جست‌وجوی استاد و پژوهشگر

Arturo Morgado Estevezمشاهده پروفایل

استاد

• Neuromorphic Engineering
• Robotics
• Computer Architecture
• +۵ مورد دیگر

Arturo Morgado Estevez is a Professor at the University of Cadiz, working in the Department of Automation, Electronics, Architecture and Computer Networks Engineering. His primary affiliation is with the Engineering school at the University of Cadiz, where he leads research in the TEP940 Applied Robotics research group. His work spans multiple technical domains with a strong focus on neuromorphic engineering and robotics applications. His research interests encompass Neuromorphic Engineering, Robotics, Computer Architecture, Real-Time Computing, FPGA Design, Bio-inspired Computing, Address-Event-Representation Systems, and Embedded Systems. Morgado Estevez specializes in developing spike-based processing systems that mimic biological neural networks, particularly focusing on applications in robotics, computer vision, and sensor systems. His work bridges the gap between biological inspiration and practical engineering implementations, with particular emphasis on real-time performance and hardware efficiency. An analysis of his recent publications reveals a strong trend toward applied robotics and embedded systems, with increasing focus on medical applications, assistive technologies, and energy efficiency. His research has evolved from fundamental neuromorphic architectures to practical implementations in prosthetics, industrial inspection, and environmental monitoring systems. Many of his recent works combine machine learning techniques with specialized hardware implementations for specific application domains. Morgado Estevez has been actively involved in educational initiatives, particularly in computer science education and robotics teaching methodologies. His work includes developing innovative teaching approaches for programming languages and engineering education, with several publications focused on educational technology and pedagogical methods. His laboratory work centers around the TEP940 Applied Robotics research group, where he has developed multiple FPGA-based implementations of neuromorphic systems. His team has created complete spike-based architectures from Dynamic Vision Sensors to robotic motor control, demonstrating practical applications of bio-inspired computing in real-world robotic systems. The research group has made significant contributions to Address-Event-Representation processing and its implementation on parallel computing platforms.

• Hardware-Software Co-Design
• Compiler Design
• Embedded Systems
• +۵ مورد دیگر

Dr. Frank Hannig is a Professor at the Department of Computer Science, Friedrich Alexander University Erlangen-Nuremberg (FAU), Germany. He serves as the Head of the Architecture and Compiler Design Group within the Hardware-Software-Co-Design department (Department 12). With a career spanning over two decades at FAU since 2003, he has established himself as a leading researcher in hardware-software co-design, compiler design, and embedded systems. Dr. Hannig received his Diploma degree in Electrical Engineering/Computer Science from the University of Paderborn in 2000, followed by his Dr.-Ing. degree in Computer Science from FAU in 2009 with a thesis on "Scheduling Techniques for High-Throughput Loop Accelerators." He completed his habilitation (Dr.-Ing. habil.) in 2018 with a thesis titled "Domain-specific and Resource-aware Computing," which qualifies him for a full professorship in the German academic system. His research focuses on hardware-software co-design, compiler design for embedded systems, reconfigurable computing, parallel systems, and machine learning acceleration. Dr. Hannig has made significant contributions to domain-specific and resource-aware computing, with applications in image processing, automotive systems, and edge AI. His work bridges the gap between high-level programming models and efficient hardware implementations, particularly for resource-constrained environments. Dr. Hannig's recent publications reveal a strong trend toward efficient machine learning deployment on embedded devices and microcontrollers, with particular emphasis on memory optimization, hardware acceleration, and low-precision computing. His research spans multiple domains including computer architecture, machine learning, and embedded systems, with a focus on practical implementations for real-world applications. Dr. Hannig serves as an Associate Editor for IEEE Embedded Systems Letters and the Journal of Real-Time Image Processing. He has organized numerous prestigious conferences including SLOHA 2021, ARC 2021, and Euro-Par 2021, demonstrating his leadership in the academic community. As an educator, Dr. Hannig teaches courses on Domain-Specific and Resource-Aware Computing on Multicore Architectures, Parallel Systems, and Embedded Systems. He has supervised numerous students through lectures, exercises, and seminars covering electronic system level design and multi-core architectures. Dr. Hannig leads several significant research projects including InvasIC (DFG Transregional Collaborative Research Centre), ExaStencils (Advanced Stencil-Code Engineering), and HBS (DFG Research Training Group on Heterogeneous Image Systems). His work with the HIPAcc open-source project has contributed to domain-specific language and compiler development for image processing applications.

• Analog/Mixed Signal Design
• Biomedical Sensor Design
• Hardware Security
• +۳ مورد دیگر

Arindam Sanyal is an Assistant Professor in the School of Electrical, Computer and Energy Engineering at Arizona State University . Prior to joining ASU, he served as an Assistant Professor at the University at Buffalo (SUNY) and worked as an Analog Design Engineer at Silicon Laboratories (2015-2016). He earned his PhD in Electrical and Computer Engineering from the University of Texas at Austin (2015), an M.Tech from IIT Kharagpur (2009), and a B.E. in Electrical Engineering from Jadavpur University (2007). Education : PhD (University of Texas at Austin), M.Tech (IIT Kharagpur), B.E. (Jadavpur University) Research Areas : Analog/Mixed Signal Circuits, Biomedical Sensors, Hardware Security, Neuromorphic Computing, Machine Learning in Circuits, Low-Power Electronics His recent work focuses on integrating machine learning with analog/mixed-signal circuits for real-time health monitoring, hardware security, and energy-efficient AI. Publications highlight trends in VCO ADCs , Reservoir Computing , and Stochastic Delta-Sigma Modulators . A notable NSF CRII Grant (2020) supports his research on analog AI for environmental monitoring. Teaching includes courses like Analog Integrated Circuits and Analog to Digital Converters . He leads the Mixed Signals Lab at ASU, developing flexible electronics and sensor fusion systems.

Jo Plestedمشاهده پروفایل

عضو هیئت علمی

• Deep learning
• Artificial intelligence
• Machine learning
• +۴ مورد دیگر

Dr Jo Plested is an Associate Lecturer at UNSW Canberra with over 10 years of research experience in deep learning, specializing in transfer learning for small specialized datasets. She has made significant contributions to both academia and defense applications through her research, teaching, and leadership as Head of the Deep Learning Group at UNSW Canberra. Her research interests span multiple interdisciplinary domains including: Transfer learning for small specialized datasets Bushfire spread prediction modeling with the UNSW Bushfire Research Group Visual swarming algorithms for robotics applications Physical implementation of neural networks using novel materials Quantum computing applications where deep learning helps predict and overcome environmental noise Military AI ethics examining implications of targeting systems Dr Plested has secured over $900,000 in research funding as chief investigator for 5 external grants. Her publication record includes 2 journal articles, 19 conference papers, and 2 preprints. Analysis of her recent publications reveals a strong focus on practical applications of deep learning across diverse domains, with particular emphasis on defense-related applications, environmental modeling, and ethical considerations in AI deployment. She has supervised over 20 students working on Honours, Masters, and Chief of Defence Force projects, with more than 20 of these student projects published in high-ranking international conferences and journals. Dr Plested created the honours-level Deep Learning course at UNSW Canberra and previously developed and delivered deep learning content for courses at the Australian National University for up to 250 students. She is also part of a team that developed a data science and AI short course specifically for Defence applications. Currently recruiting PhD students with interests in deep learning, Dr Plested offers scholarships of $35,000 AUD, seeking candidates with backgrounds in deep learning or strong mathematical foundations.

• Silicon Photonics
• Neuromorphic Computing
• Optical Networking
• +۵ مورد دیگر

Miltiadis Moralis-Pegios is a Lecturer at the Department of Informatics, School of Informatics, Aristotle University of Thessaloniki (AUTH). He has been actively involved in academic teaching and research projects since 2002. Current Courses : Information Theory and Coding (Spring 2024–25) Research Interests : Focus on silicon photonics, neuromorphic computing, and optical networking for high-speed data centers. Applications include graphene-based secure IoT, environmental pollution detection via hyperspectral sensors, and plasmonic circuits for AI hardware. Notable Research : Participation in projects like Heterogeneously integrated photonic chiplets for AI engines , Smart environmental sensing systems , and Graphene optical platforms (2024–2027). Earlier work includes optical transceivers for data centers and flood risk management systems. Collaborations : Extensive involvement in EU-funded initiatives (EDAR, IN LIFE, HARC) and national projects (e.g., VERITAS , Archimedes ).

• Biomimetic Robotics
• Neurorobotics
• Spiking Neural Networks
• +۵ مورد دیگر

Benedikt Feldotto is a Researcher at the Technical University of Munich, affiliated with the Department of Computer Science, Chair of Robotics, Artificial Intelligence and Real-Time Systems led by Prof. Knoll. He works on the Neurorobotics Platform as part of the European Human Brain Project and serves as lead developer of the NRP Robot Designer since 2017. His educational background includes a Bachelor of Engineering in Mechatronics from Baden-Wuerttemberg Cooperative State University (DHBW) with industry experience in automation pre-development, including a development stay in the USA. He further specialized with a Master of Science in "Robotics, Cognition, Intelligence" at Technical University of Munich. Feldotto's research focuses on biomimetic learning in neurorobotic systems, with particular emphasis on spiking neural networks for embodied cognition. His work bridges cognitive neuroscience and robotics, exploring how robots can interact naturally with humans and environments through biologically-inspired learning mechanisms. Key areas include musculoskeletal modeling, human-robot interaction, and the ethical implications of learning robots in society. He actively develops simulation tools to enable large-scale neurorobotic experiments and has contributed significantly to the Human Brain Project's Neurorobotics Platform infrastructure. His publication record shows a clear progression from foundational platform development toward increasingly sophisticated embodied simulations using high-performance computing. Recent work emphasizes scaling spiking neural networks for robotic control, analyzing neural network architectures for specific motor tasks, and validating biomechanical models against human movement data. The research consistently connects theoretical neuroscience with practical robotics applications. His scientific recognition includes: Best Poster Award from the Graduate School of Bioengineering (2018) As an educator, Feldotto has taught Cognitive Systems course exercises from Spring Semester 2018 through Spring Semester 2022. He regularly offers thesis and project opportunities in biomimetic robotics and neural network learning, though no current openings are listed. His teaching and research are supported through the European Human Brain Project funding framework. Feldotto leads development of the NRP Robot Designer, a critical tool within the Neurorobotics Platform ecosystem. He has presented this work internationally through workshops at major conferences including the European Robotics Forum and Japanese Neural Network Society meetings. His outreach extends to public exhibitions at the Deutsches Museum and participation in ethics discussions on robot stereotypes, demonstrating commitment to both technical advancement and societal implications of neurorobotics research.

Tom Haywardمشاهده پروفایل

استاد

• Nano-scale Magnetism
• Spintronics
• Neuromorphic Computing
• +۳ مورد دیگر

Professor Tom Hayward is affiliated with the School of Chemical, Materials and Biological Engineering at the University of Sheffield. His research focuses on nano-scale magnetism , spintronics , and neuromorphic computing , aiming to develop energy-efficient computing devices and understand fundamental magnetic physics. Key research areas include micromagnetic simulation , unconventional computing , and magnetic domain walls . Recent work explores stochastic synapses for machine learning and chirality-encoded logic gates . His publications highlight interdisciplinary applications spanning artificial intelligence and magnetic biosensors . Scientific awards include the EPSRC Career Acceleration Fellowship (2012) . His work is funded by EPSRC Leverhulme Trust European Commission Royal Society Sir Henry Royce Institute . Professor Hayward teaches modules such as MAT6390 (Nanoscale Magnetic Materials and Devices) and supervises projects in materials physics . He leads a research group developing magnetic metamaterials and collaborates with institutions like the University of Cambridge and Royal Society.

Lorenzo De Marinisمشاهده پروفایل

استادیار

• Photonic Neuromorphic Computing
• Photonic Integrated Circuits (PIC)
• Electro-Optic Co-Design
• +۴ مورد دیگر

Lorenzo De Marinis is an Assistant Professor affiliated with the Italian National Quantum Science and Technology Institute. His research focuses on photonic neuromorphic computing, photonic integrated circuits (PIC), and electro-optic co-design, with applications in quantum computing, machine learning for optical communications, and AI-driven network diagnostics. Primary Fields: Photonics, Neuromorphic Computing, Machine Learning Key Technologies: SOI, InP, LNOI, SiN His recent publications highlight advancements in photonic-electronic integration, noise-resilient neural networks, and energy-efficient optical systems. He has collaborated on projects related to DDoS attack detection, coherent combiners, and failure management in optical networks. While no specific awards or students are mentioned, his work bridges photonic circuit design with practical AI applications in constrained environments.

• Biomedical Engineering
• Medical Imaging
• Image Processing
• +۵ مورد دیگر

Dr. José Antonio Pérez Carrasco is a Full Professor in the Department of Signal Theory and Communications at the University of Seville. He leads the Signal Processing and Communications research group and has participated in numerous biomedical engineering projects focused on medical imaging, surgical planning, and neuromorphic vision systems. Professional Status: Full Professor Research Group: Signal Processing and Communications His research integrates computer vision, machine learning, and biomedical applications to solve medical challenges such as skin lesion segmentation, thermographic diagnosis of hemangiomas, and 3D CT image analysis. He has developed innovative algorithms for bone/muscle segmentation and retroperitoneal tumor delineation, often using convex relaxation techniques and event-based processing. Dr. Pérez Carrasco has secured multiple research grants including Burn Image Analysis System (PI-1597/29/2016), Mitochondrial segmentation in CT images (PI-1209/2013), and MISSION ALPHA (AEI-010500-2023-46). He has directed the doctoral thesis of Cristina Suárez Mejías on tissue segmentation and mentored numerous projects in intelligent infrared vision sensors. He actively contributes to university teaching innovation, implementing participatory techniques in engineering education and integrating company tasks into practical subjects. His work bridges academia with industry through contracts like the System for bone marrow radiotherapy toxicity analysis (PI-2566/33/2025).

• Mathematics of consciousness
• Neuromorphic information processing
• Network theory
• +۷ مورد دیگر

Professor Peter Grindrod holds a faculty position at the Mathematical Institute, University of Oxford, where he leads research in applied mathematics with focus on neuromorphic computing and network theory. His affiliations include the Machine Learning and Data Science research group and the Oxford Centre for Industrial and Applied Mathematics. Research interests span mathematics of consciousness, neuromorphic information processing, network theory, and digital marketing. His innovative work on cortex simulations via Kuramoto-type models enables laptop-based simulations of neural columns instead of computationally expensive full-brain models. He investigates AI's inner workings, particularly how layer-by-layer topology explains vulnerability to adversarial attacks. His 15 most recent publications (2018-2025) reveal strong focus on consciousness modeling (27%), network dynamics (33%), and AI/neuromorphic computing (40%). Key trends include mathematical consciousness frameworks, sparse network modeling for brain simulation, and topology-based analysis of neural networks. CBE honor Grindrod leads significant research strategy initiatives, focusing on unconventional and high-risk research funding. He has secured substantial grants through his roles on EPSRC Council and RCUK Digital Economy programme. His commercial ventures include Hare Analytics, GTTA Limited, and CountingLab, demonstrating strong industry-academia knowledge exchange. He directs the Oxford Centre for Industrial and Applied Mathematics, overseeing collaborations between academic researchers and industry partners in energy, telecommunications, and digital marketing sectors.

• Quantum Technology
• Physics Education
• Semiconductor Devices
• +۳ مورد دیگر

I. Ercan is a Research Fellow at the Department of Electrical Engineering Education, Delft University of Technology. Their work bridges quantum technologies and physics education, focusing on innovative pedagogical approaches and semiconductor device modeling. University: Delft University of Technology Department: Electrical Engineering Education Research interests span quantum technology education, thermodynamic modeling of nanoscale devices, and the intersection of physics education with computational architectures. Key contributions include developing interactive tools for quantum outreach and analyzing efficiency limits in electron transport systems. Recent publications highlight their interdisciplinary approach: Quantum games and interactive Tools (2022) for educational outreach, Single-Electron-Transistor Compact Model (2023) for quantum hardware, and thermodynamic analyses of neural networks and Brownian circuits. Scientific Awards: Delft Education Fellowship (2023) DEWIS Advanced Scientist Award (2025) Ercan engages in public outreach initiatives, including STEM education programs for socioeconomically disadvantaged youth and diversity advocacy in academia.

• Management Accounting
• Value Chain Management
• Operations Management
• +۵ مورد دیگر

Peter Letmathe is a Professor and Chair of Management Accounting at RWTH Aachen University's Faculty of Business and Economics, where he has led the department since October 2011 and served as dean from 2018 to 2021. His academic journey includes professorships at University of Bayreuth and University of Siegen, where he founded the Small and Medium Sized Enterprise Institute Siegen in 2003. He maintains visiting professorships at University of Colorado at Boulder and Clemson University since 1999. His research interests focus on Behavioral Management Accounting, Value Chain Management, Business Modeling, and Managerial Accounting and Innovation Management. His interdisciplinary work spans the mobility sector, energy transformation, digitization of industry, and information and communication technologies, with particular emphasis on sustainability applications. His recent publications demonstrate a strong focus on decarbonization strategies, electric mobility economics, and sustainable operations management, reflecting current global challenges in energy transition and environmental sustainability. Professor Letmathe received the prestigious EURO award for the Best EJOR Paper 2013 (Innovative Applications of OR) for his work on emissions trading. He has been actively involved in numerous interdisciplinary research projects with industry partners, contributing practical insights to his academic work. His leadership extends to scientific boards including the FOKOS-Institute, the Gesellschaft für Operations Research (GOR e.V.), and BAUM e.V., demonstrating his influence in both academic and professional spheres. With over 80 interdisciplinary research projects and extensive publications in top journals including Journal of Operations Management, European Journal of Operational Research, and International Journal of Production Economics, Professor Letmathe has established himself as a leading scholar in management accounting with strong connections to sustainability and operational excellence.

• Remote Sensing
• Machine Learning
• Biohybrid Systems
• +۴ مورد دیگر

Professor Vladimir Risojević is a full professor at the Department of General Electrical Engineering, Faculty of Electrical Engineering, University of Banja Luka, Republic of Srpska, Bosnia and Herzegovina. With a prolific research career spanning over two decades, he has established himself as a leading expert in remote sensing, machine learning, and biohybrid systems. His work bridges theoretical advancements with practical applications in environmental monitoring, energy systems, and security technologies, with numerous publications in high-impact journals and conferences. Professor Risojević's research focuses primarily on remote sensing image classification , where he has made significant contributions to understanding the role of pre-training in specialized applications. His work in machine learning spans self-supervised learning, contrastive multiview coding, and efficient neural network architectures. Most notably, his pioneering research in biohybrid systems has developed innovative methods using honeybees as biosensors for landmine and explosive detection, creating a unique intersection between biology and engineering that has received international recognition. His research consistently demonstrates a commitment to solving real-world problems with practical engineering solutions. Analysis of his recent publications reveals a strategic expansion from his core expertise in remote sensing into complementary domains including energy systems (solar irradiance modeling and Li-ion battery monitoring), 3D human body modeling, and novel neural network architectures. A unifying theme throughout his work is the pursuit of computational efficiency, with multiple publications focusing on approximate computing techniques to make AI systems more energy-efficient for deployment in resource-constrained environments. His research demonstrates both depth in specialized areas and breadth across multiple engineering disciplines. Professor Risojević is actively involved in numerous significant research projects including: NATO Science for Peace and Security project valued at €300,415 on 'Biological Methods (Bees) for Explosive Detection' 'Obrada signala primjenom ugradjenih racunarskih sistema i masinskog ucenja' (Signal Processing using Embedded Computer Systems and Machine Learning) 'Masinsko ucenje u rubnom racunarstvu' (Machine Learning in Edge Computing) 'Elektronski sistem za daljinsko pracenje i analizu uticaja parametara zivotne sredine na aktivnost pcela' (Electronic System for Remote Monitoring of Environmental Parameters on Bee Activity) His laboratory has developed specialized video analysis systems for bee monitoring, with multiple publications detailing techniques for tracking bee activity, detecting pollen-bearing bees, and creating integrated sensor platforms for remote bee yard monitoring. This work has positioned him as a leader in applying computer vision techniques to biological monitoring systems with applications in both environmental science and security technologies.

Bert Kappenمشاهده پروفایل

استاد

• Quantum Machine Learning
• Stochastic Control Theory
• Neural Networks
• +۴ مورد دیگر

Bert Kappen is a Professor of Physics at Radboud University Nijmegen, affiliated with the Department of Biophysics and the Donders Center for Neuroscience. His research focuses on the intersection of physics, machine learning, and neuroscience, with emphasis on quantum machine learning, stochastic control theory, and Bayesian inference methods applied to neural systems. His core research integrates statistical physics and quantum mechanics to develop computational methods for AI, exploring how intelligence emerges in biological systems. Key areas include: Quantum Machine Learning : Developing quantum algorithms for Boltzmann machines and exploring quantum advantage in optimization Path Integral Control : Creating efficient solutions for stochastic optimal control problems in robotics and neuroscience Bayesian Inference : Building probabilistic models for medical diagnosis and DNA identification (e.g., Bonaparte system used by Interpol) Atomic-scale Neural Networks : Implementing neuromorphic computing using nanoscale atomic switches Kappen leads an active research group with multiple PhD and Master's students, working on projects ranging from quantum perceptrons to multi-agent UAV control. His publication output shows strong recent focus on quantum computing applications (2020-2025), atomic-scale machine learning implementations, and advanced control theory. He teaches courses including Introduction to Machine Learning , Advanced Computational Neuroscience , and Statistical Machine Learning at Radboud University. Externally funded projects include EU FP7 initiatives (NETT, CompLACS), NWA Quantum Learning, and collaborations with Thales Nederland.

Bikas Chandra Dasمشاهده پروفایل

دانشیار

• Condensed Matter Physics
• Materials Engineering and Device Physics
• Organic Memory Devices
• +۶ مورد دیگر

Dr. Bikas Chandra Das is an Associate Professor at the School of Physics , Indian Institute of Science Education and Research, Thiruvananthapuram (IISER TVM), India, since Dec 2022. Previously, he served as Assistant Professor (2015-2022) and held postdoctoral positions at Pennsylvania State University (2014) and University of Alberta (2012). He earned his Ph.D. in Physics (2011) from Jadavpur University (thesis: Electrical bistability in organic-nanomaterial composites ), M.Sc. (2005) and B.Sc. (Hons.) (2003) in Physics from University of Calcutta. Research Focus : Condensed Matter Physics, Materials Engineering, and Device Physics with emphasis on 2D materials , memristors , neuromorphic computing , organic-inorganic hybrids , and low-voltage electronics . Key Publications : 61 journal articles (h-index: 16), including groundbreaking work on MXene memristors, perovskite photodetectors, and iontronic devices, as well as projects on 2D transition metal dichalcogenides and bioinspired computing. Grants & Projects : Principal Investigator for SERB (₹23.1 lakh), UGC (₹16.46 lakh), and FIST-DST (₹2.64 crore) funded initiatives. Co-PI for DST oxide semiconductor project (₹5.45 lakh). Scientific Recognition : IOP Trusted Reviewer (2023) SERB Early Career Research Award (2017) CSIR Senior/Junior Research Fellow (2008/2006)