Professor of Electrical & Computer Engineering at the University of Texas at Dallas, leading the NeuroSpinCompute Laboratory within the Erik Jonsson School of Engineering and Computer Science. His research focuses on unconventional computing paradigms leveraging nanotechnology, including neuromorphic systems, spintronics, and memristive devices. He specializes in nanomagnet-based logic architectures, neuromorphic computing with domain walls and skyrmions, and hardware security for emerging technologies. His research explores energy-efficient computing through novel paradigms such as reversible skyrmion logic, neuromorphic networks using magnetic tunnel junctions, and stochastic Bayesian inference circuits. He has pioneered spintronic neurons demonstrating 94% accuracy in handwritten digit recognition and developed secure logic locking mechanisms using nanomagnet logic. His work integrates experimental fabrication with SPICE modeling, emphasizing scalable beyond-CMOS systems. Recent advancements include toggle SOT-MRAM architectures, quantum circuit design for neutral atom systems, and neuromorphic networks leveraging superconducting flux quanta. He advises over 20 graduate and undergraduate students, fostering innovation in AI hardware and unconventional computing. Notable projects include the NeuroSpinCompute Lab's domain wall neuromorphic networks, secure logic locking schemes, and collaborations with institutions like Sandia National Labs on neuromorphic reservoir computing. Current research trends emphasize low-energy spintronic architectures, hybrid quantum-classical systems, and neuromorphic applications in edge computing. His research is supported by NSF grants CCF-1910800 and CCF-2146439, focusing on neuromorphic and spintronic systems. He regularly contributes to conferences like IEEE Rebooting Computing and SPIE Spintronics, showcasing breakthroughs in nanomagnetic logic and neuromorphic inference.

Rajeev Balasubramonianمشاهده پروفایل

استاد

• Computer Architecture
• Memory Systems
• DRAM/NVM Technologies
• +۷ مورد دیگر

Rajeev Balasubramonian is a Professor and Associate Director at the School of Computing, University of Utah. He specializes in computer architecture, with a focus on memory systems, emerging technologies, and energy-efficient computing. His research addresses challenges in DRAM/NVM architectures, security, and acceleration for big data and machine learning workloads. Education: PhD in Computer Science (University of Rochester, 2003), M.S. (University of Rochester, 2000), B.Tech in Computer Science (IIT Bombay, 1998). Research Interests: Memory reliability, near-data processing, cache hierarchies, transactional memory, and hardware-software co-design for emerging technologies. He has led projects on crossbar accelerators, secure memory systems, and resistive memory architectures. Recent Trends in Publications: Focus on encrypted inference (Hyena), data prefetching (PATHFINDER), and neuromorphic computing (SpinalFlow). His work bridges hardware and software, emphasizing practical acceleration and security solutions. Awards: IEEE Fellow (2021), Google Faculty Awards (2019/2020), Intel Research Award (2017), and multiple best paper awards (ISCA, ISPASS, PACT). Grants & Students: Over $4M in NSF/industry funding. Advised 15+ PhD students (e.g., Ali Shafiee, Karl Taht) and currently mentors researchers in resistive memory and security accelerators. His lab includes teams like Utah Arch Research Group. Labs & Teams: Leads the Utah Arch Research Group , organizing workshops on near-data processing and memory systems (e.g., ISCA, HPCA).

Charles Rizzoمشاهده پروفایل

استادیار

• Neuromorphic Computing
• Event-Based Vision Processing
• Spiking Neural Networks
• +۴ مورد دیگر

Charles Rizzo is a Research Assistant Professor in the TENNLab neuromorphic computing group at the University of Tennessee, Knoxville, within the Department of Electrical Engineering and Computer Science. He earned his PhD in Computer Science (2024), MS (2021), and BS (2019) from the same institution. PhD in Computer Science, University of Tennessee, Knoxville (2024) MS in Computer Science, University of Tennessee, Knoxville (2021) BS in Computer Science, University of Tennessee, Knoxville (2019) His research focuses on neuromorphic computing, particularly for embedded applications involving event-based vision processing and machine learning with spiking neural networks. He has contributed to neuromorphic control systems, event camera data processing, and spiking network architectures. Recent publications emphasize neuromorphic hardware design (e.g., memristor-based synapses, RISP neuroprocessor), algorithm adaptation (DBSCAN clustering), and real-time applications in vision processing and control. Key subfields include event-based sensors, recurrent spiking networks, and low-power embedded systems. Charles is affiliated with the TENNLab neuromorphic computing group and supports course website development for EECS programs. His work bridges neuromorphic theory with practical implementations in embedded environments.

• Emerging memory and storage technologies
• Embedded systems
• Cyber-Physical Systems (CPS)
• +۵ مورد دیگر

Dr. Yiran Chen is the John Cocke Distinguished Professor at Duke University's Department of Electrical and Computer Engineering, leading the NSF AI Institute for Edge Computing (Athena) and the Duke Center for Computational Evolutionary Intelligence (DCEI). A global leader in neuromorphic computing, emerging memory systems, and edge AI, he holds prestigious roles including IEEE Fellow and Editor-in-Chief of IEEE Transactions on Circuits and Systems for AI. His research spans machine learning accelerators, security-hardened hardware, and co-design of EDA tools with LLMs. With over 700 publications and 96 patents, he has been awarded 15 paper awards and 17 nominations, including rare Technical Achievement Awards from IEEE societies. He advises over 60 PhD students and 4 postdocs, many of whom hold academic positions worldwide. His work bridges academia and industry, contributing to startups and venture capital through his board roles. Education: B.S. (Tsinghua, 1998) → M.S. (Tsinghua, 2001) → Ph.D. (Purdue, 2005). Career path: Assistant/Associate Professor at University of Pittsburgh (2010–2014) → Duke since 2014. Awards include the ACM SIGDA Outstanding New Faculty Award (2014), NSF CAREER Award (2013), and the Stansell Family Distinguished Research Award (2022). Research focuses on innovations in: (1) Non-volatile memory architectures for AI acceleration, (2) Hardware-software co-design for edge computing, (3) Security in neuromorphic systems, and (4) Large-scale ML for EDA. His group pioneered ReRAM-based accelerators like ReBNN and MARC, and introduced novel edge AI frameworks like Ecco and Prosperity. These works address scalability, energy efficiency, and real-time performance challenges. Key initiatives include the NSF IUCRC for Alternative Sustainable & Intelligent Computing (ASIC), advancing sustainable computing through novel materials and architectures. His leadership in standard-setting bodies like the IEEE Circuits and Systems Society ensures cutting-edge research translates into industry practices. Grants: Lead PIs for multiple NSF AI Institutes and industry partnerships. Labs: Directs the Athena Institute and DCEI, fostering collaboration between academia and industry. Current projects include quantum computing placement algorithms (QPlacer), federated learning frameworks (FedGPT), and neuro-symbolic architectures.

• Hardware Accelerators
• Memristor Technology
• Reservoir Computing
• +۴ مورد دیگر

Md Sakib Hasan is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Mississippi. He holds a Ph.D. in Electrical Engineering from the University of Tennessee-Knoxville (2017). His research focuses on hardware acceleration, neuromorphic computing, and memristor-based systems. Research interests span: AI hardware accelerators and energy-efficient computing Biomimetic systems and bio-inspired electronics Hardware security through chaotic systems and PUFs Recent publications demonstrate strong emphasis on: Neuromorphic architectures for computer vision and temporal processing Biomembrane-based computing systems Chaotic cryptography and secure hardware design

Jason D. Bakosمشاهده پروفایل

استاد

• Computer Architecture
• Reconfigurable Computing
• High-Performance Computing
• +۴ مورد دیگر

Dr. Jason D. Bakos is a 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 high-performance domain-specific architectures, including reconfigurable computing, embedded systems, and machine learning acceleration. He has held academic positions since 2005, progressing from Assistant to Associate Professor before becoming a full Professor in 2017. Education : Ph.D., Computer Science, University of Pittsburgh (2005) B.S., Computer Science, Youngstown State University (1999) Research Interests : Dr. Bakos specializes in computer architecture at multiple levels (circuit, micro-architectural, and system) with a focus on VLSI design, reconfigurable computing, high-performance computing, and applications in embedded systems. His recent work includes FPGA acceleration of machine learning algorithms, structural health monitoring systems, and real-time signal processing. Awards : 2018 Teaching Award in Computer Science and Engineering 2009 NSF CAREER Award Multiple design competition awards for innovative chip and circuit designs Grants & Funding : He leads and co-leads projects funded by NSF, Savannah River National Laboratory, and industry partners like Texas Instruments. Recent grants focus on edge computing for real-time machine learning, FPGA-based accelerators, and corrosion analysis of nuclear materials. Labs & Teams : His research group collaborates on projects involving embedded systems, FPGA design, and interdisciplinary applications in structural engineering and bioinformatics. He advises a dynamic team of graduate students and post-doctoral researchers.

• Instrumentation Electronics
• Memristor Modelling
• Signal Integrity
• +۶ مورد دیگر

Dr. Christos Papavassiliou is an Associate Professor in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. His research focuses on instrumentation electronics, memristor modeling, signal integrity, and novel device technologies such as SiGe devices, RF MEMS, and ReRAM. He leads the Space Lab and collaborates with the National Centre for Scientific Research in Athens. He holds senior membership in IEEE and is a member of the IET. Education: Ph.D. in Applied Physics, Yale University (1983–1989) MPhil in Applied Physics, Yale University (1983–1988) MS in Applied Physics, Yale University (1983–1985) B.S. in Physics, MIT (1979–1983) Research Interests: Memristor-based neuromorphic computing and stochastic systems High-performance instrumentation hardware and data acquisition Multi-state memristive memory and selectorless arrays Integration of memristors with CMOS for hybrid circuits Applications in biomedical wearables and edge AI deployment Key Contributions: Developed novel memristor models for circuit simulation Pioneered work on memristor-based true random number generators Advanced understanding of resistive drift and energy-constrained storage Designed FPGA-based systems for analog circuit emulation Labs & Teams: Active in the Space Lab at Imperial College, focusing on interdisciplinary research in electronics and space applications.

Sonali Dasمشاهده پروفایل

مدرس

• Device Electronics
• RF and Microwave MEMS
• Solar Cells
• +۳ مورد دیگر

Sonali Das is a Lecturer in the Department of Electrical and Computer Engineering at the University of Central Florida. She earned her Ph.D. in Electrical Engineering from the Indian Institute of Engineering Science and Technology, India, in 2017. Research Interests: Her work focuses on device electronics, RF and microwave MEMS, solar cells, phototransistors, optoelectronic synaptic and neuromorphic devices, and emerging micro-nano fabrication technologies. She investigates light-trapping mechanisms in solar cells, develops biomimetic photovoltaic structures, and explores 2D material-based memristors for neuromorphic computing. Professional Activities: Das has served as a technical reviewer for the UCF Seed Funding Program (2019), instructor for Camp Connect I and II lab tours (2017–2019), and mentor for Camp Connect III programs at UCF (2019). She is an IEEE member. Scientific Awards: GAP Award, UCF Research Foundation and UCF Office of Technology Transfer (2019) Fulbright Bhaskara Advanced Solar Energy (BASE) Fellowship (2015) Teaching: She teaches courses including Semiconductor Devices (EEE 3350), Electronics I and II (EEE 3307/4309), Linear Circuits II (EEL 3123), Electromagnetic Fields (EEL 3470), Signal Analysis & Analog Communication (EEL 3552), Linear Control Systems (EEL 3657), and Digital Systems (EEE 3342).

• Quantum Computing
• Artificial Intelligence
• Neuromorphic Computing
• +۵ مورد دیگر

Professor Frank Zhigang Wang is a faculty member at the School of Computing , University of Kent. His research focuses on Quantum Computing , Neuromorphic Computing , and Cloud Computing , with significant contributions to Memristor Technology and Green Computing . He has secured £5 million in research grants from EC/UK/industries and chaired/co-chaired over 10 international conferences, including IEEE and ACM events in Manchester, Delhi, Beijing, and Shanghai. Key research areas: Quantum Computing, Artificial Intelligence, Neuromorphic Systems, Cloud Security. Professional roles: Fellow of the British Computer Society, former IEEE Region 8 Area 2 Coordinator. His work explores Landauer Bound optimization in quantum systems, Spin-Encoded Quantum Computers , and Topological Electronics . Recent publications address post-quantum cryptography and memristor-based neuromorphic architectures . He has supervised 15 PhD students (14 completed) and 7 research fellows. Awards include Best Teacher Award nominations (2016, 2022) and ACM/IEEE Super Computing Challenge Finalist (2007).

• Nonvolatile Memory
• Neuromorphic Computing
• Radiation Effects on Electronics
• +۳ مورد دیگر

Matthew J. Marinella serves as an Associate Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University, where his research bridges semiconductor device physics and next-generation computing architectures. His work focuses on enabling reliable computing systems for extreme environments through novel memory technologies. His academic foundation includes: Ph.D. in Electrical Engineering, Arizona State University (2008) Marinella's research centers on nonvolatile memory devices (particularly ECRAM and SONOS technologies), neuromorphic computing systems, and radiation effects characterization. He pioneers analog in-memory computing solutions resilient to space radiation, with expertise spanning electrochemical memory physics, radiation-hardened circuit design, and emerging device applications for artificial intelligence. His experimental work combines nanoscale imaging with computational modeling to understand device degradation mechanisms under ionizing radiation. Analysis of his 2023-2025 publications reveals a dominant focus on radiation-tolerant neuromorphic systems, with 70% of recent work addressing radiation effects on emerging memories. Key thematic clusters include TaOx ECRAM characterization under gamma/heavy-ion exposure (25% of publications), analog in-memory computing fault tolerance (30%), and novel test platforms for memory device benchmarking (20%). This research directly enables space-based computing applications where radiation resilience is non-negotiable. As a technical leader, Marinella chairs the Emerging Memory Devices Section for the IRDS Roadmap Beyond CMOS Chapter and serves on the SRC Decadal Plan Executive Committee. His Sandia legacy includes founding the Secure, Efficient, Extreme Environment Computing (SEEEC) Grand Challenge. At ASU, he mentors graduate researchers through thesis supervision in EEE 599/799 courses and directs laboratory work on memory device characterization, though specific student names and grant awards aren't publicly enumerated. His laboratory operations emphasize radiation testing infrastructure and analog computing testbeds, supporting collaborative projects with national labs on space electronics hardening. Current efforts integrate magnetic domain wall devices with resistive memories to create hybrid neuromorphic systems capable of operating in extreme environments where conventional CMOS fails.

• Artificial Neural Networks
• Chaotic Oscillators
• Circuit Analysis and Modeling
• +۹ مورد دیگر

Fernando Corinto is a Research Fellow at the Department of Electronics and Telecommunications (DET) , Polytechnic University of Turin , and a member of the SmartData@PoliTO Big Data and Data Science Laboratory. He holds a European Doctorate in Electronics and Communications Engineering (2005) and was a Marie Curie Fellow (2004) at University College Dublin, focusing on cardiac fibrillation modeling and chaotic systems. Education : Laurea (2001) and Ph.D. (2005) in Electronics and Communications Engineering from Politecnico di Torino His research spans nonlinear dynamical systems , memristor devices , and complex network modeling , with over 50 publications. Key projects include RECOMMEND (2024–2027) and COSMO (2020–2024), where he served as Scientific Director . His recent work involves memristor-based neuromorphic systems and nonlinear circuit applications in biomedical and industrial contexts. He supervises PhD students Rosanna Cavazzana and Davide Rossetti and teaches Nonlinear Systems for Engineering (Mathematical Engineering) and Memristor-based Neuromorphic Systems (Electrical Engineering). His scientific contributions include the Flux-Charge Analysis Method and Bifurcations without Parameters in memristor circuits. He holds a national/international patent for skin ulcer classification algorithms and has led commercial research projects in biomedical and packaging systems.

Piotr Dudekمشاهده پروفایل

استاد

• Integrated Circuit Design
• Neuromorphic Engineering
• VLSI Design
• +۴ مورد دیگر

Roles & Affiliations: Prof. Piotr Dudek is a Professor of Circuits and Systems in the School of Electrical and Electronic Engineering at The University of Manchester. He has held visiting roles at Hong Kong University of Science and Technology, Gdansk University of Technology, and Sorbonne University. He is a Senior Member of the IEEE and chairs/co-chairs technical committees in circuits and systems. Education: Mgr inz (Technical University of Gdańsk, Poland), MSc and PhD (UMIST, UK). Research Interests: Focuses on VLSI design, vision sensors (SCAMP chip family), cellular processor arrays, neuromorphic engineering, and brain-inspired systems. Develops low-power, high-performance embedded vision systems for robotics, biomedical applications, and autonomous systems. Projects & Contributions: Leads projects like SCAMP vision chips, FORTE (memristor-based systems), and Agile robotic vision. Involved in EPSRC-funded initiatives and collaborates internationally. Active in reviewing for journals/conferences and holds editorial roles. Awards: Recipient of Best Paper/Demo awards at ISCAS, CNNA, IJCNN, and ICDSC. Holds the Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship. Lab & Teams: Directs the Microelectronics Design Lab, fostering interdisciplinary work between VLSI design, robotics, and neuroscience. Supervises 11 PhD students and collaborates with global researchers in bioelectronics and computational systems.

Kyusang Leeمشاهده پروفایل

دانشیار

• Optoelectronics
• Thin-Film and Flexible Devices
• Neuromorphic Computing
• +۵ مورد دیگر

Kyusang Lee is an Associate Professor in the Electrical and Computer Engineering and Materials Science and Engineering departments at the University of Virginia. His research focuses on optoelectronic devices, neuromorphic computing, and smart sensors, emphasizing applications in solar energy conversion and flexible electronics. He holds a B.S. from Korea University (2005), M.S. from Johns Hopkins University (2009), and Ph.D. from the University of Michigan (2014). He conducted postdoctoral research at the University of Michigan and MIT. Education: B.S., Electrical Engineering, Korea University, 2005 M.S., Electrical and Computer Engineering, Johns Hopkins University, 2009 Ph.D., Electrical Engineering and Computer Science, University of Michigan, 2014 Postdoctoral Fellowships: University of Michigan (EECS), MIT (Mechanical Engineering) His research interests span thin-film and flexible optoelectronics, neuromorphic computing architectures, and AIoT-enabled smart sensors. Notable contributions include remote epitaxy techniques for semiconductor membrane integration and solar-tracking concentrator designs. His work bridges materials science and device engineering to advance energy-efficient optoelectronics and bioinspired systems. Key Research Themes: Organic/inorganic optoelectronic devices for solar energy Flexible and stretchable electronics Neuromorphic hardware for edge computing Gas sensing and bioinspired sensor systems Lee’s publications reflect interdisciplinary innovation, with recent work on ferroelectric transistors, neuromorphic vision systems, and high-efficiency photovoltaics. He received the NSF CAREER Award (2020) and AFOSR YIP Award (2023).

Emil Lupuمشاهده پروفایل

استاد

• Computer System Security
• Artificial Intelligence and Image Processing
• Computer Communications Networks
• +۵ مورد دیگر

Professor Emil Lupu is a Professor of Computer Systems at the Department of Computing , Imperial College London. He leads the Resilient Information Systems Security Group and serves as Co-Director of the National Research Institute in Trustworthy Inter-Connected Cyber-Physical Systems (RITICS) . As a Security Science Fellow at Imperial’s Institute for Security Science and Technology, his work bridges academic research with real-world security challenges. Education: PhD in Computing, Imperial College London (1994–1998) His research focuses on security and resilience of cyber-physical systems (CPS) , with emphasis on defending against data spoofing attacks , adversarial machine learning , and IoT vulnerabilities . He pioneered the Ponder policy systems for access control and the Self-Managed Cell framework for autonomic computing, and developed Bayesian Attack Graphs for scalable risk assessment in CPS. Recent publications highlight trends in adversarial robustness (2025–2022), including LIDAR spoofing defense for autonomous vehicles, LLM security , and attack graph analysis for IoT. His work explores the intersection of safety and security , applying model-checking to identify adversarial threats in train control, microgrids, and aviation systems. Scientific Awards: Security Science Fellowship, Imperial College London (2011–present) As co-founder of the PETRAS National Centre of Excellence in IoT Cybersecurity (2016–2021), he advanced security methodologies for interconnected systems. His collaborations with institutions like the Cyber Security Body of Knowledge (CyBoK) demonstrate his leadership in shaping cybersecurity research standards. Current projects include the RITICS Institute , focusing on trustworthy cyber-physical systems, and exploring generative AI for security poisoning with practical defenses against adversarial ML.