جستجوی استاد راهنمای دانشگاهی

Bian Yang is a Professor at the Department of Information Security and Communication Technology , Norwegian University of Science and Technology (NTNU), Faculty of Information Technology and Electrical Engineering. His research focuses on biometric data protection , privacy-enhancing technologies , and healthcare cybersecurity since joining NTNU in 2008. Education: Ph.D. (2006), M.S. (2002), B.S. (2000) Research Interests: Secure health data management, privacy-preserving computing, human factors in security, secure biometrics, and multimedia security His work includes establishing the eHealth and Welfare Security (eHWS) group under NTNU's Center for Cyber and Information Security (CCIS) in 2016, aiming to bridge cybersecurity and healthcare sectors. Recent publications emphasize privacy-preserving continuous authentication , generative AI in biometrics , and consent management in IoT . Articles span journals like Pattern Recognition , IEEE Access , and Journal of Personalized Medicine , reflecting interdisciplinary applications of machine learning , GANs , and policy frameworks to healthcare security. Scientific Contributions: Over 15 recent publications address topics such as AI-driven stress detection , security for wearable devices , and cybersecurity in low-income healthcare , with a focus on privacy barriers , adaptive protocols , and multi-party data governance .

• Information Security
• Cybersecurity
• Biometrics
• +۳ مورد دیگر

Dr. Syed Fawad Hussain is an Associate Professor in the School of Computer Science at the University of Birmingham, Dubai campus. He is actively engaged in research and academic leadership, contributing significantly to the fields of artificial intelligence and machine learning. Education: PhD in Computer Science, Grenoble Alpes University, France (2010) MSc in Computer Science, Sorbonne University (Pierre and Marie Curie), Paris, France (2006) BS in Computer Science (2003) Dr. Hussain's research focuses on developing advanced machine learning algorithms for high-dimensional data, with specializations in multi-view learning, co-clustering, and semantic kernels. His work spans interdisciplinary domains including mobile healthcare, social media analysis, and cultural heritage preservation. He applies AI to classify ECG/EEG signals, detect diseases like schizophrenia and NAFLD, restore ancient manuscripts, and improve wireless communication systems. His recent publications reveal a strong trend in applying deep learning and ensemble methods to real-world problems across healthcare, defense, transportation, and digital humanities. He frequently collaborates on projects involving signal processing, image classification, and data fusion. Dr. Hussain has supervised numerous graduate students and co-authored with researchers across multiple disciplines. He has contributed to national-level academic development in Pakistan as co-chair of the Higher Education Commission's curriculum revision committee. Scientific Contributions: Published in high-impact journals such as Scientometrics , Information Fusion , Expert Systems with Applications , and Knowledge-Based Systems . Active in both theoretical and applied AI research. Contributor to national curriculum development in computer science. He is affiliated with the University of Birmingham’s Dubai campus and previously served as a faculty member and dean at GIK Institute, Pakistan. His work continues to bridge theory and application in artificial intelligence.

• Artificial Intelligence
• Machine Learning
• Multi-View Learning
• +۷ مورد دیگر

Dr. Daniel Schlör is a researcher at the Chair of Data Science (Informatics X) at the University of Würzburg, with additional affiliations to the CLiGS (Computational Literary Genre Stylistics) research group in Digital Humanities. His work focuses on machine learning for cybersecurity, fraud detection, and explainable AI, with recent projects exploring synthetic data generation, knowledge graph integration, and deep learning for imbalanced datasets. Research interests include Explainable AI (XAI) for anomaly detection Deep learning architectures for domain-specific relationships Multi-agent simulations for fraud scenario modeling Computational stylistics in digital humanities Article trends show expertise in Developing novel neural units (e.g., ModeConv) for structural anomaly differentiation Advancing XAI methods with generative inpainting techniques Creating open ERP datasets for occupational fraud research Applying graph neural networks to water distribution leakage detection Labs & collaborations include the Data Science Chair’s AI Institute at Hubland Nord campus and CLiGS research group for computational literary analysis.

• Machine Learning
• Anomaly Detection
• Natural Language Processing
• +۵ مورد دیگر

Xiaoping Qian is a Professor in the Department of Mechanical Engineering at the University of Wisconsin-Madison, where he leads research in topology optimization, shape optimization, and additive manufacturing. He holds a PhD in Mechanical Engineering from the University of Michigan (2001) and previously taught at the Illinois Institute of Technology. Education: PhD (2001) University of Michigan MS (1994) & BA (1992) from Huazhong University of Science and Technology His research spans topology optimization for multi-physics systems, machine learning-based design, and thermofluid systems. Recent work focuses on physics-informed neural networks, 3D concrete printing, and additive manufacturing constraints. Articles from 2025-2023 highlight his contributions to heat exchangers, structural optimization, and AI-driven design. Notable scientific awards include the 2019 Kaiser Professorship 2015 ASME Fellow Multiple Best Paper Awards from ASME, ACM, and IEEE . He advises PhD students through courses like M E 890 - PhD Research and Thesis and M E 990 - Dissertator Research and Thesis . Current projects include thermohydraulic testing of heat sinks and quantum topology optimization.

• Topology optimization
• Shape optimization
• Multi-physics systems
• +۳ مورد دیگر

Baoguo Yu is a Professor at the Department of Navigation Science and Technology within the School of Geodesy and Geomatics at Wuhan University. He has established himself as a leading researcher in the field of navigation and positioning systems, with over 70 publications spanning from 2016 to 2025 across prestigious journals including IEEE Transactions on Instrumentation and Measurement, Sensors, and IEEE Access. His research interests primarily focus on GNSS (Global Navigation Satellite Systems), indoor positioning technologies, pseudolite systems, and signal processing for navigation applications. Professor Yu's work demonstrates a strong emphasis on practical implementations, particularly in challenging environments where traditional GNSS signals are degraded or unavailable. His recent publications reveal a growing integration of machine learning techniques with traditional navigation approaches, reflecting the evolving nature of the field. Analysis of his 15 most recent publications shows a clear research trajectory toward high-precision positioning solutions, with particular attention to indoor navigation challenges, LEO satellite navigation augmentation, and robust positioning under interference conditions. His work spans theoretical algorithm development to practical implementations for smartphones, unmanned vehicles, and specialized navigation equipment. Professor Yu maintains extensive collaborations with researchers across China, particularly with scholars from Wuhan University and other prominent Chinese institutions. His frequent co-authors include Lu Huang, Xingli Gan, Heng Zhang, and Chuanzhen Sheng, suggesting well-established research teams working on complementary aspects of navigation technology. His research has significant applications in autonomous vehicles, pedestrian navigation, emergency response systems, and any domain requiring precise positioning where GPS signals are unreliable. The consistent publication output in high-impact journals demonstrates his active research program and continued contributions to advancing navigation science.

• GNSS (Global Navigation Satellite Systems)
• Indoor Positioning Systems
• Pseudolite Technology
• +۷ مورد دیگر

Jan Lellmann is a Professor at the Institute of Mathematics and Image Computing of the University of Lübeck, with affiliations to Fraunhofer MEVIS . His research focuses on variational image processing , emphasizing the systematic formulation of prior knowledge into energy functions for improved accuracy and data efficiency. Applications span medical imaging , earth sciences , and biological data analysis . He develops non-smooth optimization methods for problems with combinatorial aspects like image segmentation . His recent work includes manifold-constrained optimization and quantum algorithms for imaging tasks. He has contributed software libraries like MFOPT (for manifold optimization) and COAL (for convex energy minimization). Scientific Awards: Best Student Paper Award at SSVM 2021 Honorable Mention at CVPR 2016 Recent Research Trends: Integration of quantum computing with classical image registration. Advancements in Riemannian geometry for protein dynamics and cryo-EM. Development of meta-learning frameworks for adaptive image alignment. Focus on non-smooth and higher-order regularization for sparse data reconstruction.

• Variational Image Processing
• Optimization on Manifolds
• Image Registration
• +۷ مورد دیگر

Christoph Reisinger is a Full Professor of Applied Mathematics at the Mathematical Institute, University of Oxford, a position he has held since 2017 (previously Associate Professor from 2006-2017). He is a Fellow at St Catherine's College (since 2006) and an Associate Member of the Oxford-Man Institute of Quantitative Finance (since 2008). He serves as Director of Graduate Studies (Teaching) at the Mathematical Institute (2023-2026) and was Co-Director of the Centre for Doctoral Training on The Mathematics of Random Systems (2022/23). He is also a member of the Mathematical and Computational Finance Group (since 2006) and the Data Science Group (since 2018). Professor Reisinger's research lies at the intersection of stochastic modeling, computational mathematics, and machine learning. His work spans stochastic control, numerical analysis of nonlinear PDEs, mathematical foundations of reinforcement learning and deep learning, and computational finance. Current research focuses on approximation and reinforcement learning of stochastic control problems, mathematical analysis of neural networks, mean-field limits of particle systems, numerical approximation of high-dimensional SDEs and PDEs, and mathematical modeling of financial markets. His publication record shows a strong trend toward integrating machine learning with traditional computational finance methods. Recent work explores policy gradient methods for stochastic control, mean-field game theory applications to financial markets, and novel numerical schemes for McKean-Vlasov equations. His research bridges theoretical mathematics with practical applications in quantitative finance, particularly in option pricing, risk management, and market microstructure modeling. Professor Reisinger has supervised numerous doctoral students, many of whom have gone on to prestigious positions in academia and industry. His current PhD students include Boris Baros, William Gibson, Matthieu Meunier, Zihan Guo, Maria Olympia Tsianni, Filippo De Angelis, and Michael Giegrich. His past students include several who have received awards such as the Graduate Paper Competition First Prize and G-Research DPhil Prizes. He serves as Editor-in-Chief of the Journal of Computational Finance (since 2018) and holds associate editor positions at Applied Mathematics and Optimization (2022-2024), Applied Mathematical Finance (since 2018), and International Journal of Computer Mathematics (since 2012).

• Stochastic Control
• Computational Finance
• Machine Learning
• +۷ مورد دیگر

Guillaume Rabusseau is an Associate Professor at Mila and the Department of Computer Science and Operations Research (DIRO) at Université de Montréal , holding a Canada CIFAR AI Chair since 2019. His research spans machine learning, theoretical computer science, and multilinear algebra. Education : PhD in Computer Science (2016) from Aix-Marseille Université , MSc in Fundamental Computer Science from AMU, BSc in Computer Science (distance learning) from AMU. Research Interests : Tensor methods for machine learning, spectral learning algorithms, connections between weighted automata, tensor networks, and RNNs, low-rank regression, and nonlinear computational models on structured data. Publication Trends : Recent work focuses on tensor train decomposition, temporal graph benchmarks, quantum-inspired ML, spectral regularization, and formal methods for sequence modeling. Collaborative papers address dynamic graphs, foundational models for molecular learning, and high-order pooling in GNNs. Scientific Awards : Canada CIFAR AI Chair (2019–present, renewed) Advising : Supervises PhD students like Maude Lizaire and Pascal Tikeng Notsawo, and MSc students such as Soroush Omranpour. Past advisees include Andy Huang (now at Oxford) and Tianyu Li (Samsung).

• Machine Learning
• Multilinear Algebra
• Tensor Networks
• +۴ مورد دیگر

Ramesh Harjani is the Edgar F. Johnson Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. He maintains an active research program with numerous ongoing projects and publications, and is currently accepting PhD students. His office is located in Kenneth H. Keller Hall on the University of Minnesota campus in Minneapolis. Dr. Harjani received his educational training at prestigious institutions globally: B.Tech in Electrical and Electronics Engineering from Birla Institute of Technology, Pilani, India (1982) MTech in Electrical Engineering from Indian Institute of Technology, New Delhi (1984) PhD in Electrical and Computer Engineering from Carnegie Mellon University (1989) Professor Harjani's research focuses on analog and RF circuit design for communication systems with particular emphasis on CMOS technologies. His work spans both wireless and wired communication domains, with current efforts directed toward high-performance front-end components for broadband and multi-standard wireless systems, as well as next-generation high-speed wired communication systems. His research group also investigates specialized areas including data converters, sensor interface circuits, analog circuit synthesis, and micro-power analog design. Recent publication trends show continued innovation in analog circuit design methodologies, with emphasis on improving performance metrics while addressing modern semiconductor challenges like FinFET self-heating effects. His work bridges theoretical circuit design with practical implementation challenges in advanced technology nodes, particularly 65nm CMOS processes. The research demonstrates strong industry relevance with applications in wireless communications, signal processing, and high-speed data conversion. Professor Harjani leads multiple significant research projects: Scalable Ultra-Low Variation Analog-Time Neural Network (S-UATN) Accelerator (2025-2025) Passive Tile Architecture for MAX (2023-2024) ALIGN Analog Layout Design Suite (2023-2024) Automated Layout Of Analog Arrays In Advanced Technology Nodes (2022-2024) ALIGN: Analog Layout, Intelligently Generated from Netlists (2018-2023) He maintains the Harjani Research Group, which collaborates extensively with industry partners including BAE SYSTEMS, NORTHWESTERN UNIVERSITY, SPAWAR, and THE NATIONAL SCIENCE FOUNDATION, demonstrating strong connections between academic research and practical engineering applications.

• Analog Circuit Design
• RF Circuit Design
• Wireless Communications
• +۷ مورد دیگر

Jordy Kaufman is an Associate Professor in the School of Health Sciences at Swinburne University of Technology, where he conducts interdisciplinary research at the intersection of child development, psychology, and technology. His work explores how children interact with and learn from virtual reality, touchscreens, social robots, and language apps, using advanced methods such as EEG, neuroimaging, and behavioral studies. BSc in Cognitive Science, Carnegie Mellon University PhD in Psychology, Duke University Postdoctoral Training, Centre for Brain and Cognitive Development, University of London His research focuses on cognitive and social development in the digital age, with particular emphasis on learning technologies, screen media impacts, and safety training using immersive environments. He investigates how emerging technologies shape children’s cognition, empathy, and social behavior, and how these tools can be optimized for educational and clinical applications. His recent publications reveal a strong trend in the use of immersive virtual reality (IVR) for teaching water safety, problem-solving, and time-telling, as well as examining neural responses to language apps and ethical implications of child-robot interaction. His work demonstrates that IVR enhances engagement, knowledge retention, and transfer of learning compared to traditional methods. Marbach Castle Residency, Jacobs Foundation (2017) Editor, Child Development (2015–) Associate Editor, Infant and Child Development , PLoS ONE , Frontiers in Psychology Member, Guideline Development Group for Australian 24-hour Movement Guidelines Kaufman supervises numerous PhD and master’s students, with research topics spanning digital play, social cognition, autism interventions, and injury prevention. He has received grants from the Australian Department of Education, Life Saving Victoria, and international bodies to support applied research in technology-enhanced learning and child safety. He leads a lab that established Australia’s first infant cognitive neuroscience facility and continues to pioneer the use of immersive technologies in developmental psychology.

• Developmental Psychology
• Child Psychology
• Cognitive Neuroscience
• +۱۲ مورد دیگر

Dana Rezazadegan is a Senior Lecturer in Computer Science and Software Engineering at Swinburne University of Technology, affiliated with the School of Science, Computing and Emerging Technologies. Her work bridges artificial intelligence, computer vision, and healthcare applications, with a strong focus on human-robot collaboration and digital health solutions. Research Interests: Her primary research areas include deep learning, computer vision, LLMs, health informatics, continual learning, and AI for inclusive education. She investigates how intelligent systems can support human activities in healthcare and assistive robotics. The recent publications highlight a consistent focus on applying AI to healthcare challenges—ranging from clinical NLP and patient decision support to automated severity assessment in diseases like COVID-19. She also contributes significantly to foundational AI methods, including uncertainty quantification, speech summarization, and real-time 3D pose estimation for embedded and mobile platforms. Superstar of STEM, Science and Technology Australia, 2021 Teaching Award, Swinburne University of Technology, 2020 Early Career Marie Curie Fellowship, 2014 Top 20 in Iran's Mathematics Olympiad, 2000 Dana actively supervises PhD students on topics such as continual learning, AI tutoring, electricity price forecasting, and 3D pose estimation. She has secured multiple research grants, including projects on inclusive AI for dyslexic learners and AI for energy forecasting. Her collaborative work spans institutions like Macquarie University and CSIRO, and she engages in public outreach through media contributions on AI ethics and privacy. She leads and contributes to interdisciplinary teams focused on AI in healthcare and education, often involving co-design with clinicians and end-users. Her labs and research groups emphasize real-world applicability, ethical considerations, and human-centered AI design.

• Artificial Intelligence
• Deep Learning
• Computer Vision
• +۷ مورد دیگر

Yannik Hahn serves as a Scientific Researcher and doctoral student at the Institute for Technologies and Management of Digital Transformation (TMDT) at the University of Wuppertal since June 2021, focusing on machine learning applications for industrial and environmental systems. His educational background includes a Bachelor's and Master's degree in Computer Science from Heinrich Heine University Düsseldorf, specializing in machine learning and deep learning. His Master's thesis investigated cross-lingual text summarization using transformer-based neural networks. Hahn's research spans Machine Learning, Deep Learning, Natural Language Processing, Explainable AI, and Reinforcement Learning, with emphasis on practical implementations in manufacturing quality control and environmental forecasting. His work bridges theoretical AI advancements with real-world industrial challenges. Analysis of his publications reveals a consistent focus on transformer architectures and deep learning for time-series applications, particularly in arc welding quality prediction and flood forecasting. His research demonstrates innovation in representation learning, out-of-distribution detection, and efficient continual learning systems for industrial deployment. No scientific awards are documented in the available materials. As a doctoral candidate, Hahn has no listed advisees or formal mentoring responsibilities. No research grants or funding sources are specified in the provided documentation. He operates within the Institute for Technologies and Management of Digital Transformation (TMDT), which specializes in digital transformation technologies and their industrial implementation, particularly in manufacturing and environmental monitoring contexts.

• Machine Learning
• Deep Learning
• Natural Language Processing
• +۲ مورد دیگر

C. H. T. Child is an Associate Professor in the Department of Computer Science at City, University of London, with a distinguished research career spanning over two decades. The academic maintains an active research profile with publications extending through 2025, demonstrating continued scholarly contributions across multiple domains of computer science and artificial intelligence. Child's primary research interests center around Reinforcement Learning , Artificial Intelligence , and Computer Vision , with specialized expertise in non-Euclidean geometry applications for gaming environments, medical informatics, and human-computer interaction. The research portfolio reveals a consistent trajectory from foundational work in reinforcement learning algorithms to applied research in game AI, medical informatics, and scene representation learning. A notable research thread involves the application of non-Euclidean geometry to create novel gaming experiences, while another significant strand focuses on developing advanced AI systems for non-player characters with personality modeling capabilities. Analysis of the most recent publications (2019-2025) shows an evolution toward more sophisticated deep learning architectures applied to complex problems in medical informatics and scene representation. The work has increasingly incorporated transformer networks, attention mechanisms, and advanced feature extraction techniques, reflecting broader trends in the field while maintaining the researcher's distinctive focus on reinforcement learning fundamentals. The publication record demonstrates successful translation of theoretical AI concepts into practical applications across gaming, healthcare, and computer vision domains. Child has supervised multiple doctoral students and research collaborators, with several co-authors appearing consistently across publications as first authors on significant works. The research has been supported through university resources and likely external funding, though specific grant details are not provided in the available materials. The academic maintains an active presence in both theoretical and applied research communities, with publications spanning top conferences and journals in computer science and AI. The researcher leads work in the Extreme AI Personality Engine project and contributes to computer vision research with applications in hand pose estimation and medical imaging. These research streams are well-integrated, with reinforcement learning principles providing a unifying theoretical framework across diverse application domains.

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

Ryan Sullivan is an atmospheric scientist at Argonne National Laboratory's Environmental Science Division. His research integrates in situ field measurements, remote sensing, numerical modeling, and data analytics (including geospatial statistics, time series analysis, and machine learning) to study atmospheric processes and their societal impacts. Key areas include atmospheric composition, urban air quality, radiative forcing through cloud-aerosol interactions, and land-biosphere-atmosphere coupling via turbulent fluxes. As Lead Instrument Mentor for the DOE's Atmospheric Radiation Measurement (ARM) facility, he oversees Eddy Correlation (ECOR) flux measurement systems, Surface Energy Balance Systems (SEBS), and Energy Balance Bowen Ratio (EBBR) systems, while serving as Associate Instrument Mentor for Soil Temperature and Moisture Profile (STAMP) systems. He acts as Principal Investigator for multiple AmeriFlux sites across North America, including US-A03, US-A10, US-A12, and others. 2017 Ph.D. in Atmospheric Sciences, Cornell University 2016 M.S. in Atmospheric Sciences, Cornell University 2014 M.S. in Geological Sciences – Atmospheric Science, Indiana University 2012 B.S. in Meteorology, Northern Illinois University 2010 A.S., William Rainey Harper College His scientific publications focus on methane flux analysis, land-atmosphere energy exchange, prescribed fire emissions, and AI/ML applications in climate science. Notable awards include the National Champion (Switzerland) and Frontiers Planet Prize 2nd Edition recognition. Ryan's recent publications reveal trends in machine learning for climate data analysis, Arctic energy budget studies, and wildfire emission factor quantification. His work spans collaborations with institutions like AmeriFlux, FLUXNET-CH4, and DOE-sponsored field campaigns.

• Atmospheric Science
• Air Quality
• Climate Modeling
• +۵ مورد دیگر

Gary H. Glover is Professor of Radiology at Stanford University School of Medicine, with courtesy appointments as Professor of Electrical Engineering and Professor of Psychology. He also holds professorships in the Neurosciences and Biophysics programs and serves as Director of the Radiological Sciences Laboratory at the Lucas Center. His academic career spans over five decades with significant contributions to medical imaging physics. Dr. Glover earned his B.S. (1964), M.S. (1965), and Ph.D. (1969) in Electrical Engineering from the University of Minnesota, where his dissertation focused on "Millimeter-wave interaction with an InSb Magnetoplasma." His educational background laid the foundation for his pioneering work in MRI physics and engineering. Dr. Glover's research focuses on the physics and mathematics of Magnetic Resonance Imaging, particularly rapid MRI scanning methods using spiral and other non-Cartesian k-space trajectories for dynamic imaging of brain function. His work develops pulse sequences and processing methods for mapping cortical brain function by imaging metabolic responses to stimuli, with applications in basic neuroscience and clinical settings. His research has significantly advanced the understanding of hemodynamically driven increases in oxygen content in activated cortex, using pulse sequences sensitive to paramagnetic behavior of deoxyhemoglobin. Analysis of Dr. Glover's recent publications reveals a consistent focus on improving fMRI techniques, with particular emphasis on physiological noise correction, spiral imaging methods, and BOLD signal analysis. His work spans technical MRI physics, physiological monitoring, and clinical applications in neuroimaging and breast imaging. The publications demonstrate his continued leadership in addressing fundamental challenges in MRI acquisition, reconstruction, and physiological confound effects. General Electric Company Steinmetz Award (1985) Fellow, American Institute for Medical and Biological Engineering (1997) President, ISMRM (1998) Gold Medal, ISMRM (2000) RSNA Outstanding Researcher Award (2001) Member, National Academy of Engineering (2006) Outstanding Teacher Award, ISMRM (2010) ISMRM Lauterbur Lecturer (2018) Dr. Glover has advised numerous graduate students and postdoctoral researchers, with his research group including members such as Hyemin Han, Emily Ferenczi, and Haisam Islam. His laboratory has received substantial funding from NIH and other sources to advance MRI technology. The Radiological Sciences Laboratory under his direction has been a hub for innovation in MRI physics, developing techniques like spiral-in/out imaging, physiological noise correction methods (RETROICOR), and specialized software tools for fMRI analysis. The Radiological Sciences Laboratory at the Lucas Center, directed by Dr. Glover, has maintained a collaborative research environment focused on developing advanced MRI techniques. The lab has produced numerous software tools including the fmriutil package for fMRI analysis, retroicor for physiological noise correction, and specialized reconstruction algorithms. Dr. Glover's team has consistently bridged engineering physics with clinical applications, maintaining strong collaborations across Stanford and with external institutions.

• Magnetic Resonance Imaging
• fMRI
• Neuroimaging
• +۵ مورد دیگر