جستجوی استاد راهنمای دانشگاهی

• Semiconductor thin film materials and devices
• Material nano-characterization
• Electromechanical coupling characterization
• +۳ مورد دیگر

Huang Boyuan serves as an Associate Professor and Doctoral Supervisor in the Department of Materials Science and Engineering at Southern University of Science and Technology (SUSTech). His research group operates within the Guangdong Provincial Key Laboratory of Information Functional Oxide Materials and Devices, utilizing advanced facilities including AFM, PLD, ALD, CVD, SHG, semiconductor analyzers, and cleanroom lithography equipment. Education: Ph.D. in Mechanical Engineering, University of Washington (2016-2020) B.S. in Physics, Nanjing University (2012-2016) Huang's research focuses on developing cutting-edge characterization techniques, particularly artificial intelligence atomic force microscopy (AI-AFM) and mechanically gated transistors (MGT). His work bridges materials science, nanotechnology, and artificial intelligence to investigate nanoscale multi-field coupling mechanisms in semiconductor materials. The research group actively explores two-dimensional thin film materials, experimental mechanics, and energy material interfaces, with particular emphasis on data-driven approaches to material characterization. Huang's publication portfolio demonstrates consistent high-impact output across materials science and nanotechnology domains, with increasing emphasis on AI-integrated characterization methods. His recent work shows strong interdisciplinary connections between materials physics, machine learning, and semiconductor device engineering, reflecting the growing importance of data science in advanced material characterization. Scientific Recognition: R&D 100 Awards Finalist (2020) Shenzhen Natural Science Award (2021) Shenzhen Overseas High-level Talent (2020) National Excellent Self-financed Overseas Student Scholarship (2020) Featured in MIT Technology Review Huang has successfully secured funding for eight research projects, including the NSFC Original Exploration Plan project and Major Research Plan Integration Project. His research group actively mentors graduate students and postdoctoral fellows, with several students receiving prestigious awards and scholarships. The team maintains strong industry connections, with two authorized patents achieving technology transfer. The research group operates within state-of-the-art facilities at SUSTech, maintaining collaborative relationships with national research initiatives including the National Major Scientific Research Instrument Development Projects. Current recruitment efforts focus on doctoral candidates and research assistants specializing in two-dimensional thin film materials, experimental mechanics, and energy material interface studies.

Ethan Ahnمشاهده پروفایل

دانشیار

• Nanoelectronics
• Energy-efficient Devices
• Carbon-based Nanomaterials
• +۶ مورد دیگر

Ethan Ahn is an Associate Professor at George Mason University's Department of Electrical and Computer Engineering, part of the Volgenau School of Engineering. Previously, he held academic positions at the University of Texas at San Antonio (2016–2023) and served as a senior panel process engineer at Apple and a postdoctoral researcher at Stanford University. He leads the Mason Nanoelectronics Lab, focusing on nanoscale materials and devices for energy-efficient electronics, spintronics, and beyond-CMOS technologies. Educational Background: PhD in Electrical Engineering, Stanford University (2015) MS and BS in Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST, 2005–2007) Research Interests: His work spans emerging nanomaterials (e.g., graphene, MoS2), energy-efficient logic/memory devices, and carrier transport mechanisms. Key areas include spintronics, phase-change memory, and energy harvesting systems. His lab collaborates with industry and government through VMEC and VAST alliances. Grants & Awards: Recipient of over $4M in funding, including the AFOSR Grant in Quantum Electronic Solids and the NSF EAGER Grant in Electronics/Photonics. He serves on IEEE technical committees and the editorial board of Scientific Reports. Labs & Teams: The Mason Nanoelectronics Lab operates a state-of-the-art nanofabrication facility, advancing research in nanoelectronics and workforce development. The lab explores biomedical devices, energy solutions, and smart infrastructure integration.

Kaveh Shamshiمشاهده پروفایل

استادیار

• Hardware Security
• Circuit Obfuscation
• Side-Channel Resilient Design
• +۳ مورد دیگر

Dr. Kaveh Shamshi is an Assistant Professor in the Electrical and Computer Engineering Department at the University of Texas at Dallas (UTD), affiliated with the Erik Jonsson School of Engineering and Computer Science. He joined UTD in 2020 after completing his Ph.D. at the University of Florida under Dr. Yier Jin. His research focuses on formal methods, hardware security (e.g., circuit obfuscation, side-channel resilience), and CAD tools for trustworthy hardware. He has received best paper awards at HOST’17 and GLSVLSI’18, and was a best paper candidate at DATE’19. Education: B.S., Sharif University of Technology (2014); M.S., University of Central Florida (2017); Ph.D., University of Florida (2020) Key Research Areas: IP protection via circuit obfuscation, side-channel-resistant designs, and beyond-CMOS circuit security His work integrates formal verification and machine learning to combat hardware threats like reverse engineering and supply chain attacks. Recent projects include SAT-based deobfuscation techniques and machine-learning attacks on logic locking schemes. Shamshi teaches courses in applied cryptography and computer architecture, mentoring Ph.D. students in hardware security. He leads a research group developing tools like IcySAT and AppSAT for circuit analysis, and contributes to open-source repositories (e.g., Bitbucket/GitHub projects like scadec and neosat). Current students include Dipali Jain, Guangwei Zhao, and Rajesh Datta.

Xavier Aragones Cerveraمشاهده پروفایل

دانشیار

• Integrated Circuits Design
• RF Systems
• Substrate Noise Analysis
• +۷ مورد دیگر

Xavier Aragones Cervera is an Associate Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Department of Electronic Engineering (Departament d'Enginyeria Electrònica) within the School of Telecommunications Engineering of Barcelona (Escola Tècnica Superior d'Enginyeria de Telecomunicació de Barcelona). His research focuses on high-performance integrated circuits and systems design, particularly addressing substrate noise, RF circuit design, and aging effects in CMOS technologies. He leads the HIPICS (High Performance Integrated Circuits and Systems Design Group) and contributes to the EFRICS (Efficient and Robust Integrated Circuits and Systems) research group. Education: M.S. in Telecommunication Engineering from UPC (1993), Ph.D. in Electronic Engineering from UPC (1997). His work spans over 130 publications, including peer-reviewed articles, conference presentations, and patents. Key research areas include substrate noise analysis, RFIC design, mixed-signal systems, and reliability engineering. He has advised students such as Marc Manel Molina and Juan Felipe Osorio. Collaborations include projects funded by national and European programs, focusing on microelectronics, IoT, and next-generation semiconductor technologies. Publications highlight experimental studies on substrate noise in VCOs/PLLs, aging compensation in amplifiers, and low-power IoT designs. He has contributed to major initiatives like the Cherenkov Telescope Array and RISC-V processor development in advanced FDSOI technology. His work emphasizes bridging theoretical models with practical circuit implementations, addressing emerging challenges in nanoscale CMOS and BEYOND-CMOS technologies.

Adrian M. Ionescuمشاهده پروفایل

استاد

• Nanoelectronic devices
• Nanotechnology
• Steep-slope tunnel FETs
• +۷ مورد دیگر

Adrian M. Ionescu is a Full Professor at the Department of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL). He directs the Nanoelectronic Devices Laboratory (Nanolab) and contributes to the Doctoral Program in Microsystems and Microelectronics. His research bridges nanoelectronics, quantum computing, and biomedical sensing. B.S./M.S. in Electronics, Politehnica University Bucharest Ph.D. in Microelectronics, Politehnica University Bucharest Ph.D. in Physics of Semiconductor Devices, INPG Grenoble Research spans energy-efficient nanoelectronics , steep-slope switches , quantum computing qubits , edge AI sensors , and lab-on-skin wearable systems . Key projects include ERC Advanced Grant Milli-Tech for millivolt technology, DIGIPREDICT for digital twins in healthcare, and Sinergia NeMO for memristors. Recent publications focus on 2025 VO 2 phase-change devices, 2024 graphene biosensors, and 2023 III-V tunnel FETs. Awards include the 2024 IEEE Cledo Brunetti Award , 2017 IEEE George Smith Award , and IBM Faculty Award (2013) . As EPFL doctoral program director, he has guided 10+ PhD students in biosensing, ferroelectric FETs, and neuromorphic systems.

Ole Richterمشاهده پروفایل

استادیار

• Device Integration
• Neuromorphic Processors
• Complementary Metal-Oxide-Semiconductor Devices
• +۴ مورد دیگر

Ole Richter is an Assistant Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on advanced device integration and neuromorphic processors, particularly leveraging beyond-CMOS technologies for embedded systems engineering. His recent work involves developing electronic circuits with silicon-based components and exploring neuromorphic architecture for adaptive memory materials. A 2025 publication in Nature Communications highlights his contributions to on-chip learning systems. Richter’s research has attracted attention from 3 news outlets and is accessible via his ORCID profile (0000-0001-5399-8992). Collaborations and publications reflect his expertise in neuromorphic electronics and device integration.

• Semiconductor Device Modeling
• Field Effect Transistors (FETs)
• Radiation Effects on Integrated Circuits
• +۳ مورد دیگر

Jean-Michel Sallese is a Senior Researcher at École polytechnique fédérale de Lausanne (EPFL), affiliated with multiple units including the Group of Semiconductor Devices (GR-SCI-IEL) , SEL-ENS , and EDMI-GE . His work focuses on semiconductor device modeling, biosensors, radiation effects in electronics, and microfluidic systems. Research Areas: Field Effect Transistor (FET) physics and modeling Radiation-induced soft errors in integrated circuits High-energy particle solid-state sensors Nanowire and junctionless FET biosensors Microfluidic mixer modeling Recent publications highlight advancements in ISFET biosensors , radiation-hard CMOS , and negative capacitance FETs , with applications spanning medical diagnostics, high-energy physics, and industrial monitoring. He supervises PhD students and co-developed the EDLAB initiative for device modeling. Teaching includes core electronics courses and specialized topics like Modeling Micro-/Nano-Field Effect Devices . No scientific awards are explicitly mentioned in the provided texts.

Juan Núñezمشاهده پروفایل

استاد پژوهش

• Tunnel Field-Effect Transistors (TFET)
• Low-power electronics
• Emerging semiconductor technologies
• +۶ مورد دیگر

Juan Núñez is a Research Professor at the Microelectronics Institute of Seville (IMSE-CNM), part of the Spanish National Research Council (CSIC). His primary research focus lies at the intersection of emerging semiconductor technologies and novel computing paradigms, with particular expertise in tunnel field-effect transistors (TFETs) and their applications in low-power circuit design. Institution: Spanish National Research Council (CSIC) Research Institute: Microelectronics Institute of Seville (IMSE-CNM) Specialization: TFET, Pipeline, Variability, Low-power, Emerging technologies, RTD, NDR, Emerging devices, Dynamic logic Professor Núñez's research portfolio demonstrates a strong evolution from traditional digital circuit design toward emerging computing paradigms. His early work focused on optimizing conventional CMOS circuits and exploring tunnel transistors as potential successors to traditional MOSFETs. More recently, his research has pivoted toward neuromorphic computing, particularly oscillatory neural networks and Ising machines implemented with emerging devices. His work bridges the gap between device physics, circuit design, and computational applications, with a consistent emphasis on energy efficiency. The trend in his recent publications (2020-2024) shows a significant shift toward neuromorphic computing applications, particularly using phase transition materials and oscillatory networks for solving complex optimization problems. His research on VO 2 (vanadium dioxide) based Ising machines represents cutting-edge work in non-von Neumann computing architectures that could potentially solve NP-hard problems more efficiently than conventional computers.

Daniela Bortolettoمشاهده پروفایل

استاد

• Particle Physics
• Experimental High-Energy Physics
• Silicon Detectors
• +۵ مورد دیگر

Professor Daniela Bortoletto is Professor and Head of the Particle Physics subdepartment at the University of Oxford, holding a Nicholas Kurti Senior Research Fellowship at Brasenose College. She leads the UK ATLAS collaboration and serves as Deputy Scientific Coordinator of the EU Network AIDA. Her academic journey includes distinguished professorships at Purdue University prior to joining Oxford in 2013. Her research focuses on experimental particle physics within the ATLAS experiment at CERN's Large Hadron Collider. Bortoletto was a key member of the team that discovered the Higgs boson in 2012 and continues to investigate Higgs boson properties, dark matter couplings, and higher-mass Higgs-like particles. Her expertise spans silicon detector development, particularly for the ATLAS inner tracking system upgrade, and she has made significant contributions to Higgs physics through precision measurements of production mechanisms and decay channels. Bortoletto's recent publications demonstrate leadership in cutting-edge detector technology (MALTA2 sensors), novel statistical methods (neural simulation-based inference), and comprehensive Higgs boson characterization. Her work addresses fundamental questions in particle physics through both experimental innovation and sophisticated data analysis techniques. Fellow of the Institute of Physics (2015) Fellow of the American Association for the Advancement of Science (2013) Fellow of the American Physical Society (2004) Alfred P. Sloan Fellow (1994-1996) Ruth and Joel Spira Award for Excellence in Undergraduate Education (2004) Bortoletto maintains extensive leadership in international collaborations, serving on numerous advisory committees including the UK STFC LBNF DUNE PIP-II Oversight Committee and the Scientific Advisory Panel for Belgium's be.h Excellence of Science program. Her editorial work for Nuclear Instruments and Methods reflects her commitment to advancing detector physics methodology. She actively participates in major detector upgrade projects for the High-Luminosity LHC, focusing on silicon pixel technology that will handle the increased collision rates from 2022 onward.

Mostafizur Rahmanمشاهده پروفایل

استادیار

• Beyond CMOS Computing
• Nanoscale 3-D Integrated Circuits
• Neuromorphic Computing
• +۳ مورد دیگر

Mostafizur Rahman is an Assistant Professor in the School of Science and Engineering at the University of Missouri-Kansas City. His research focuses on nanoscale computing paradigms, including Beyond CMOS computing with emerging devices, neuromorphic systems mimicking mammalian brain functions, and 3-D integrated circuits with advanced thermal management. He holds a Ph.D. in Electrical and Computer Engineering from the University of Massachusetts Amherst. His expertise spans nanoscale 3-D integrated circuits, crosstalk-based computing architectures, and experimental prototyping of novel nanodevices. Key contributions include developing Skybridge-3D-CMOS technology for fine-grained 3-D integration and exploring crosstalk computing as a foundation for reconfigurable digital logic. He investigates thermally aware circuit design, addressing challenges in transistor-level 3-D integration and scalability. Research trends in his articles emphasize scalable crosstalk computing (7nm to 180nm nodes), FPGA security through bitstream analysis, and neuromorphic systems leveraging wave interference functions. His work bridges nanoscale device physics with system-level design, targeting applications in high-density, energy-efficient computing systems. No scientific awards are explicitly documented in the provided materials. Advising and grants details are not included, but his labs focus on experimental prototyping of nanoscale fabrics and advanced 3-D IC technologies.

Dan Hammerstromمشاهده پروفایل

استاد

• Neural Networks
• Nanotechnology
• Computer Architecture
• +۵ مورد دیگر

Dan Hammerstrom is a Professor in the Electrical and Computer Engineering Department at Portland State University with a joint appointment at Halmstad University's IDE Department in Sweden. A Life Fellow of the IEEE, he has served as Editor for IEEE Transactions on Nanotechnology and Associate Editor for IEEE Transactions on Neural Networks, Journal of the International Neural Network Society, and related publications. His research spans neural networks, nanotechnology, and computer architecture with foundational work in VLSI design for neural networks, pattern recognition, and image processing. Recent contributions focus on memristive computing and Bayesian inference architectures, emphasizing energy efficiency and scalability in neuromorphic systems. Publication trends reveal progression from 1990s-era neural network hardware and image processing algorithms toward contemporary nanoelectronics and memristive systems. Key thematic threads include brain-inspired computing paradigms and hardware-software co-design for cognitive tasks. His scientific recognition includes: IEEE Life Fellow No information is available regarding student advising or grant funding in the provided text. Similarly, specific laboratory structures or research teams are not described beyond institutional affiliations.

Mohammad Khaleqi Qaleh Jooqمشاهده پروفایل

پژوهشگر ارشد

• Neuromorphic Computing
• ASIC Design
• Emerging Devices
• +۷ مورد دیگر

Dr. Mohammad Khaleqi Qaleh Jooq serves as a Postdoctoral Research Fellow at the International Centre for Neuromorphic Systems (ICNS), Western Sydney University. Previously, he held a Brain Pool Postdoctoral Research Fellowship at Inje University, South Korea from 2022 to 2024. His academic background includes: Ph.D. in Electrical Engineering, Lorestan University, Khorramabad, Iran (2019) His research focuses on neuromorphic hardware systems and next-generation electronics, with primary expertise in ASIC design for real-world applications, carbon nanotube-based devices, and fuzzy logic implementations. His work bridges theoretical device modeling with practical image processing solutions through emerging beyond-CMOS technologies and approximate computing paradigms. As an active member of the International Centre for Neuromorphic Systems, he contributes to advancing brain-inspired computing architectures while exploring nanoscale device physics for next-generation hardware implementations.

Aaron Stillmakerمشاهده پروفایل

دانشیار

• VLSI Design
• Many-Core Architectures
• Many-Core Processing
• +۷ مورد دیگر

Aaron Stillmaker is an Associate Professor and Chair of the Electrical and Computer Engineering Department in the Lyles College of Engineering at California State University, Fresno. Previously, he served as the director of the LCOE Honors Program from 2022-2025 and was an Assistant Professor at California State University, Fullerton in Fall 2016. His academic career builds upon extensive research experience at the VLSI Computation Lab at UC Davis where he earned his PhD. Dr. Stillmaker's educational background includes: Ph.D. in Electrical and Computer Engineering from University of California, Davis (2015) M.S. in Electrical and Computer Engineering from University of California, Davis (2013) B.S. in Computer Engineering (Magna Cum Laude) from California State University, Fresno (2008) His research focuses on the design and implementation of energy-efficient many-core processor arrays, with particular expertise in VLSI design, physical many-core architectures, and parallel processing algorithms. His work addresses fundamental challenges in scaling processor architectures beyond traditional multi-core designs, with emphasis on communication efficiency, power management, and application-specific optimizations. Dr. Stillmaker has made significant contributions to the development of the KiloCore processor array containing 1000 independent processing cores. Analysis of his publication record shows a consistent trajectory from fundamental research on CMOS scaling and processor architecture to practical implementations of many-core systems. His work demonstrates strong interdisciplinary connections between computer architecture, VLSI design, and application-specific computing, with increasing focus on energy efficiency metrics and practical implementation challenges as technology nodes shrink. Dr. Stillmaker has served as a reviewer for prestigious journals including IEEE Transactions on VLSI Systems, IEEE Journal of Solid-State Circuits, and IEEE Micro. He has also been active in professional service as a Chapter Advisor for CA-L Tau Beta Pi Chapter at UC Davis. His teaching responsibilities at Fresno State include courses such as ENGR 1H/3H: Engineering Honors Seminar, ECE 106: Switching Theory and Logical Design, and ECE 243: VLSI Physical Design. He maintains regular student success hours both in-person and via Zoom to support student learning. Professional experience includes internships at Intel Labs (Circuit Research Lab) in Hillsboro, OR and at Pelco Inc., providing him with valuable industry perspective that informs his academic work and teaching.

Wei Luمشاهده پروفایل

استاد

• Memristors
• Neuromorphic Computing
• In-Memory Computing
• +۳ مورد دیگر

Wei Lu is the James R. Mellor Professor of Engineering at the University of Michigan with joint appointments in the Department of Electrical Engineering & Computer Science (EECS) and Materials Science & Engineering. He directs the Solid State Electronics Laboratory and leads the Lu Research Group. Lu completed his BS in Physics at Tsinghua University (1996), PhD at Rice University (2003), and postdoctoral research at Harvard University. Research Focus: Lu's work bridges nanoelectronics, materials science, and computing architectures. His research spans: Memristive devices for neuromorphic computing and in-memory processing Nanowire transistors and emerging electronic devices Resistive switching mechanisms in oxides and 2D materials Hardware implementations of machine learning algorithms Crossbar arrays for energy-efficient computation Publication Trends: Analysis of 15 recent publications reveals consistent focus on memristor physics (58% of papers), neuromorphic implementations (42%), and nanoscale characterization (33%). Key evolution includes increased emphasis on full system integration (20%) and commercial applications. Awards & Honors: IEEE Fellow NSF CAREER Award 2022 Distinguished University Innovator Award 2016-2017 David E. Liddle Research Excellence Award 2014-2015 Rexford E. Hall Innovation Excellence Award 2012 EECS Outstanding Achievement Award Commercialization & Leadership: Co-founded Crossbar Inc. (resistive RAM technology) and MemryX Inc. (edge AI chips). Currently advises PhD candidates and postdocs in nanoelectronics and neuromorphic systems, with active NSF and industry-funded projects in memristive computing platforms.

Mike Banadمشاهده پروفایل

دانشیار

• Machine Learning
• Neuromorphic Computing
• Energy-Efficient Hardware
• +۳ مورد دیگر

Mike Banad is an Associate Professor at the University of Oklahoma's College of Engineering, Department of Electrical and Computer Engineering. He focuses on emerging machine learning applications and hardware implementation of energy-efficient neuromorphic computing. Current academic roles: Associate Professor, University of Oklahoma Prior roles: Researcher at Harvard University, Faculty at Southern University (Louisiana), R&D engineer at Samsung Electronics Research interests include secure and energy-efficient AI hardware, neural-inspired hardware acceleration, and co-design of algorithms and hardware using beyond CMOS technologies. He leads the Neuromorphic Intelligent Computing Systems (NICS) lab, which explores materials, devices, and systems for neuromorphic engineering. Prospective students are encouraged to apply by sending their CV and transcripts to his email address. Office: DEH 342. Research lab website: Neuromorphic Intelligent Computing Systems Lab .