جست‌وجوی استاد و پژوهشگر

Ernst Nieburمشاهده پروفایل

استاد

• Computational Neuroscience
• Systems Neuroscience
• Cognitive Neuroscience
• +۱۲ مورد دیگر

Ernst Niebur is a Professor of Neuroscience at Johns Hopkins University, affiliated with the Mind/Brain Institute and the Department of Neuroscience within the Zanvyl Krieger School of Arts and Sciences. He is actively involved in multiple interdisciplinary graduate training programs, including Neuroengineering, Neuroscience, Electrical and Computer Engineering, Psychological and Brain Sciences, Visual Neuroscience, and the Institute for Computational Medicine. His research lies at the intersection of computational and systems neuroscience, focusing on the development of quantitative models of brain function grounded in neurophysiology, anatomy, and behavior. Key research interests include selective attention, perceptual grouping, neural synchrony, decision-making, and neuromorphic implementations of cognitive functions. He has pioneered models of proto-object-based visual saliency and border ownership in visual cortex, often in collaboration with experimental neurophysiologists. The analysis of his recent publications (2017–2024) reveals a strong focus on computational models of attention, saliency, and decision-making, with increasing integration of neuromorphic and robotic applications. His work spans cognitive neuroscience, vision science, and neuroengineering, frequently employing biologically realistic neural network models and analyzing spike train dynamics. There is a consistent trend toward modeling higher-order cognitive functions using system-level approaches that incorporate temporal coding and network dynamics. Ernst Niebur has not been mentioned as having received formal scientific awards in the provided text, but his extensive publication record in high-impact journals such as PLoS Computational Biology, Journal of Neuroscience, Vision Research, and IEEE Transactions demonstrates significant scholarly impact. He advises graduate students and has mentored numerous former lab members, contributing to training in neuroscience and neuroengineering. While no specific grants are listed, his involvement in multiple NIH-funded training initiatives suggests active participation in funded research. His lab collaborates across disciplines, particularly with engineers and neurophysiologists, and explores both biological and artificial implementations of neural computation. His lab focuses on constructing and testing computational models of neural systems, particularly those involved in attention and perception. The lab integrates theoretical modeling with empirical data, maintaining close ties with experimental groups. Research themes include dynamic visual saliency, neuromorphic hardware implementations, and neural mechanisms of decision-making.

• Neuromorphic Engineering
• Machine Learning
• Deep Learning
• +۱۲ مورد دیگر

Gaetano Di Caterina is a Senior Lecturer in the Department of Electronic and Electrical Engineering at the University of Strathclyde, within the Faculty of Engineering. He is a Fellow of the Higher Education Academy (HEA) and leads the Neuromorphic Sensor Signal Processing Lab as part of the Centre for Signal & Image Processing (CeSIP). He holds key academic roles, including Course Director for the MSc in Machine Learning and Deep Learning (joint with Computer and Information Sciences) and Adviser of Studies for Year 1 of the Computer and Electronic Systems undergraduate program. He is currently the Leonardo Lecturer (2023–2027) in a strategic partnership between Strathclyde and Leonardo UK Ltd. PG Certificate in Learning and Teaching in Higher Education, University of Strathclyde (2021–2024) Doctor of Engineering, University of Strathclyde (2009–2013) Master of Engineering, University of Strathclyde (2006–2009) Bachelor of Engineering, Università degli Studi di Napoli Federico II (2001–2005) His research centers on neuromorphic engineering, machine learning, deep learning, digital signal processing, and embedded systems, with applications in surveillance, medical diagnostics, robotics, and space and underwater sensing. He applies these techniques to video analytics, EMG and speech processing, medical imaging, and event-based vision systems. His work often integrates spiking neural networks and neuromorphic sensors to improve robustness in low-SNR and real-time environments. His recent publications reflect a strong trend in leveraging neuromorphic computing and deep learning for challenging real-world problems—such as turbulence mitigation in imaging, voice disorder classification, underwater and space object detection, and brain signal analysis. These works span computer vision, biomedical engineering, signal processing, and AI, demonstrating a highly interdisciplinary approach focused on practical, deployable solutions. His scientific recognition includes: Best Paper Award, IEEE, 27 Aug 2022 IEEE Brain Data Bank Challenges and Competitions, 31 Oct 2017 He is actively involved in research funding and supervision, serving as Principal Investigator on multiple projects funded by the Air Force Office of Scientific Research and Leonardo UK Ltd. He supervises PhD students and postdoctoral researchers in the Neuromorphic Sensor Signal Processing Lab. His professional activities include hosting academic visitors and participating in strategic AI workshops at Strathclyde. He is currently accepting PhD students and continues to expand his work in applied AI and neuromorphic technologies. He leads the Neuromorphic Sensor Signal Processing Lab within the CeSIP research group, fostering innovation in sensor fusion, spiking neural networks, and real-time embedded AI systems. The lab engages in both theoretical and applied research, collaborating with industry and defense partners to develop next-generation intelligent sensing solutions.

Mihai Adrian Ionescuمشاهده پروفایل

استاد

• Nanoelectronic Devices
• Nanotechnology
• Steep-Slope Tunnel FETs
• +۷ مورد دیگر

Adrian M. Ionescu is a Professor at the Nanoelectronic Devices Laboratory (Nanolab) within the School of Engineering at École Polytechnique Fédérale de Lausanne (EPFL) , Switzerland. His work spans nanoelectronics , quantum computing , and energy-efficient technologies , with a focus on steep-slope transistors , ferroelectric materials , and digital twins for healthcare . He leads major European projects like DIGIPREDICT and Milli-Tech , pioneering low-power devices for IoT and quantum systems . Education: B.S./M.S. in Electronics and Telecommunications (1989, Politehnica University Bucharest); PhD in Microelectronics (1994, Bucharest) and Physics of Semiconductor Devices (1997, Grenoble INP). Research Interests: Beyond CMOS devices, quantum qubits , edge AI sensors , ferroelectric negative capacitance , and lab-on-skin technology for biofluid analysis . Scientific Awards: IEEE Cledo Brunetti Award (2024) IEEE George Smith Award (2017) IBM Faculty Award (2013) André Blondel Medal (2009) Swiss Academy of Sciences (SATW) Member (2015) Leadership: Founder of Nanolab; former Director of EPFL's Doctoral Program in Microsystems and Microelectronics; Editor of IEEE Transactions on Electron Devices; Technical Chair of IEEE conferences. Grants & Projects: Advanced ERC Grant for Milli-Tech (energy-efficient DIGIPREDICT (digital twins for disease prediction), and SINERGIA NEMO (neuromimetic memristors). Students: Supervised over 40 PhD students, including Ehsan Ansari, Fabio Bersano, and Vanessa Conti, working on quantum dots , sweat biomarkers , and van der Waals heterostructures . Lab Focus: Nanolab develops low-power nanoelectronics , quantum computing architectures , and sustainable fabrication processes using non-toxic materials .

• Surface Science
• 2D Materials
• Condensed Matter Physics
• +۳ مورد دیگر

Abdullah Al-Mahboob is a Staff Scientist at the Center for Functional Nanomaterials, Brookhaven National Laboratory, and an Adjunct Assistant Professor at State University of New York – Farmingdale State College and CUNY-Queensborough Community College. His research focuses on surface and interface science, 2D materials, and quantum heterostructures. Education: Doctor of Science (Tohoku University, Japan), Master of Science/Master of Philosophy (Jahangirnagar University, Bangladesh). His work leverages spectro-microscopy and robotic fabrication tools like CFN QPress to study dynamical phenomena in 2D quantum materials, including interlayer energy transfer, excitonic structure, and surface magnetism. He has published in journals such as Nano Letters , Nature , and Physical Review B , with a focus on 2D materials, heterostructures, and surface dynamics. Dr. Al-Mahboob has held academic and research roles across institutions including Yale University, University of Liverpool, and Tohoku University. He utilizes techniques like Low-Energy Electron Microscopy (LEEM), X-ray Photoemission Electron Microscopy (XPEEM), and time-resolved angle-resolved photoemission spectroscopy (TR-ARPES) to investigate quantum and nanoscale properties.

Liam Mc Daidمشاهده پروفایل

استاد

• Computational Neuroscience
• Neural Networks
• Computer Hardware
• +۵ مورد دیگر

Liam Mc Daid is a Professor of Computational Neuroscience at the School of Computing, Engineering and Intelligent Systems, Ulster University. He serves as Research Director for Computing, Engineering and Intelligent Systems within the Faculty of Computing, Engineering and Built Environment. His research spans computational neuroscience, neural networks, and biomedical engineering, with a focus on hardware implementations and fault detection in systems like RISC-V. He contributes to UN Sustainable Development Goals related to health and technology. Recent publications highlight his work on spiking neural networks for fault detection, deep learning in medical imaging, and biotech assays for cardiac diagnostics. His research has been cited over 1,600 times, with an h-index of 23. Scientific awards include the 2011 Adaptive Routing Strategies Prize, the 2010 Northern Ireland Science Park 25K Biotechnology Award, and InventNI’s 2019 Life and Health Startup of the Year. His work has been featured in media outlets addressing asthma management and health tech innovation.

• Artificial Intelligence
• Machine Learning
• High-performance Computing
• +۴ مورد دیگر

Yihui "Ray" Ren is a Research Scientist at the Brookhaven National Laboratory , affiliated with the Computational Science Initiative . His work bridges Artificial Intelligence with scientific domains through novel hardware integration, neural network optimization, and privacy-preserving methodologies. Education: PhD in Physics from University of Notre Dame (2015) and Hunan University (2009) Redefining AI's application in science, Ren's research encompasses: Unpaired image translation for simulation-experiment alignment Neural network optimization for real-time systems Novel hardware exploration and benchmarking Privacy-preserving AI frameworks Scientific data compression techniques His publications (2014–2023) reveal interdisciplinary patterns spanning: High-performance computing for AI workloads Edge computing implementations in scientific instrumentation GAN architectures for imaging applications 3D data processing in medical imaging Network reliability analysis with statistical mechanics Ren has mentored SULI interns and contributed to projects in: ASIC-based neuromorphic computing Climate prediction systems Scientific workflow resilience TPC detector data compression Front-end electronics AI integration His research combines theoretical network dynamics with practical AI deployment, focusing on hardware-software co-design and cross-domain scientific applications.

Susanne Hoffmann-Eifertمشاهده پروفایل

پژوهشگر

• Nanoelectronics
• Thin Film Materials
• Memristive Devices
• +۷ مورد دیگر

Dr. Susanne Hoffmann-Eifert is a group leader at the Peter Grünberg Institute (PGI), specifically within the Electronic Materials (PGI-7) department at Forschungszentrum Jülich, Germany. She leads the research group focused on Functional Thin Films for Novel Electronic Devices, aiming to advance nanoscale memory technologies for neuromorphic computing applications. Her research centers on the experimental development and optimization of nanoscale electronic devices using metal oxides and 2D materials. Key techniques include atomic layer deposition (ALD) and chemical vapor deposition (CVD) for growing ultra-thin films, with a strong emphasis on controlling layer and interface properties to enhance device performance. The group investigates amorphous and nanocrystalline thin films, interface effects, and the physics underlying memristive behavior, targeting integration into 3D neuromorphic circuits for energy-efficient computing. Dr. Hoffmann-Eifert supervises a team of doctoral researchers and a master’s student, contributing to the training of next-generation scientists in advanced materials and device engineering. Her work bridges fundamental materials science with practical applications in next-generation computing architectures. She is actively involved in cutting-edge research projects related to neuromorphic hardware, with publications and collaborations reflecting her expertise in functional thin films and nanodevices. Her leadership in this field positions her at the forefront of efforts to develop beyond-CMOS computing technologies. ORCID: Available via profile Google Scholar: Available via profile

Mike Schwarzمشاهده پروفایل

استاد

• Semiconductor Device Physics
• Schottky Barrier Transistors
• Compact Modeling
• +۵ مورد دیگر

Professor Mike Schwarz is a faculty member at the Technical University of Mittelhessen (THM), where he holds a position in the Department of Electrical Engineering and Information Technology. He has been serving as Professor for Embedded Systems and Simulation since October 2020. Prior to his academic appointment, he worked at Robert Bosch GmbH as Head of Diode Design and Head of Process and Device Simulations MEMS from 2016-2020, and as a MEMS Sensor Design & Simulation Engineer from 2013-2020. His educational background includes a Doctor of Philosophy from Universitat Rovira i Virgili, Tarragona, Spain (2009-2012), where he completed his dissertation on "Two-Dimensional Analytical Predictive Modeling of Schottky Barrier SOI and Multi-Gate MOSFETs." He also holds a Master of Engineering from the same institution (2008-2009) and a Diploma in Engineering from Gießen-Friedberg University of Applied Sciences (2003-2008). Professor Schwarz's research spans semiconductor device physics, particularly Schottky barrier transistors and compact modeling, MEMS sensor design, neuromorphic computing using memristive devices, and machine learning applications in complex plasma physics. His recent work shows a clear trend toward applying AI techniques to plasma particle tracking and advancing cryogenic electronics through detailed physics-based modeling of Schottky barrier devices. He has made significant contributions to understanding device behavior at deep cryogenic temperatures, which has implications for quantum computing applications. His publication record demonstrates consistent productivity across multiple venues, with recent articles appearing in Solid-State Electronics, Machine Learning & Knowledge Extraction, New Journal of Physics, and Nano Futures. His work bridges theoretical device physics with practical applications in sensor technology and neuromorphic computing. Best paper honorable mention award LAEDC conference, Guatemala 2024 IEEE Transactions on Electron Devices List of Golden Reviewers 2015, 2012 IEEE Electron Devices Letters List of Golden Reviewers 2017, 2015 Awarded for the second best dissertation of the year (DEEEA, URV) 2013 Doctorate with distinction 2012 Awarded the Friedrich Dessauer Prize (best diploma thesis of the year) 2008 Professor Schwarz serves on multiple IEEE committees, including as Editor for IEEE Transactions on Electronic Devices since 2025, and as Chair of the IEEE EDS Germany Chapter from 2020-2025. He has been actively involved in organizing the Symposium Schottky Barrier MOS Devices since 2016 and has served on technical program committees for MIXDES and other conferences. His professional activities demonstrate strong engagement with both academic and industrial communities in semiconductor device research.

Nan Duمشاهده پروفایل

پژوهشگر

• Memristive Technology
• Neuromorphic Computing
• Resistive Switching
• +۳ مورد دیگر

Nan Du is a Researcher at the Leibniz Institute of Photonic Technology in the Photonics and Quantum Detection department, leading the Radiometry working group . Her research focuses on memristive devices, neuromorphic computing, and cryptographic applications. Her publications highlight expertise in resistive switching dynamics , self-rectifying analog memristors , and security vulnerabilities in emerging technologies . She has advanced understanding in multiferroic materials like BiFeO3 and YMnO3, emphasizing applications in in-memory computing and true random number generation . The 15 most recent articles (2023-2025) span fields like neuromorphic architectures , deep learning hardware , and quantum detection , with subtopics covering oxygen vacancy migration , crossbar array optimization , and side-channel attack mitigation . She actively explores interfacial layer engineering to enhance memristor performance and two-dimensional permutation-based protection for deep neural networks. Her work addresses critical challenges in device variability , stochastic behavior modeling , and energy-efficient security solutions .

• Spin Electronics
• Electron Transport
• Heat Flow
• +۶ مورد دیگر

William H. Rippard is a Researcher at the National Institute of Standards and Technology (NIST), where he serves as Group Leader of the Spin Electronics Group in the Quantum Electrodynamics Division. His work focuses on metrologies for spin-based devices, including electron transport, heat flow, and spin excitations in nanoscale systems for applications in computing and quantum sensing. Education BS in Physics and Mathematics, University of Florida (1994) PhD in Applied Physics, Cornell University (2000) His research spans spin electronics, neuromorphic hardware, and hybrid superconducting/ferromagnet structures, with over 70 peer-reviewed publications in journals like Nature and Physical Review Letters . He has contributed to advancements in high-frequency behaviors of nanoscale devices and their applications in quantum-enhanced magnetic sensing. His scientific awards include the 2008 PECASE, NIST Silver, and Bronze awards. He served as Acting Division Chief for the Quantum Electrodynamics Division from 2021 to 2023.

Johannes Zierenbergمشاهده پروفایل

پژوهشگر

• Statistical Physics
• Monte Carlo Simulations
• Neural Networks
• +۳ مورد دیگر

Johannes Zierenberg is a Researcher at the Max Planck Institute for Dynamics and Self-Organization in Göttingen, Germany, and part of Viola Priesemann's group. His work bridges statistical physics, computational neuroscience, and epidemic modeling through advanced data analysis and Monte Carlo simulations PhD in Physics from University of Leipzig (2016) MSc in Physics from University of Leipzig (2010) BSc in Physics from University of Leipzig (2008) His research focuses on stochastic complex systems with expertise in: Monte Carlo simulations Statistical physics of neural networks Epidemic spread dynamics Finite-size scaling analysis Homeostatic plasticity Disordered media modeling Article trends show interdisciplinary work across: Neuroscience (40%) Statistical physics (35%) Epidemiological modeling (15%) Computational methods (10%) Key software contributions include: MR. Estimator - Neural timescale analysis tool Loading MonteCarloX.jl - Julia package for advanced simulations correlated_disorder - Lattice disorder generation Technical collaborations with: Prof. Wolfhard Janke (Leipzig) on polymer physics Dr. Viola Priesemann on neural dynamics Dr. F. Paul Spitzner on epidemic modeling Prof. Michael Wilczek on complex systems

Kofi Appiahمشاهده پروفایل

مدرس ارشد

• Edge Computing
• Neuromorphic Hardware
• Embedded Computer Vision
• +۶ مورد دیگر

Dr. Kofi Appiah serves as a Senior Lecturer in the Department of Computer Science at the University of York, where he contributes to undergraduate and postgraduate teaching while leading departmental initiatives as Microsoft Certification Coordinator and Undergraduate Admissions Tutor. His academic credentials include: PhD in Computer Science, University of Lincoln, UK MSc in Computer Science, University of Oxford, UK MSc in Electrical & Electronic Engineering (System-on-Chip specialization), Royal Institute of Technology (KTH), Sweden BSc in Computer Science, University of Science and Technology, Ghana Dr. Appiah's research bridges hardware and AI with core interests in Edge Computing, Neuromorphic Hardware, and Embedded Computer Vision. His work extends to Health Informatics, Medical Devices, and Machine Learning applications where he develops efficient systems for real-world deployment. This interdisciplinary approach connects computer vision with healthcare diagnostics, environmental monitoring, and energy-aware computing solutions. Analysis of his recent publications reveals a strong trajectory in applying neuromorphic principles to practical challenges, including wildlife conservation through video analytics, medical image classification for cancer detection, and energy-efficient smart-home systems. His work consistently integrates deep learning with hardware acceleration for edge deployment across diverse domains from power grid security to elderly care. As an academic supervisor, Dr. Appiah has guided research degree students as Director of Studies and Second Supervisor. His departmental leadership includes critical roles in admissions and certification programs. He actively contributes to the Vision, Graphics and Learning (VGL) research group, collaborating on computer vision and machine learning advancements within York's Department of Computer Science.

• Tactile Sensing
• Robotics
• Pattern Recognition
• +۵ مورد دیگر

Alejandro Pequeno Zurro serves as a Postdoctoral Researcher at the University of Groningen's Faculty of Science and Engineering, specifically within the Bio-inspired Circuits & Systems research group at the Zernike Institute for Advanced Materials. His work bridges engineering, computer science, and neuroscience through innovative applications of neuromorphic hardware and bio-inspired systems. Dr. Pequeno Zurro's research focuses on tactile sensing in robotics , spatio-temporal pattern recognition , and human-robot interaction systems . His work combines neuromorphic computing with practical applications like Braille reading systems and social robotics, creating novel approaches to machine perception. His research demonstrates how bio-inspired circuit designs can significantly enhance robotic capabilities in human environments, particularly through improved tactile feedback mechanisms. His recent publications reveal a strong emphasis on applying neuromorphic hardware to solve real-world problems in accessibility and human-robot collaboration. The research shows particular strength in translating biological sensory principles into technological solutions, with applications ranging from assistive technologies for the visually impaired to more natural human-robot interactions. As a member of the Zernike Institute's research team, Pequeno Zurro collaborates extensively with international researchers in robotics and neuromorphic engineering. His work appears in reputable venues including Frontiers in Neuroscience and the Social Robotics conference proceedings, demonstrating both academic rigor and practical relevance to emerging robotic systems.

• Sustainable Computing
• Energy-Efficient Algorithms
• IoT
• +۵ مورد دیگر

Denisa Constantinescu is a postdoctoral researcher at the Embedded Systems Laboratory (ESL) at École Polytechnique Fédérale de Lausanne (EPFL) since 2022, affiliated with EcoCloud for sustainable computing technologies. She holds a PhD in Mechatronics (2022) and a Master's in Computer Engineering (2017) from Universidad de Málaga, and a B.Sc. in Systems Engineering from University Politehnica of Bucharest (2015). Her research focuses on sustainable and energy-efficient algorithms for wearables, IoT, and data centers, with specialization in scientific computing for astronomy, mobile robot navigation, and biomedical domains . She has received recognition including the Intel oneAPI Innovator Award (2020) and the SCIE-ZONTA Award (2021) . Her recent publications address themes like FPGA acceleration in genomics and astronomy , privacy-preserving biomedical algorithms , urban digital twins for climate action , and energy-efficient interferometry for radio astronomy . She actively contributes to scientific community service as a reviewer and organizer for conferences like PASC25 and ICPP 2025. Intel oneAPI Innovator (2020) SCIE-ZONTA 2021 Award IMFAHE Shark Tank Contest Winner Erasmus Student Scholarship As a daily supervisor of 5 PhD students and 2 Master's theses at EPFL, and through her involvement with Campus Tech Chicas in Spain, she advocates for equal opportunities in education.

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Dr. Varuna De Silva is a Reader (Associate Professor) in Machine Intelligence at Loughborough University London, where he serves as Programme Director for the Artificial Intelligence and Data Analytics MSc programme. He joined as a Lecturer in 2016 after industry experience at Apical Ltd, where his patented algorithms were deployed in over 500 million devices. His research focuses on scaling AI to real-world engineering systems through multi-agent reinforcement learning, multimodal computer vision, and simulation modeling. Education: B.Sc. (Hons) Engineering, University of Moratuwa, Sri Lanka (2007) Ph.D. Electronic Engineering, University of Surrey (2011) PG Cert in Academic Practice, Loughborough University (2018) His work spans reinforcement learning architectures, neuromorphic computing, and AI applications in healthcare, environmental systems, and autonomous vehicles. Recent publications demonstrate strong emphasis on interpretable AI, brain-computer interfaces, and causal reasoning in multi-agent environments, with consistent cross-disciplinary integration of machine learning techniques. Awards: IEEE Chester Sall Award, Consumer Electronics Society (2010) Vice Chancellor's Award for Best Post Graduate Research Student (2011) Overseas Research Scholarships Award (2008) He leads EPSRC-funded projects on next-generation machine intelligence and maintains industrial collaborations. As module leader for Bayesian Methods in Deep Learning and Statistical Methods in Finance, he drives curriculum development in AI. His team actively researches autonomous systems and human-AI interaction frameworks.