جستجوی استاد راهنمای دانشگاهی

• Medical Imaging
• PET/CT Imaging
• Quantitative Image Analysis
• +۵ مورد دیگر

Drew A. Torigian is a Professor of Radiology at the Perelman School of Medicine, University of Pennsylvania, with extensive contributions to medical imaging research. His work focuses on developing advanced methodologies for PET/CT image analysis, disease quantification, and anatomical segmentation through innovative applications of deep learning and computer vision techniques. Dr. Torigian's research interests span multiple critical areas in medical imaging including quantitative image analysis, PET/CT applications, deep learning in medicine, radiation therapy planning, and anatomical segmentation. His work has significantly advanced the field of medical image analysis through the development of novel algorithms for disease quantification without explicit object delineation, standardized anatomic space frameworks, and attention-based neural networks for medical image interpretation. His recent publications demonstrate a strong trend toward integrating artificial intelligence with medical imaging, particularly in developing gaze-guided neural networks, geographical attention mechanisms, and hybrid transformer-convolutional architectures for improved medical image analysis. These works consistently address critical challenges in radiology including disease quantification, anatomical segmentation, and the development of clinically interpretable AI models. Dr. Torigian has received recognition through his substantial publication record in top-tier medical imaging venues including Medical Image Analysis, IEEE Transactions on Biomedical Engineering, and leading medical imaging conferences. His research has been consistently funded through various mechanisms supporting innovation in medical imaging technology. Through his mentorship and collaborative research, Dr. Torigian has contributed to advancing the field of medical imaging with practical applications in radiation therapy planning, disease quantification, and the development of standardized methodologies for image analysis. His work bridges the gap between computer science innovation and clinical radiology applications.

• Computer Science
• Machine Learning
• IoT
• +۷ مورد دیگر

Ali Hassan is a researcher affiliated with the National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science, Department of Computer and Software Engineering. His work spans multiple domains in computer science, engineering, and applied mathematics, focusing on areas such as machine learning, IoT, energy systems, and medical informatics. His research explores: Reinforcement learning applications for battlefield information systems IoT antenna design and performance evaluation Optimization of second-life battery systems in electric vehicles Transformers for real-time vehicle collision avoidance Image hashing techniques using visual attention models Neural network-based phasor estimation for power grids Biomedical sensor systems for non-invasive health monitoring Mathematical modeling of viral dynamics Recent publications demonstrate his emphasis on interdisciplinary approaches combining AI, signal processing, and sustainability. He has collaborated with institutions across Pakistan, France, Saudi Arabia, and the USA, with a focus on practical implementations in cybersecurity, energy optimization, and healthcare technology.

Gültekin Işıkمشاهده پروفایل

دانشیار

• Deep Learning
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Assoc. Prof. Gültekin Işık is affiliated with the Department of Computer Hardware at Iğdır University's Faculty of Engineering. He holds a PhD in Computer Engineering from Hacettepe University (2011–2019), focusing on Turkish dialect recognition using deep learning. His academic roles include serving as Head of the Department from 2019 to 2021. His research interests span deep learning applications in computer vision (e.g., YOLO-based crowd detection), optimization algorithms (e.g., Slime Mould Algorithm), environmental modeling (solar power efficiency prediction), and healthcare (breast cancer detection). He has authored numerous peer-reviewed articles and books, including works on convolutional neural networks for plant disease identification and hybrid optimization techniques for data clustering. Prof. Işık teaches advanced courses such as İleri Derin Öğrenme (Advanced Deep Learning) and Sinir Ağları (Neural Networks). His recent work emphasizes real-time video analysis for public health (social distancing) and energy systems optimization. Despite no listed awards, his contributions to interdisciplinary fields like bioacoustics and renewable energy are notable. He has advised two master’s students: Mehmet Şirin Gündüz (2023) on YOLO-based crowd detection and Seda Bayat (2021) on bird species recognition using deep learning. His research integrates theoretical models with practical applications in domains ranging from agriculture to medical imaging.

Masoud Daneshtalabمشاهده پروفایل

استاد

• Deep Learning Acceleration
• Hardware/Software Co-Design
• Interconnection Networks
• +۷ مورد دیگر

Masoud Daneshtalab is a Professor at Mälardalen University, leading the Heterogeneous System research group (HERO). He previously held roles as a European Marie Curie Fellow at KTH Royal Institute of Technology (2014) and as a university lecturer and group leader at the University of Turku, Finland (2012-2014). His research focuses on interconnection networks, hardware/software co-design, deep learning acceleration, and evolutionary optimization. He specializes in fault-tolerant DNN accelerators, time-sensitive networking (TSN), and embedded systems. His work bridges theoretical advancements with practical implementations, emphasizing reliability and efficiency in edge computing and AI applications. Research interests include: Network-on-Chip (NoC) architectures and congestion prediction Fault resilience in deep neural networks (DNNs) Optimization of federated learning and homomorphic encryption for edge AI Integration of TSN with 5G and automotive systems Hardware acceleration techniques for computational efficiency Recent publications emphasize advancements in robust AI architectures, fault tolerance mechanisms, and TSN-based communication protocols. His work often addresses practical challenges in deploying machine learning models on resource-constrained devices. He actively contributes to interdisciplinary projects in autonomous systems, healthcare monitoring via FMCW radar, and neural architecture search for embedded applications. Labs/Teams: Leads the HERO group at Mälardalen University, focusing on heterogeneous computing systems and real-time embedded systems.

Kjell Gunnar Robbersmyrمشاهده پروفایل

استاد

• Mechatronics
• Machine Design
• Condition Monitoring
• +۵ مورد دیگر

Kjell Gunnar Robbersmyr is a Professor at the University of Agder's Department of Engineering Sciences and director of the Top Research Center in Mechatronics. With a Ph.D. in mechanical engineering from NTNU (1992), his career spans academic leadership, research management at Agder Research, and active contributions to IEEE. His work focuses on mechatronics, machine design, and condition monitoring, with special emphasis on fault diagnosis in electric motors and vehicle crash modeling. Senior Member of IEEE Member of Norwegian Academy of Technical Sciences Member of Agder Academy of Sciences Research interests include: Advanced fault diagnosis in electric drives using AI and signal processing Vehicle crashworthiness modeling with lumped parameter and finite element methods Optical measurement technology for machine monitoring Digital twin applications for infrastructure and wind energy systems Condition monitoring of low-speed bearings and rotating machinery Recent publications demonstrate expertise in: Deep learning for imbalanced motor fault datasets Transformer networks in power electronics diagnostics 3D reconstruction techniques for mechanical systems Multi-classifier decision fusion in power systems Dynamic operations modeling for electric vehicles Scientific contributions include: Over 50 peer-reviewed articles Leadership in the Intelligent Monitoring research group Development of novel inverter topologies Innovations in wind turbine condition monitoring Advancements in laser-based mechanical diagnostics

Simo Alamiمشاهده پروفایل

مربی

• Deep Learning
• Reinforcement Learning
• Inverse Reinforcement Learning
• +۴ مورد دیگر

Simo Alami is a Ph.D. candidate and Tutor at Ecole Polytechnique, affiliated with the LIX computer science laboratory under the supervision of Professor Jesse Read. He teaches tutorials for the Advanced Machine Learning and Autonomous Agents course (Computer Science, Ingénieur 3A/Master1) since 2020, focusing on practical implementations of machine learning algorithms and autonomous systems. His academic background includes undergraduate and graduate studies in Mathematics and Computer Science at Université Pierre et Marie Curie and Ecole CentraleSupélec. Ph.D. Candidate, Ecole Polytechnique (2020-Present) Master's Studies, Mathematics and Computer Science, Université Pierre et Marie Curie and Ecole CentraleSupélec Alami's research centers on developing algorithms that learn from experience through self-learned metrics, with core expertise in Deep Learning, Inverse Reinforcement Learning, and Metric Learning. His work targets robotic applications where agents must perform novel tasks by understanding logical concepts, while also extending to energy management systems through non-invasive load monitoring. He explores how meta-learning frameworks can enable transferable reward structures across domains. His publication record reveals a strong trajectory in distributional reinforcement learning and inverse reinforcement learning using generative models, with significant contributions to metric learning for clustering and energy disaggregation. The consistent focus on transferable methodologies across robotics and smart grid applications demonstrates his interdisciplinary approach to solving real-world problems through advanced machine learning techniques. Scientific recognition includes: Nvidia Grant awarded in 2022 for research on Meta-Inverse Reinforcement Learning 3rd Prize at Hi Paris Hackathon (2021) for a reinforcement learning-based smart grid energy management solution While not supervising graduate students as a Ph.D. candidate, Alami has secured competitive external funding including the Nvidia research grant. His industry collaboration with Accenta during a visiting researcher position resulted in ECML 2022 publications on non-invasive load monitoring, highlighting his ability to bridge academic research with practical engineering applications. As an active member of the LIX laboratory at Ecole Polytechnique, Alami participates in cutting-edge computer science research while maintaining external collaborations. His visiting researcher position at Accenta demonstrates engagement with industry partners, and his organization of the IRT SystemX Electromobilité et territoires webinar in 2021 reflects leadership in knowledge dissemination within the energy and mobility research community.

Gita Alaghbandمشاهده پروفایل

استاد

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Professor Gita Alaghband is Chair and PhD Director at the Department of Computer Science and Engineering, University of Colorado Denver. Her work bridges high-performance computing with AI applications in computer vision, facial recognition, and deep learning optimization. Current research focuses on real-time multi-human tracking for autonomous systems Develops explainable AI solutions for medical and financial domains Leads the Parallel Distributed Systems (PDS) Lab Key publication trends include: 2025: Medical imaging AI for pediatric trauma analysis 2024: Financial forecasting with causal econometrics and adversarial defense systems 2023: Trajectory prediction models and HEVC compression frameworks Scientific recognition: IRC Best Paper Award (2020) for facial recognition research Students advised include industry leaders at Google, VMWare, and Nissan, with research spanning parallel computing, medical imaging AI, and autonomous systems.

Jaafar Alghazoمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۷ مورد دیگر

Dr. Jaafar Alghazo is an Associate Professor in the Math, Science and Technology Department at the University of Minnesota. He currently teaches courses in Information Technology Management and Software Engineering, including ITM 3900-E90: Internship, SE 2200-001: Intro to Software Engineering, and SE 4500-001: Senior Project I. Research Focus: Machine Learning, Deep Learning, Medical Image Analysis, Cybersecurity, IoT, UAV Communications Key Publication Trends: 15 most recent works span 2025–2023, emphasizing neural network optimization, Arabic sign language recognition, flood detection, and secure medical imaging techniques

• Operations Research
• Healthcare Optimization
• Predictive Maintenance
• +۴ مورد دیگر

Rosita Guido is an Associate Professor in the Department of Mechanical, Energy and Management Engineering at Università della Calabria, specializing in Operations Research (MATH-06/A). Her academic role focuses on mathematical optimization and artificial intelligence applications across healthcare, manufacturing, and sustainable systems. She teaches graduate courses including Tools and Methods for Innovation in Healthcare and Business Intelligence within the Management Engineering program. Her research interests span healthcare optimization, predictive maintenance, machine learning applications, and sustainable supply chain management. Guido develops advanced mathematical models and algorithms for complex decision problems under uncertainty, with particular emphasis on healthcare systems optimization, tool condition monitoring, and resource allocation. Her methodological expertise includes mathematical programming, combinatorial optimization, stochastic programming, and hybrid AI techniques. Analysis of her recent publications reveals a strong interdisciplinary focus bridging engineering and healthcare. Key trends include the application of machine learning (particularly SVMs and CNNs) to medical diagnostics, optimization of healthcare operations (patient admission scheduling, bed management), and integration of AI with IoT for industrial applications. Her work demonstrates consistent innovation in solving real-world problems through mathematical modeling and algorithm development. As an active member of the Operations Research (Ricerca Operativa) research group at DIMEG, she contributes to projects addressing logistics optimization, green transportation systems, energy infrastructure management, and clinical process optimization. The group maintains international collaborations with prestigious research centers and companies including Amazon. Guido's teaching portfolio includes graduate-level courses in Management Engineering, where she integrates her research expertise into curriculum development. Her academic service includes participation in departmental governance and research initiatives within the Department of Mechanical, Energy and Management Engineering.

• Medical Image Analysis
• Deep Learning
• Machine Learning
• +۷ مورد دیگر

İlkay Öksüz is an Associate Professor in the Department of Computer Engineering at Istanbul Technical University (ITU), where he has been serving since 2021, following his appointment as a Doctoral Academic Staff member in 2020. He previously held research positions at King's College London (2017–2020), The University of Edinburgh (2016), and Yale University (2015–2016). He earned his Ph.D. in Computer, Decision and Systems Science from IMT School for Advanced Studies Lucca, Italy, in 2017, under the supervision of Prof. Sotirios Tsaftaris. B.Sc. in Electronics Engineering, Istanbul Technical University (2010) M.Sc. in Electrical-Electronics Engineering, Bahçeşehir University (2013) Ph.D. in Computer, Decision and Systems Science, IMT School for Advanced Studies Lucca (2017) His research focuses on medical image analysis, particularly in cardiac MRI, with core interests in image segmentation, registration, quality assessment, and reconstruction using deep learning. He also explores electricity price forecasting and explainable AI. His work bridges machine learning with clinical applications, aiming to improve diagnostic accuracy and automation in radiology. The 15 most recent publications highlight a strong trend in applying deep learning to medical imaging, especially cardiac and prostate MRI, mammography, and ECG analysis. A significant emphasis is placed on explainability , optimization , and clinical applicability , with frequent participation in MICCAI challenges and collaborations with radiology departments. Projects also extend into energy forecasting, showing interdisciplinary versatility. Scientific awards include: 2024 ITU Young Scientist Award in Engineering 2024 Parlar Research Incentive Award He leads the Predictive Intelligence and Medical Imaging (PIMI) Lab at ITU, mentoring graduate students and managing multiple funded projects, including the TUBITAK International Fellowship. His lab has achieved top placements in national and international AI competitions such as Teknofest and MICCAI challenges. He serves as principal investigator (PI) on several active grants focused on interpretable deep learning for medical imaging and electricity forecasting. The PIMI Lab, under his leadership, actively participates in high-impact medical AI challenges and has consistently achieved top rankings in competitions related to prostate cancer detection, breast imaging, and cardiac MRI reconstruction, demonstrating strong translational research impact.

• Digital Signal Processing
• Biomedical Engineering
• Audio Processing
• +۷ مورد دیگر

Rafik Goubran is a Professor in the Department of Systems and Computer Engineering at the Faculty of Engineering and Design, Carleton University. He holds the distinguished title of Chancellor’s Professor and currently serves as the Vice-President (Research and International). He earned his Ph.D. from Carleton University and is a licensed Professional Engineer (P.Eng.). His research expertise spans digital signal processing, biomedical engineering, audio processing, and the development of smart environments for senior independent living, with applications in patient monitoring, sensor systems, and real-time analytics. Dr. Goubran's recent publications highlight a strong focus on applying machine learning and sensor technologies to healthcare challenges, particularly in gerontechnology. His work involves non-invasive monitoring of sleep apnea, cognitive decline, driving behavior in older adults, and ambient health monitoring using smart homes and IoT systems. He explores innovative methods such as using pressure mats, audio analysis, and video magnification for vital sign detection and behavioral assessment. Life Fellow, IEEE Fellow, Canadian Academy of Engineering (CAE) Chancellor’s Professor, Carleton University Dr. Goubran has co-supervised 24 Ph.D. and 72 Master’s students and has secured significant research funding from NSERC, CIHR, NCE, and OCE for projects related to aging, health monitoring, and smart technologies. He is the co-leader of the TAFETA project and was the founding Director of the Ottawa-Carleton Institute for Biomedical Engineering. His work involves extensive collaboration with institutions like the Bruyère Research Institute and industry partners such as QNX and BlackBerry. He leads a research lab focused on technology-assisted environments, developing systems for patient monitoring, fall detection, and cognitive assessment, contributing significantly to the field of assistive and ambient intelligence for healthcare.

• Computer Vision
• Image Processing
• Photometric Stereo
• +۱۲ مورد دیگر

Vasileios Argyriou is an academic researcher affiliated with Kingston University, with extensive contributions in computer vision, image processing, and AI applications in IoT, healthcare, and smart agriculture. His work spans photometric stereo, motion estimation, federated learning, and UAV-based remote sensing. Research Interests: His research focuses on developing advanced computer vision techniques including photometric stereo for 3D reconstruction, deep learning for anomaly detection, federated learning for privacy-preserving AI, and AI-driven solutions for smart farming and healthcare. He explores motion estimation, facial recognition, and synthetic data generation for training robust models. The recent publications (2024–2025) highlight a strong trend toward applying machine learning in real-world systems—particularly federated learning for intrusion detection, UAV-based agricultural monitoring, 5G security, and medical imaging. His work integrates computer vision with IoT, wireless communications, and edge computing, demonstrating interdisciplinary innovation. Scientific Contributions: Author of a book on image and video registration with quality metrics. Extensive publication record in top journals including IEEE Transactions, Sensors, and Pattern Recognition. Pioneering work in photometric stereo, motion estimation using phase correlation, and GAN-based face reenactment. He has advised or collaborated with numerous researchers across fields, though specific student names are not listed. His work often involves large-scale data analysis, simulation frameworks, and real-time systems. He is involved in projects related to smart cities, precision agriculture, and autonomous systems. Laboratories and Teams: His collaborations suggest involvement with research groups focused on intelligent systems, computer vision, and IoT at Kingston University and beyond, particularly with Panagiotis Sarigiannidis, Thomas Lagkas, and others in networking and AI.

Massoud Zolgharniمشاهده پروفایل

استاد

• Computer Vision
• Medical Imaging
• Artificial Intelligence
• +۷ مورد دیگر

Massoud Zolgharni is a Professor of Computer Vision at the School of Computing and Engineering, University of West London. He joined UWL as a Senior Lecturer in 2018 and was promoted to Associate Professor in 2020. Previously, he held academic positions at the University of Lincoln and was a Research Associate at Imperial College London. He is a Fellow of the Higher Education Academy and leads the MSc Artificial Intelligence and PhD programs at UWL. BSc in Mechanical Engineering, Amirkabir University of Technology Master’s and PhD, Brunel and Swansea Universities, UK His research focuses on computer vision and medical imaging , particularly in automated cardiac imaging using AI and deep learning . His work aims to develop low-cost, non-invasive techniques for echocardiography and cardiovascular diagnostics. He has published extensively in journals such as Computers in Biology and Medicine , IEEE Transactions , and European Heart Journal . Recent publications (2023–2025) highlight his leadership in AI-driven echocardiography, including left ventricle segmentation, Doppler analysis, and multimodal seizure detection. His research integrates deep learning, signal processing, and real-world clinical validation, establishing a strong interdisciplinary profile bridging computing and medicine. Scientific Awards : Fellow of the Higher Education Academy He has secured major grants from the British Heart Foundation , including a £1.5M Programme Grant (2022–2027) on AI integration in echocardiography. He serves on university research committees, acts as Critical Reader for the School, and organizes the monthly research seminar. He teaches across a wide range of computing and AI programs, contributing to curriculum development and research supervision.

Di Yangمشاهده پروفایل

استادیار

• Remote Sensing
• Geospatial Analytics
• Machine Learning
• +۷ مورد دیگر

Di Yang is an Assistant Professor at the University of Florida, specializing in geospatial technologies and environmental monitoring. His research integrates machine learning, remote sensing, and citizen science to address land use changes, ecosystem dynamics, and human-environment interactions across global scales. He leads projects funded by NASA, BLM, and NCAR, focusing on topics like forest management, climate resilience, and geospatial cloud computing. Education: Ph.D. in Geography, University of Florida (2019) M.S. in Environmental Engineering, Texas A&M University-Kingsville (2013) B.S. in Environmental Science, Liaoning University of Petroleum and Chemical Technology (2011) Research Interests: Remote sensing applications, geospatial analytics, machine learning for environmental monitoring, citizen science, land change science, and sustainability. Grants & Projects: NASA: Forecasting Mosquito-Borne Disease Risk in a Changing Climate (PI) NASA Early-Career: Evaluating Long-term Impacts of Land-use Transformation on Fire Regimes BLM: Monitoring Yermo xanthocephalus using UAS and AI/ML His lab develops innovative frameworks like the Open Land-Use Map strategy and leverages platforms like Google Earth Engine for large-scale environmental analysis. Yang actively collaborates on NEON biodiversity surveys and contributes to ecological network design.

• Mechanical Engineering
• Manufacturing Engineering
• Finite Element Analysis
• +۵ مورد دیگر

Ambarish Kulkarni is an Associate Professor at Swinburne University of Technology's School of Engineering, specializing in mechanical design, virtual/augmented reality (VR/AR), and electric vehicle (EV) technologies. His research focuses on product design, EV drivetrains, battery packaging, medical devices, and bushfire shelter design. He holds a PhD from Swinburne, a Master's from Queensland University of Technology, and a Bachelor's from the Indian Institute of Industrial Engineering. Research interests span finite element analysis, bio-mechanical modeling, and Industry 4.0 applications. Notable projects include Bombardier Tram development, therapeutic sleep systems, and smart manufacturing frameworks. He has supervised over 20 HDR students on topics ranging from predictive maintenance to extended reality in education. Awards include the Teaching Excellence Award (2016) and Premier's Design Awards for medical and safety innovations. He is a Fellow of the Institution of Engineers (Australia) and actively involved in professional bodies like SAE International. Grants funded projects on hydrogen generators, smart manufacturing systems, and graphene supercapacitors. His work bridges academia and industry through collaborations with firms like Bambach Wires and iMOVE Australia.