جست‌وجوی استاد و پژوهشگر

Ziad Kobtiمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۶ مورد دیگر

Ziad Kobti is a Professor and Director of the School of Computer Science at the University of Windsor. His work focuses on advancing artificial intelligence, machine learning, and data science with applications in healthcare, cybersecurity, and social network analysis. He leads initiatives such as the Graph Neural Network Lab and collaborates with industry partners like Sterling Information Technologies to foster cybersecurity talent. Kobti has organized programming competitions, co-op education programs, and research workshops, emphasizing experiential learning for students. His research spans recommendation systems, graph neural networks, pandemic modeling, and medical diagnostics, with a focus on impactful real-world solutions. He is involved in academic leadership, program development, and community outreach through events like Computer Science Demo Day. Research interests include: Graph Neural Networks and Dynamic Link Prediction Recommender Systems and Anomaly Detection Social Network Analysis and Team Formation Healthcare Informatics and Medical Imaging Cybersecurity and Privacy Risk Analysis Machine Learning for Public Health and Pandemic Preparedness Recent projects include collaborations on AI-driven crop disease detection, electric vehicle research, and palliative care simulation frameworks. He has developed frameworks like UDIS and BeComE, and contributes to open-source tools for social network analysis and anomaly detection. His work integrates multi-disciplinary approaches, combining computational models with domain-specific expertise in healthcare, agriculture, and social systems.

• Hypertrophic Cardiomyopathy
• Myocardial Disease
• Genetics of Heart Disease
• +۳ مورد دیگر

Dr. Michelle Michels is a Researcher in the Department of Cardiology at Erasmus University Medical Center (Erasmus MC), Rotterdam, the Netherlands, specializing in inherited cardiac conditions with 191 research outputs including 181 articles. Her work centers on hypertrophic cardiomyopathy and related genetic heart diseases within Europe's leading cardiovascular research framework. Her research spans hypertrophic cardiomyopathy , genetic myocardial disorders , and cardiac imaging , investigating genotype-negative disease expression, arrhythmia risks in RBM20-associated cases, and physical activity impacts through accelerometry. Current projects include the ENERGY clinical trial on trimetazidine therapy and European Reference Network initiatives for rare cardiomyopathy management, emphasizing multidisciplinary collaboration across 12 international profiles. Recent publications reveal strong trends toward translational research integrating genetic profiling with clinical outcomes, particularly in pre-symptomatic disease stages and quality-of-life interventions. Her work consistently addresses therapeutic gaps in inherited heart conditions through multicenter cohort studies and randomized trials. Scientific awards: No specific awards were documented in the source material. Dr. Michels has supervised 4 students per institutional records and participates in major European collaborative networks including the European Reference Network for rare heart diseases. Her grant-funded work focuses on clinical trials like ENERGY and multinational studies on transition care for inherited cardiomyopathies. Embedded within Erasmus MC's Cardiology department—a hub for genetic heart disease research—she collaborates extensively across European institutions as evidenced by co-authorship in high-impact journals and network visualizations showing country-level research partnerships.

• Robotics
• Artificial Intelligence
• Medical Imaging
• +۵ مورد دیگر

Dr Shahnewaz Ali is a Postdoctoral Fellow in the Faculty of Engineering's School of Electrical Engineering & Robotics at Queensland University of Technology (QUT). He is affiliated with the Centre for Robotics and holds a PhD from QUT and an MSc in Computer Engineering from Politecnico di Milano. His research focuses on the intersection of robotics, artificial intelligence, and biomedical applications, emphasizing wearable technology, surgical robotics, and sensor systems. Key research interests include AI-driven medical imaging, robotic surgery systems, and biomarker detection using wearable devices. His work spans sensor technologies for robotics, real-time surgical scene analysis, and predictive performance monitoring. Recent projects include developing a wearable monitoring system for stress biomarkers and advancing arthroscopic surgical techniques through 3D mapping and deep learning. Education: PhD, Queensland University of Technology MSc (Computer Engineering), Politecnico di Milano Dr Ali's publications cover topics like surgical scene restoration, microRNA-based performance prediction, and sensor development for medical robotics. His research bridges engineering and healthcare, aiming to enhance diagnostic accuracy and robotic surgical precision. Collaborations include multidisciplinary teams at QUT and international institutions.

• Optical Fibers
• Photonics
• Materials Science
• +۷ مورد دیگر

Professor Heike Ebendorff-Heidepriem is a Professor in the School of Physics, Chemistry and Earth Sciences at the University of Adelaide. She serves as Deputy Director of the Institute for Photonics and Advanced Sensing (IPAS) and Director of the Optofab Adelaide Hub within the Australian National Fabrication Facility (ANFF). Her research focuses on advanced optical fiber fabrication, materials science, and photonics applications, including high-precision glass extrusion, exposed-core fibers, and quantum sensing. She leads a multidisciplinary team of over 20 researchers and has secured >$20M in funding, collaborating with global industry and academic partners. Her work spans fiber lasers, mid-infrared photonics, and sensor technologies, with significant contributions to nanomaterial integration and plasmonic processes. She has supervised 12 PhD and 5 HDR students over 15 years, emphasizing mentorship in photonics and materials innovation. Research Interests: Professor Ebendorff-Heidepriem specializes in developing novel optical fibers and glasses for applications such as high-power laser delivery, nonlinear optics, and evanescent field sensing. Her team pioneers fabrication techniques like billet extrusion and ultrasonic milling, enabling structures like suspended nanowire-core fibers and diamond-doped sensors. Key areas include mid-infrared materials (tellurite, fluoride glasses), plasmonic nanoparticle embedded glasses, and portable sensing solutions for mining and biomedical fields. Labs/Teams: IPAS and Optofab Adelaide Hub provide advanced facilities for fiber fabrication, laser development, and materials characterization. Her collaborations include 82 university groups and 30 industry/defense partners, driving commercialization of technologies like gold nanoparticle-based colored glass and diamond-doped fiber sensors.

• Artificial Intelligence
• Machine Learning
• Internet of Things
• +۱۳ مورد دیگر

Edoardo Patti is an Associate Professor in the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino, Italy. He is actively involved in research and teaching, focusing on artificial intelligence, machine learning, IoT, smart grids, energy communities, co-simulation, and digital twin technologies. He is a member of the EDA (Electronic Design Automation) research group and the Interdepartmental Center IAM@PoliTo for Integrated Additive Manufacturing. His research interests span Artificial Intelligence, Machine Learning, Internet of Things, Smart Grids, Energy Communities, Co-simulation, Distributed Software Infrastructure, Digital Twin, Physics-Informed Neural Networks, and Reinforcement Learning . His work is closely aligned with Sustainable Development Goals such as Affordable and Clean Energy (Goal 7), Sustainable Cities (Goal 11), and Climate Action (Goal 13). The latest publications reveal a strong trend in AI-driven modeling for energy systems, including digital twins for wave energy converters, urban electric mobility simulation, real-time battery health estimation, and co-simulation platforms for integrated energy systems. His work combines machine learning with physical models and large-scale distributed systems to address sustainability challenges. He supervises several PhD students, including Maria Adelaide Loffa, Matia Torlini, Rafael Natalio Fontana Crespo, Pietro Rando Mazzarino, Claudia De Vizia, and Marco Massano. He has led numerous research projects funded by EU (H2020), PNRR, regional initiatives, and corporate contracts, particularly in additive manufacturing, smart grids, and IoT platforms. He is involved in multiple research groups and centers: EDA - Electronic Design Automation (DAUIN) Interdepartmental Center IAM@PoliTo - Integrated Additive Manufacturing

Tadeusz Więckowskiمشاهده پروفایل

استاد

• Electromagnetic Compatibility
• Antenna Theory
• Radio Wave Propagation
• +۶ مورد دیگر

Tadeusz Więckowski is a Professor and Head of the Department of Telecommunications and Teleinformatics at the Faculty of Information and Communication Technology, Wrocław University of Science and Technology. His work bridges advanced electromagnetic theory with practical telecommunications systems. His research interests include: Electromagnetic Compatibility (EMC) of devices and systems Antenna theory and design Radio wave propagation Radiocommunication systems Gyrotron and microwave source technologies Testing methodologies for EMC infrastructure The recent publications highlight a strong focus on high-frequency electromagnetic systems, particularly gyrotrons for spectroscopy and electromagnetic compatibility testing. His work spans both theoretical modeling and experimental validation, with applications in telecommunications, broadcasting (e.g., DAB), and scientific instrumentation. There is also engagement with modern topics such as IoT testing, intrusion detection, and neural networks for document verification, indicating interdisciplinary reach. Tadeusz Więckowski leads the Department of Telecommunications and Teleinformatics, which is involved in the operation of the Laboratory for Electromagnetic Compatibility. This infrastructure supports advanced research in signal integrity and system compatibility.

Francesco Picarielloمشاهده پروفایل

پژوهشگر

• Electrical and Electronic Measurements
• Data Acquisition Systems
• Compressive Sampling
• +۷ مورد دیگر

Francesco Picariello is a Researcher at the Department of Engineering (DING) at the University of Sannio, specializing in Electrical and Electronic Measurements (ING-INF/07). His academic profile shows extensive expertise in measurement systems, data acquisition, and signal processing with applications across multiple domains including biomedical engineering, structural health monitoring, and wireless communications. His research interests focus on the design, implementation, and metrological characterization of mobile measurement systems, wireless sensor networks, data acquisition systems based on compressed sensing, signal processing, and image processing. These research activities have been applied to intelligent transportation systems, aerial photogrammetry using UAVs, indoor navigation, motion tracking, power consumption analysis of processors, wideband data acquisition systems, and physiological monitoring applications. Dr. Picariello's work demonstrates significant trends in compressive sampling techniques, with numerous publications on ECG signal processing, RF emitter localization, and structural health monitoring systems. His research shows a clear progression toward IoT integration, wearable medical devices, and security applications for communication channels. His scientific collaborations include institutions such as the University of Naples 'Parthenope', Military University of Technology of Warsaw, National Institute of Standards and Technology (NIST), Keysight Laboratories, CERN Institute, and the University of Tokyo, reflecting the international recognition of his work. As an active researcher with publications spanning from 2016 to 2025, Dr. Picariello has made substantial contributions to measurement science, particularly in the development of novel techniques for data acquisition, signal processing, and system metrological characterization across multiple application domains.

Kok-Leong Ongمشاهده پروفایل

استاد

• Business Analytics
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Kok-Leong Ong is a Professor of Business Analytics at RMIT University's College of Business & Law, where he serves as Director of the CoBL Technology Initiative, Director of the Enterprise AI and Data Analytics Hub, and Head of the Department of Information Systems and Business Analytics. With over $1.4 million in research grants, he specializes in translating analytics and machine learning into practical business applications across multiple verticals including e-Commerce, public health, sports, urban studies, marketing, and learning. His research has consistently ranked in the top 25% and 5% of works in their domains according to Altmetric. Professor Ong's research spans Business Analytics, Artificial Intelligence, Machine Learning, and Information Systems, with a focus on making data actionable through analytics-2-business translation, automation, and applications. His work addresses critical challenges in cybersecurity for wearable health devices, ECG-based authentication systems, federated learning privacy, carbon accounting for maritime transport, and AI-driven solutions for vehicle damage detection. He has developed frameworks for operationalizing analytics in business contexts and has made significant contributions to mHealth applications for infant care and breastfeeding support. Among his notable scientific achievements, Professor Ong has received two VC's Teaching Awards and was named one of Australia's Leading Data Academics by CDO Magazine in 2021. He has secured over $1.4 million in research funding and serves on prestigious conferences including KDD and PAKDD. His research has been recognized for its high impact, with many works ranking in the top percentiles of their respective fields. Professor Ong actively supervises numerous research students across diverse topics including human-aligned AI, securing LLMs for financial applications, cyber risks in enterprise AI systems, ECG authentication security, and AI transformation for SMEs. He previously played a key role in establishing Australia's first Business Analytics degree and led La Trobe Business School's analytics program from 2015 to 2019 before joining RMIT in September 2021. Through the Enterprise AI and Data Analytics Hub and his role with RMIT University's Digital3 board, Professor Ong leads initiatives focused on bridging the gap between advanced analytics capabilities and business value creation. His work emphasizes practical implementation of AI and analytics solutions that address real-world challenges across multiple industry sectors.

• Tactile Sensors
• FPGA-Based Systems
• Robotics
• +۳ مورد دیگر

Óscar Oballe-Peinado is a professor at the Department of Electronics, School of Engineering, University of Málaga. His work focuses on tactile sensors, FPGA-based embedded systems, and robotics, particularly addressing crosstalk elimination, sensor calibration, and smart preprocessing techniques. He contributes to both academic research and educational innovations, including remote digital electronics laboratories. PhD in Electronics (University of Málaga, 2016) Active researcher in tactile sensor technology and FPGA implementations Supervisor of Raúl Lora Rivera's research on texture detection and compliance recognition His research explores advanced methods for tactile sensor optimization, including hysteresis correction, quantization error reduction, and compliance recognition algorithms. Recent publications (2023-2024) focus on smart tactile preprocessing and embedded FPGA implementations for robotics. The 2023-2024 articles emphasize his contributions to tactile sensor applications in robotic manipulation, texture detection, and compliance recognition using FPGA-based embedded systems. These works align with trends in smart sensor interfaces and real-time processing for robotics. Collaborated on improving remote digital electronics laboratories using Raspberry Pi 4 (2022) Developed FPGA-based tactile sensor suite electronics (2017)

Mai Buiمشاهده پروفایل

مدرس

• Computer Vision
• Medical Image Analysis
• Surgical Robotics
• +۵ مورد دیگر

Dr. Mai Bui is a Lecturer at the Deutsches Herzzentrum München (German Heart Center Munich), affiliated with the Chair of Computer Science Applications in Medicine under Prof. Nassir Navab. Her research focuses on integrating advanced computer vision and deep learning techniques into medical applications, particularly in surgical robotics, image-guided interventions, and medical augmented reality. She has contributed to critical areas like catheter tracking, pose estimation, and real-time medical imaging analysis. Dr. Bui teaches multiple courses including Computer Aided Medical Procedures , Medical Augmented Reality , and Introduction to Surgical Robotics . Her work emphasizes low-dose imaging solutions and robust navigation systems for minimally invasive procedures. She collaborates with interdisciplinary teams at labs such as DHM, IFL Lab, and NARVIS Lab to advance translational research in medical AI. Her recent publications highlight innovations in adversarial networks for pose refinement, lightweight camera localization systems, and multimodal inference in ambiguous medical scenes. These contributions address core challenges in surgical navigation and medical device tracking.

• Uncertainty Quantification
• Bayesian Inverse Problems
• Data Science
• +۷ مورد دیگر

Dr. Aretha Teckentrup is a Lecturer in the Mathematics of Data Science at the University of Edinburgh's School of Mathematics. Her research focuses on integrating mathematical models with observational data, particularly in areas like uncertainty quantification, Bayesian inverse problems, and computational methods for partial differential equations (PDEs). She holds a PhD in Mathematics from the University of Bath and has held postdoctoral positions internationally, including in Florida. Her work emphasizes interdisciplinary approaches, blending statistics, numerical analysis, and applied mathematics. Notably, she has contributed to advancing Gaussian processes, multilevel Monte Carlo techniques, and sparse grid methods for high-dimensional problems. Her academic journey reflects a strong commitment to bridging theoretical foundations with practical applications. She has published extensively on topics such as probabilistic numerical methods, error estimation in Bayesian inference, and adaptive sampling strategies. Dr. Teckentrup is an active member of the SIAM community, having received the prestigious SIAG/UQ Early Career Prize in recognition of her contributions to uncertainty quantification. Her research continues to explore innovative solutions for data-driven modeling challenges in science and engineering. Dr. Teckentrup’s work often addresses the growing importance of combining data with physical models, exemplified by her development of numerical methods for weather prediction and stochastic dispersion modeling. She advocates for collaboration across disciplines and emphasizes the transformative potential of integrating computational tools with real-world data. Her career trajectory underscores the dynamic and collaborative nature of modern academic research in applied mathematics and data science.

Michael Kerberمشاهده پروفایل

استاد

• Computational Topology
• Algebraic Topology
• Computational Geometry
• +۴ مورد دیگر

Michael Kerber is a Professor at Graz University of Technology, Institute of Geometry, specializing in computational topology and geometry. His research bridges mathematical theory with applications in data analysis, focusing on persistent homology and geometric algorithms. PhD from Max Planck Institute for Informatics (2009) Postdoc positions: Max Planck Institute, Stanford University, IST Austria His work centers on designing efficient algorithms for topological data analysis, particularly: Persistent Homology 2-Parameter Persistence Geometric Filtrations Algebraic Curve Analysis High-Dimensional Sphere Packing Recent publications emphasize: Improved Delaunay bifiltration methods NP-hardness of interleaving distance computation Sparse Čech filtrations for big data Integration with graph neural networks He has co-developed key software tools: PHAT DIPHA HERA SOPHIA

• Computer Science
• Artificial Intelligence
• Reinforcement Learning
• +۶ مورد دیگر

Eric Eaton is a prominent researcher in Computer Science, specializing in Artificial Intelligence, Reinforcement Learning, and Federated Learning. His work bridges theoretical advancements with practical applications in healthcare, robotics, and educational technology, as evidenced by his collaborations with institutions like the Vector Institute and co-authors such as Marcel Hussing and Amir-massoud Farahmand. Research Focus: Lifelong Learning, Object-Centric Representation, and Algorithmic Fairness Key Contributions: ELLA algorithm, Distributed Continual Learning frameworks, and AI integration in surgical video analysis His recent publications address critical challenges in high update ratio reinforcement learning, federated learning for surgical data, and ethical considerations in algorithmic fairness. These works highlight his interdisciplinary approach, combining AI with healthcare and education. Eaton's leadership in projects like FORLA and Slot-BERT demonstrates innovation in unsupervised learning and temporal coherence. His involvement in the CS2023 curriculum design underscores his commitment to advancing computer science education.

• Biomedical Engineering
• Machine Learning
• Wearable Technology
• +۳ مورد دیگر

Muhammad Awais Bin Altaf is a researcher specializing in biomedical engineering, machine learning, and wearable technology. His work focuses on low-power embedded systems for neurological and cardiovascular monitoring, including EEG processors for seizure detection and PPG-based blood pressure classification. He has collaborated extensively with co-authors like Wala Saadeh and Jerald Yoo on IEEE journals and conferences. His research interests include Biomedical signal processing Wearable health devices Machine learning for medical diagnostics Energy-efficient hardware design Neurological disorder detection Embedded systems for clinical applications Recent publications highlight trends in shallow neural networks, autoencoders, and hardware acceleration for real-time health monitoring. Key subfields span seizure prediction, stress detection, and impedance-adaptive sensors. Collaborations include institutions in Germany, Finland, and Pakistan. His work often integrates open-source toolflows and industry-standard chip design techniques, emphasizing practical implementations for wearable environments. Contributions to HDR imaging algorithms and biomedical SoCs demonstrate interdisciplinary expertise in signal processing and healthcare technology.

Ahmed El-Sayedمشاهده پروفایل

دانشیار

• Robotics
• Artificial Intelligence
• Fuzzy Systems
• +۴ مورد دیگر

Dr. Ahmed El-Sayed is an Associate Professor in the Department of Electrical and Computer Engineering at the School of Engineering, University of Bridgeport. He holds a Ph.D. in Computer Science and Engineering (2016) and M.Sc. degrees in Computer Engineering (2011) and Engineering Mathematics (2006). Ph.D. in Computer Science and Engineering, University of Bridgeport (2016) M.Sc. in Computer Engineering, University of Bridgeport (2011) M.Sc. in Engineering Mathematics, Alexandria University (2006) B.Sc. in Electrical Engineering, Alexandria University (2003) His academic work focuses on robotics , artificial intelligence , and computer vision , with specific emphasis on fuzzy systems , machine learning , and soft computing . His research spans theoretical developments and practical implementations in autonomous systems , medical imaging , and industrial automation . The 15 most recent publications highlight his contributions to generative models , medical diagnostics , disassembly sequencing , and high-dimensional feature analysis . These works intersect disciplines such as robotics , machine learning , and sustainable manufacturing . Professional Memberships: IEEE ASEE Phi Kappa Phi Upsilon Pi Epsilon Egyptian Engineering Syndicate (lifetime member) He teaches courses including Embedded Systems Design , Computer Vision , and Introduction to Autonomous Vehicles .