Find Academic Supervisors

• Tactile Sensing
• Robotics
• Pattern Recognition
• +5 more

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
• +5 more

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
• +3 more

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.

• Fiber Photonics
• Quantum Communications
• Neuromorphic Computing
• +3 more

Bennet Fischer is a Research Scientist at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) working in the Research Department Fiber Photonics within the Hybrid Fibers work group. His research focuses on advanced photonic technologies with applications ranging from quantum communications to neuromorphic computing. Dr. Fischer's research interests span fiber photonics, quantum communications, and optical computing technologies. His work explores innovative approaches to light manipulation using fiber optics, including broadband frequency generation through soliton fission, high-dimensional quantum key distribution, and genetic algorithm optimization of photonic structures. His research bridges fundamental photonics with practical applications in secure communications, energy-efficient computing, and optical sensing systems. Analysis of Dr. Fischer's recent publications reveals a strong focus on developing fiber-based systems for quantum information processing, neuromorphic computing, and optical communications. His work frequently combines advanced fiber optics with integrated photonic platforms, demonstrating expertise in both theoretical modeling and experimental implementation. The research shows a clear trajectory toward developing practical, energy-efficient photonic systems that could enable next-generation computing and communication technologies. Dr. Fischer is actively involved in collaborative research within the Hybrid Fibers work group at Leibniz-IPHT, contributing to projects that integrate fiber photonics with quantum technologies and neuromorphic computing approaches. His work demonstrates strong international collaboration, particularly with researchers in Canada, Australia, and other European institutions.

• Computational Mechanics
• Viscoplasticity
• Neural Networks in Engineering
• +6 more

Associate Professor Dr. Marcus Stoffel is a faculty member at RWTH Aachen University, serving within the Faculty of Mechanical Engineering as head of the Chair and Institute of General Mechanics. His research integrates advanced computational mechanics with cutting-edge machine-learning techniques, focusing on viscoplastic material modeling, neural network surrogates, biomechanical systems, and neuromorphic computing. Education: While explicit degrees are not listed in the text, his title of Dr. and rank of Associate Professor at a leading German technical university imply doctoral and post-doctoral qualifications in mechanical engineering or a closely related discipline. Research Interests: Computational viscoplasticity and damage mechanics Physics-informed neural networks and spiking neural architectures Machine-learning-enhanced finite element methods Crashworthiness and structural optimization Biomechanics of musculoskeletal tissues and medical devices Sustainable neuromorphic computing for engineering applications Publication Trends: Across 2023–2025, Stoffel’s articles reveal a rapid pivot toward AI-accelerated engineering, coupling neuromorphic processors with traditional solid-mechanics problems such as viscoplastic beam response and membrane instabilities. Recurrent themes include surrogate modeling via spiking neural networks, generative learning for design optimization, and data-driven biomechanical analyses of spinal instrumentation and cartilage. Scientific Awards: No specific awards are mentioned in the provided text. Funding & Teams: While individual grants are not detailed, the extensive publication output and involvement in interdisciplinary projects (e.g., neuromorphic computing, biomechanics, crashworthiness) indicate robust external funding. He leads the Institute of General Mechanics, supervising a research group that bridges theoretical mechanics, numerical methods, and experimental biomechanics. Laboratories & Facilities: Stoffel’s team operates within the Institute of General Mechanics at RWTH Aachen, equipped with advanced finite-element computing resources, experimental biomechanical rigs (e.g., spine test rigs and bioreactors), and neuromorphic hardware platforms for sustainable AI research.

Arturo Morgado EstevezView profile

Professor

• Neuromorphic Engineering
• Robotics
• Computer Architecture
• +5 more

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
• +5 more

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
• +3 more

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 PlestedView profile

Academic

• Deep learning
• Artificial intelligence
• Machine learning
• +4 more

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
• +5 more

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
• +5 more

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 HaywardView profile

Professor

• Nano-scale Magnetism
• Spintronics
• Neuromorphic Computing
• +3 more

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 MarinisView profile

Assistant Professor

• Photonic Neuromorphic Computing
• Photonic Integrated Circuits (PIC)
• Electro-Optic Co-Design
• +4 more

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
• +5 more

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
• +7 more

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.