Find Academic Supervisors

• Machine Learning
• Internet of Things
• Biomedical Engineering
• +7 more

Md. Mehedi Hasan is a researcher at Daffodil International University's Department of Computer Science and Engineering. With over 17 years of research activity, his work spans multiple domains including Internet of Things (IoT), machine learning, biomedical informatics, and environmental technology. Current affiliations include collaborations with institutions in Bangladesh, Japan, and Malaysia. Research areas: Machine Learning, IoT Security, Medical Imaging, Aquaculture Monitoring Key technologies: Deep Learning, Time Sensitive Networking, Kerberos Authentication Notable contributions: NAND Flash memory analysis, Pediatric health applications, Urbanization impact studies His 2025 publications focus on medical data preservation ( Comput. Biol. Medicine ), pedestrian risk assessment ( Complexity ), and toddler screen time management ( SoftwareX ). Recent work explores the intersection of environmental analysis with deep learning and cryptographic solutions for smart infrastructure. Scientific contributions include: Radiation tolerance analysis of neuromorphic systems (IRPS 2020) Smart meter security protocols (Earth Sci. Informatics 2024) Acoustic breathing phase detection (Proc. ACM IMWUT 2021)

Elmar RückertView profile

Professor

• Robotics
• Machine Learning
• Industrial Automation
• +3 more

Elmar Rückert is a Professor at the Chair of Cyber Physical Systems at Karlsruhe Institute of Technology. His work focuses on robotics, machine learning, and industrial automation with applications in environmental science and data-driven systems. Recent activities include invited talks on topics like Bayesian Optimization for control systems and deep learning in robotics. Research interests span tactile robot learning, privacy-aware AI, and sensor fusion for autonomous systems. He has published extensively in venues like IEEE International Conference on Robotics and AAAI Conference on Artificial Intelligence, with a strong emphasis on practical applications in industrial processes and environmental monitoring. Key contributions include the EnvoDat dataset for robotic spatial reasoning and methods for skill disentanglement in RKHS. His collaborative work extends to material science and environmental chemistry through partnerships on technology-critical element analysis.

• Artificial Vision
• CMOS Technology
• Image Recognition
• +3 more

Diego Cabello Ferrer is a Professor at the University of Santiago de Compostela’s Department of Electronics and Computing under the Faculty of Physics. His research focuses on advanced CMOS technologies, embedded vision systems, energy harvesting, and biomedical devices. He holds a PhD from the same university (1984), specializing in cybernetic systems and microprocessor-based analysis of animal behavior. Education: PhD in Electronics and Computing, University of Santiago de Compostela (1984). His work spans artificial vision algorithms, low-power CMOS sensor design, and micro-energy harvesting systems. Key contributions include vision sensors with in-pixel computation, ultra-low power voltage regulators for biomedical implants, and solar energy harvesting integrated circuits. His research emphasizes real-time image processing, edge computing for embedded systems, and hardware acceleration for neural networks. Publications highlight innovations in CMOS vision sensors for background subtraction, energy-efficient LDO designs, and hyperdimensional computing architectures. His teams have developed perpetual wireless sensor networks for environmental monitoring and Snail pest detection. Current efforts focus on in-memory computing with FeFETs and SRAM-based neural network accelerators. Labs/Teams: Center for Research in Intelligent Technologies (CITIUS). Grants & Collaborations: Projects include iCaveats (embedded vision architectures) and energy harvesting integration in biomedical devices. His work bridges theoretical signal processing with practical hardware implementations, addressing challenges in power efficiency, miniaturization, and real-time performance for embedded vision systems.

Pietro PeronaView profile

Professor

• Machine Learning
• Computer Vision
• Signal Processing
• +5 more

Pietro Perona is the Allen E. Puckett Professor of Electrical Engineering at the California Institute of Technology (Caltech). He holds a D.Eng. from the University of Padua (1985) and a Ph.D. from UC Berkeley (1990). His academic roles include serving as Director of the Center for Neuromorphic Systems Engineering (1999–2004) and Executive Officer for Electrical Engineering (2006–2010). His research focuses on computational vision, visual recognition, and machine learning. Key projects include the Visipedia initiative (smart apps for species identification like iNaturalist and Merlin Bird ID) and collaborations on neuroethology with Professors Anderson and Dickinson, studying fruit fly and mouse behavior. He also explores human visual perception and crowd-sourced learning. Perona has been recognized as an IEEE Fellow and leads the Caltech Vision Lab, which develops machine vision systems for applications in conservation, robotics, and neuroscience. His lab’s work spans foundational research to real-world tools, including datasets like the Caltech Fish Counting Dataset and MABe22 benchmarks. He teaches courses on computational vision and has advised numerous projects in AI and robotics. Current collaborations include partnerships with Amazon Web Services (AWS) and Disney on AI-driven solutions.

• Medical Imaging
• Machine Learning
• Computer Vision
• +3 more

Aggelos K Katsaggelos is a Professor at Northwestern University, holding roles in both the McCormick School of Engineering and the Department of Radiology (Basic and Translational Radiology Research). His research spans medical imaging, machine learning, computer vision, semiconductor materials, and astrophysics simulations. Key contributions include advancements in deep learning for medical diagnostics, semiconductor detector characterization, and computational astrophysics. He is affiliated with the Northwestern University Clinical and Translational Sciences Institute (NUCATS). Research interests include AI-driven medical imaging analysis, signal processing for healthcare applications, and semiconductor physics. Notable projects include work on POSYDON for binary star simulations and innovations in GAN-based image super-resolution. He has authored over 350 publications, focusing on interdisciplinary applications of machine learning in healthcare and engineering. No scientific awards are explicitly listed, but his extensive publication record reflects significant academic contributions. External professional relationships for 2024 were none, indicating focused academic engagement. Advising and grants details are not specified in the provided texts.

Qingxue ZhangView profile

Associate Professor

• AI-Chip-Sensor Co-design
• Precision Medicine
• Smart World
• +7 more

Qingxue Zhang is an Associate Professor in the Department of Electrical and Computer Engineering at the College of Engineering, Temple University. He brings over two decades of experience from academia, industry, and medical research, including postdoctoral work at Harvard University and leadership roles as a system architect and team manager in high-tech R&D. His interdisciplinary research bridges AI, chip design, and sensor technology for applications in precision medicine and smart environments. His research interests include: AI - Brain-inspired AI - Theories and Algorithms Chip - Brain-inspired, MPSoC, ASIC Sensor - Wearable, Embedded, IoT Big Data Precision Medicine Smart World Dr. Zhang's work is driven by the vision of AI-Chip-Sensor co-design, enabling next-generation intelligent systems. His research has been funded by major agencies including NSF CISE and NSF ENG, reflecting its innovation and impact. Although specific publications are not listed, his contributions span over 50 journals, conferences, and patents, indicating a strong and sustained output in these domains. He has received several honors, including: National Science Foundation CAREER Award Best Paper Award Favorite Professor Award Dr. Zhang is actively involved in academic service, serving as an Associate Editor for multiple IEEE journals and as a reviewer for top-tier publications including Nature Scientific Reports and various IEEE Transactions . He has also reviewed grants for approximately 20 NSF, NIH, and NIST panels. He has chaired or served on committees for leading conferences such as IEEE ICCE, IEEE IPCCC, and DAC. He leads the UbiEi Lab, fostering innovation and training the next generation of engineers and researchers.

Ulrich RückertView profile

Professor

• Neuromorphic Computing
• Embedded Systems
• Machine Learning
• +9 more

Prof. Dr.-Ing. Ulrich Rückert is a Professor at the Faculty of Engineering of the University of Bielefeld , where he leads the Cognitronics & Sensor Technology Group and participates in CITEC (Center for Cognitive Interaction Technology). He serves as Vice Rector for Digitalization and Data Infrastructure , driving university-level digital transformation initiatives. Research Focus : Neuromorphic computing, spiking neural networks (SNNs), embedded systems, robotics, UWB localization, and reconfigurable hardware. Projects : Leading federal and EU-funded initiatives like eProcessor (RISC-V multi-core systems), VEDLIoT (efficient deep learning in IoT), and Al4DG (AI in distribution grid control). Teaching & Leadership : Academic advisor for the Master in Biomechatronics , chairs examination boards, and leads the Library Commission . His work integrates neuromorphic hardware with edge computing and real-time systems , supported by grants from the European Union and German Federal Government . Recent publications analyze FPGA-based SNNs , UWB localization , and resource-efficient embedded architectures . Scientific Contributions : Over 200 publications in robotics, neural networks, and hardware-software co-design. Notable collaborations with institutions in Germany, Switzerland, and Italy.

• Weigh-in-Motion Systems
• Sensor Fusion
• Transportation Metrology
• +3 more

Ryszard Sroka serves as Professor and Head of the Department of Metrology and Electronics at AGH University of Science and Technology's Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering in Kraków, Poland. His leadership extends to university committees including the Discipline Council for Automation, Electronics, Electrical Engineering and Space Technologies and the Rector's Finance Committee. His research spans transportation metrology and biomedical sensor systems, with primary expertise in weigh-in-motion (WIM) technology for vehicle mass enforcement. Key focus areas include dynamic weighing accuracy assessment, sensor fusion for traffic monitoring, calibration methodologies for WIM systems, and innovative biomedical applications such as autoclave sterilization IoT systems and multimodal thermographic wound imaging. His work bridges theoretical metrology with practical engineering solutions for road safety and healthcare diagnostics. Analysis of his 15 most recent publications (2021-2025) reveals a strategic evolution from traditional WIM system optimization toward interdisciplinary applications. While 70% of recent work advances high-accuracy enforcement systems and sensor longevity studies, 30% explores biomedical frontiers including steam sterilization monitoring and surgical diagnostic imaging. This dual-track approach demonstrates significant methodological transfer between transportation and medical sensor domains. As Department Head, he oversees metrology and electronics research initiatives with emphasis on real-world implementation. His team maintains active development of traffic parameter measurement systems and biomedical sensor networks, positioning the department at the intersection of measurement science and industrial application.

• Robotics
• Artificial Intelligence
• Computational Neuroscience
• +7 more

Robin Dietrich is a Researcher at the Department of Informatics 6 - Chair of Robotics, Artificial Intelligence and Real-time Systems at the Technical University of Munich . His work bridges computational neuroscience and robotics, focusing on translating neural mechanisms from mammalian brains into algorithms for mobile robot navigation. B.Sc. and M.Sc. in Computer Science Research on hippocampal temporal dynamics for neuromorphic SLAM Specializes in spiking neural networks for navigation and radar processing Research Interests : Robin's research explores the intersection of robotics, artificial intelligence, and computational neuroscience . His work specifically investigates spiking neural networks, FMCW radar data processing, and neuromorphic algorithms for autonomous systems. Recent projects focus on uncertainty quantification, evolutionary optimization, and multi-robot exploration using biologically inspired models. Publication Trends : Robin's publications (2019-2025) demonstrate a consistent focus on neuromorphic computing for robotic perception , with increasing specialization in spiking neural networks for radar processing and biologically inspired navigation algorithms . Key collaborations include contributions to multi-robot exploration metrics and hardware acceleration frameworks. Teaching Contributions : Robin has taught Digital Signal Processing and Real-Time Systems lectures since 2019, co-led seminars on Bio-inspired Data Processing , and supervised practical courses on Intelligent Mobile Robots using ROS.

Jose A. BoludaView profile

Associate Professor

• Computer Vision
• Hardware Design
• Image Processing
• +4 more

Dr. Jose A. Boluda is an Associate Professor in the Department of Computer Engineering at the University of Valencia's School of Engineering. He holds a B.S. in Physics (Electricity, Electronics and Computer Science, 1992) and a Ph.D. (2000) from the same institution. With 6 teaching quinquenios and 4 research sexenios, his academic career spans over two decades. His research focuses on: Computer vision hardware design Smart vision sensors and neuromorphic computing Reconfigurable architectures (FPGA) High-speed motion analysis and change-driven processing Biomimetic visual sensing strategies He has authored over 50 international publications and participated in 15 research projects. His publications predominantly explore: Event-based vision sensors Hardware-accelerated image processing Neuromorphic engineering applications Real-time systems for robotics and 3D scanning Low-power circuit design for computer vision Awards include: Fernando Sapiña Award 2023 for Valencian teaching materials Fernando Sapiña Award 2021 for English teaching materials Fernando Sapiña Award 2021 for Valencian teaching materials He has directed 2 doctoral theses and supervised multiple master's projects. Research includes international collaborations at the University of Virginia, University of Macedonia, and Universitat Jaume I. His lab focuses on developing novel vision sensors and processing architectures.

Adrien F. VincentView profile

Associate Professor

• Neuromorphic Computing
• Spiking Neural Networks
• Memristive Devices
• +2 more

Adrien F. Vincent is an Associate Professor at IMS Bordeaux (Laboratory of Integration, Material to System) under Université de Bordeaux, Institut Polytechnique de Bordeaux, and CNRS. He leads research in neuromorphic computing with a focus on spiking neural networks and memristive devices. His team, 2HC Production Engineering, develops energy-efficient hardware for real-time event-based data processing. Key research areas: Neuromorphic systems, Low-power electronics, Memristor technology Recent publications explore spintronic neural networks, STDP plasticity, and energy optimization His work addresses hardware-friendly learning algorithms and co-integration of analog silicon neurons with memristive arrays. Projects include ULPEC (Ultra-Low Power Event-Based Camera) and MIRA2015 (Memristive Architectures).

John Morgan SampsonView profile

Associate Professor

• Energy Efficient Computing
• Energy Harvesting
• Dark Silicon
• +6 more

John Morgan Sampson is an Associate Professor in the Department of Computer Science and Engineering. His research focuses on energy-efficient computing, neuromorphic systems, and memory safety. Active in energy harvesting systems and in-memory computing Specializes in dark silicon, non-volatile memory, and deep neural networks Collaborates on projects related to distributed sensor networks and secure architectures Research Trends: Recent articles highlight advancements in energy-efficient computer architecture , memory safety validation , and neuromorphic computing . Key subfields include spiking neural networks , edge-cloud partitioning , and heap memory protection .

• Virtual Reality
• Augmented Reality
• Computer Vision
• +4 more

Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.

• Artificial Intelligence
• Medical Robotics
• Embedded Systems
• +5 more

Jan-Matthias Braun is an Associate Professor at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark, specializing in Applied AI and Data Science. His research bridges artificial intelligence, robotics, and medical device engineering. Current projects focus on explainable AI integration in colon capsule endoscopy Development of real-time FPGA-based systems for colorectal diagnostics Biomechanical modeling for adaptive orthotic devices His work emphasizes cross-disciplinary applications of machine learning in healthcare, particularly for gastrointestinal disease detection and assistive robotics. Publications demonstrate expertise in deep neural networks, hardware acceleration, and smart environment control systems. Teaching responsibilities include: Advanced cybersecurity courses Deep learning applications in epilepsy detection Mentorship in capsule endoscopy image analysis

Mohsen KaboliView profile

Assistant Professor

• Embodied Robotics
• Tactile Intelligence
• Machine Learning
• +3 more

Mohsen Kaboli is an Assistant Professor (Part-time) at Eindhoven University of Technology (TU/e) in the Netherlands, affiliated with the Department of Electrical Engineering and the Electronic Systems group. Since 2018, he has served as head of the AI, Robotics & Cognitive Vehicle research lab at BMW Group’s Center of Invention in Munich, Germany. Previously, he held positions at Radboud University’s Institute for Brain and Cognition (2019-2022) and Technical University of Munich (TUM) as a postdoctoral research fellow. His research focuses on Embodied Robotics, Visuo-Tactile Interactive Perception, and Machine Learning, with applications in Robotic Grasping, Human-Robot Collaboration, and Neuromorphic Computing. He leads European-funded projects like PHASTRAC (Oscillatory Neural Networks for AI Edge Computing) and INTUITIVE (Tactile User Interfaces). Keywords: Tactile Intelligence, AI, Robotics Application Domains: Mobile Robotics, Medical Instrumentation, Neuroprosthetics His recent publications (e.g., ViTract, Shared Visuo-Tactile Perception) emphasize robust object pose estimation and shape reconstruction using Bayesian filtering and Graph Neural Networks. He has authored ~40 academic works and contributed to 20 patents. Scientific Awards : IEEE Senior Member (2018) Georges Giralt Ph.D. Award (Finalist) IEEE ICRA Outstanding Paper Award (Finalist, 2023) IEEE FLEPS Outstanding Student Award (Winner, 2022) IEEE ROSE Outstanding Paper Award (Winner, 2024) Mohsen Kaboli serves as Editor/Associate Editor for IEEE ICRA, IROS, R-AL, T-RO, and IJRR. His work bridges tactile perception, AI, and robotics, with impacts in wearables, medical devices, and autonomous systems.