جستجوی استاد راهنمای دانشگاهی

Francesc J. Ferriمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• Machine Learning
• +۷ مورد دیگر

Francesc J. Ferri is a Professor in the Department of Computer Science at the University of Valencia since 2008. He holds a Licenciado in Physics (1987) and a Ph.D. in Pattern Recognition (1993) from the same university. His research focuses on Computer Vision, Pattern Recognition, and Machine Learning, with contributions to subspace learning, metric learning, and kernel methods. He has authored/co-authored ~140 papers (h-index=11 as of 2011), with notable work on feature selection and large-scale classification. Education: Licenciado in Physics (1987), Ph.D. in Pattern Recognition (1993), both from University of Valencia. Affiliations: Member of AERFAI, IAPR, ACM, IEEE. Teaching includes courses on Automata Theory, Algorithms, Computer Vision, and Pattern Recognition at undergraduate and graduate levels. He has advised multiple Ph.D. and MSc students, including Adrian Perez-Suay (metric learning) and Katerine Diaz-Chito (subspace-based image recognition). Research projects include sabbaticals at Surrey University (1993) and Michigan State (2005), and collaborations on audio event detection, bioinformatics, and e-learning systems. His recent work addresses explainable AI, edge computing for safety systems, and multidataset HRTF analysis.

Kim W. Wongمشاهده پروفایل

استادیار

• Visual Cognition
• Intuitive Physics
• Navigational Affordances
• +۵ مورد دیگر

Kim W. Wong is an Assistant Professor in the Psychology Department at Williams College, where she leads the WAVE lab. Her research focuses on visual cognition, intuitive physics, and the spontaneous extraction of higher-level properties from visual scenes. She completed her PhD in Psychology at Yale University under Brian Scholl’s Perception and Cognition lab, with additional collaborations at the Cognitive & Neural Computation lab (Yildirim) and Johns Hopkins’ Cognitive Neuroscience Lab (McCloskey). Her work explores how perception extracts concepts like navigational affordances, unfinishedness, and intuitive physics (e.g., block tower stability, cloth dynamics). Key projects include studying visual routines in mazes, spontaneous path tracing, and the perception of causal history through physics-based priors. She employs methods like change detection, visual working memory tasks, and computational modeling (e.g., the Woven model for cloth perception). Her research has been presented at venues like the Vision Sciences Society and the Society for Philosophy and Psychology. Notable contributions include demonstrating how unfinished visual events prioritize memory retention and how unstable towers break into visual awareness faster than stable ones. Education: PhD in Psychology (Yale University), with prior involvement in labs studying perception, cognitive neuroscience, and statistical learning. Her current projects extend to soft material perception, temporal dilation effects, and the neural underpinnings of intuitive physics.

Yazan Alqudahمشاهده پروفایل

استاد

• Optical Wireless Communication
• Mobile Networks
• Embedded Systems
• +۳ مورد دیگر

Dr. Yazan Alqudah is a Professor in the Department of Electrical and Computer Engineering at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He holds a Ph.D. in Electrical Engineering from Pennsylvania State University (2003) and has held roles at Intel Corporation and multiple universities, including Princess Sumaya University for Technology and Western Carolina University. His research focuses on optical wireless communication, mobile networks, and embedded systems, with contributions to machine learning applications in transportation and healthcare. Education: Ph.D., Electrical Engineering, Pennsylvania State University, 2003 M.S., Electrical Engineering, Pennsylvania State University, 1997 B.S., Electrical Engineering and Computer Science, University of Jordan, 1993 Research Interests: His work spans broadband optical wireless communication, next-generation mobile networks, embedded systems design, and machine learning applications in healthcare and transportation. Notable areas include autonomous vehicle sensing, wearable health monitoring, and fault-tolerant automotive systems. Publications Trends: Recent articles emphasize machine learning for driving behavior analysis, sensor fusion in autonomous systems, and optimization of wireless communication protocols. Earlier work includes contributions to WiMAX technology, optical wireless MIMO systems, and energy storage solutions for solar PV arrays. Awards: Intel Recognition Awards (2005, 2007) Best Researcher Award at PSUT (2014) Labs & Projects: Developed hands-on labs for broadband wireless technology and embedded systems education. Current projects include a mobile application for breathing abnormality detection and tools for facial thermography-based lie detection.

Yong Wangمشاهده پروفایل

دانشیار

• Autonomous systems
• Energy systems
• Manufacturing systems
• +۳ مورد دیگر

Yong Wang is an Associate Professor and Associate School Director at the School of Systems Science and Industrial Engineering, Binghamton University. He holds dual PhDs from the University of Illinois and Huazhong University of Science and Technology. His research focuses on autonomous systems, energy systems, manufacturing systems, healthcare systems, operations research, and data science . Notable areas include smart energy infrastructure optimization, autonomous vehicle navigation, and healthcare analytics through machine learning and deep learning techniques. Recent work demonstrates a strong emphasis on real-time control systems, sustainable energy integration, and data-driven decision making . His publications span topics such as battery lifecycle modeling, pandemic prediction systems, and optimization of manufacturing logistics. Labs: Director of the Smart Energy Operations Research Laboratory (SEORL) Education: PhD in Engineering (University of Illinois), PhD in Systems Engineering (Huazhong University of Science and Technology) Research outputs highlight interdisciplinary approaches combining robotics, energy systems, and healthcare, with applications in smart grids, autonomous driving, and medical imaging.

Igor Gilitschenskiمشاهده پروفایل

استادیار

• Robotics
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Igor Gilitschenski is an Assistant Professor in Computer Science at the University of Toronto, leading the Toronto Intelligent Systems Lab (TISL). He focuses on developing probabilistic and learning-based techniques for robotic perception and decision-making, aiming to enable robust interactive autonomy. Prior to this, he held roles at MIT CSAIL, ETH Zurich's Autonomous Systems Lab, and the Karlsruhe Institute of Technology (KIT). His research interests include autonomous systems, computer vision, and deep learning applied to robotics. He collaborates with institutions like the Vector Institute and is a Vector Research Scholar. His work spans robot learning, simulation-driven policy training, and safety-critical systems. Notable projects include pseudo-simulation for autonomous driving, vision-language-action models, and neural radiance fields for 3D scene representation. He actively recruits PhD students to work at the intersection of computer vision, deep learning, and robotics, emphasizing controllable simulation engines and safe learning frameworks. Recent research trends in his articles highlight advancements in generative models, reinforcement learning, and perception systems for dynamic environments. He emphasizes collaboration across disciplines, including event-based vision and language-guided reasoning for robotic tasks.

Luca Cassanoمشاهده پروفایل

دانشیار

• Hardware Security
• RISC-V Processors
• Deep Learning Reliability
• +۳ مورد دیگر

Luca Cassano is an Associate Professor at the Department of Electronics, Informatics and Bioengineering (DEIB) at Politecnico di Milano, Italy. He leads the Hardware Forge research group, focusing on hardware security, RISC-V processors, and reliability of deep learning systems. He earned his Ph.D. in Information Engineering from the University of Pisa (2013), with prior postdoctoral research at CNR and Politecnico di Milano. His research spans hardware Trojans, microarchitectural attacks (e.g., Spectre/Meltdown), fault-tolerant design, and radiation-hardened systems. Notable achievements include winning the TTTC's E. J. McCluskey Doctoral Thesis Award (2014) and multiple best paper awards at IEEE conferences. He teaches courses on hardware accelerators and computer fundamentals, and collaborates with industry partners like TDS-Space and ESA on satellite systems and secure RISC-V cores. Research interests include: Hardware Security (Trojans, Side-Channel Attacks) Secure RISC-V Processor Design Deep Learning Reliability Fault-Tolerant Systems Selected articles highlight work on benchmarking deep learning resilience, transient execution attacks on RISC-V, and radiation effects mitigation. His work bridges theory and practice, addressing challenges in embedded systems, space computing, and cybersecurity. Grants and collaborations emphasize applied research in dependable and secure embedded systems.

Isobelle Barrett Meyeringمشاهده پروفایل

پژوهشگر ارشد

• Australian history
• Childhood
• Children's rights
• +۵ مورد دیگر

Dr Isobelle Barrett Meyering is an ARC DECRA Research Fellow at Macquarie University's School of Humanities. Her research focuses on Australian history, particularly children's rights, feminism, and gender history. She leads projects like 'Child Citizens: Young People and Australian Democracy since 1945,' examining shifts in youth citizenship. Previously, she held a Macquarie University Research Fellowship (2018–2024) and roles at UNSW, UOW, and the Australian Domestic and Family Violence Clearinghouse. Educated at UNSW (PhD, 2017), her work bridges feminist theory and child rights advocacy. Meyering co-convenes the Children's Rights research stream at Macquarie and contributes to organizations like the History Council of NSW and Oral History NSW. Education: PhD in History, UNSW (2017), focusing on women's and children's liberation Research Interests: Meyering's work explores the intersection of feminism and children's rights, particularly in the 1970s. She analyzes historical movements, including the Australian women's liberation movement's role in shaping child rights discourse. Her book *Feminism and the Making of a Child Rights Revolution, 1969–1979* highlights radical visions of children's liberation. She also examines suburban feminism, backlash movements, and the cultural history of organizations like Pipi Storm Theatre Company. Grants & Projects: ARC DECRA Fellowship (2024–2027) Macquarie University Research Fellowship (2018–2024) ANU Visiting Fellowship (2022) Awards: Thelma Hunter Women and Politics PhD Prize (2018) AWGSA PhD Award (2018) Literati Award (2020) Professional Roles: Co-convenor of Macquarie's Children's Rights Research Stream Member of the History Council of NSW General Council (2019–present) Past Treasurer of the History Council of NSW (2019–2023) Founder of *History in the Making*, a student journal

• Medical Imaging
• Machine Learning
• Temporal Data Analysis
• +۳ مورد دیگر

Svitlana Zinger is a Full Professor at Eindhoven University of Technology (TU/e), specializing in context-informed dynamic image analysis for clinical decision support. She leads research in medical image/video analysis for diagnosis/prognosis, with expertise in temporal data analysis, machine learning, and multimodal data processing. Her affiliations include the Center for Care & Cure Technology, Eindhoven MedTech Innovation Center (e/MTIC), NeuroPlatform, and EAISI Health. She holds a PhD from the École Nationale Supérieure des Télécommunications, France, and has extensive postdoctoral experience in image ontology and handwritten document analysis. Academic background: MSc in Computer Science (Dnepropetrovsk State University, Ukraine, 2000), PhD in 3D Data Interpolation (2004). Research focuses on neuroimaging, cardiovascular monitoring, and AI-driven medical decision support. Key projects include FORSEE (video-based adverse event detection), NEUROTREND (fMRI biomarkers for depression), and STRATUM (neurosurgical 3D tools). Research interests span brain network dynamics in autism/depression, hyperspectral imaging for tumor delineation, and camera-based physiological monitoring. She teaches courses like 'Image Analysis for Healthcare Technologies' and 'Medical Image Processing'. Awards include 2nd place in the CAMELYON17 challenge for metastasis detection. Supervises 32 research projects, collaborating with clinical and industrial partners globally. Her labs include the Biomedical Diagnostics Lab and Signal Processing Systems group. Current work explores biomarkers for accelerated cognitive aging in epilepsy and AI-driven tools for neurosurgery. Active in international collaborations across Europe and beyond.

• Glaucoma Detection
• Ophthalmic Imaging
• Artificial Intelligence in Ophthalmology
• +۲ مورد دیگر

Benjamin Yixing Xu, MD, PhD, is Associate Professor of Ophthalmology and Chief of the Glaucoma Service at the Keck School of Medicine, University of Southern California. His research develops novel interventions to prevent glaucoma-related vision loss, combining non-invasive ocular imaging and artificial intelligence for early detection. He investigates health disparities in glaucoma care and utilizes population studies like CHES and LALES to examine disease prevalence. Dr. Xu's work focuses extensively on primary angle closure glaucoma (PACG), leveraging advanced imaging and epidemiological methods. His research portfolio includes AI-based diagnostic tools, health outcome disparities, and ophthalmic informatics. Current projects examine stress biomarkers in glaucoma pathogenesis and federated learning approaches for multi-institutional data analysis. Education includes BS in Biomedical Engineering from Yale University, and MD/PhD from Columbia University through the NIH Medical Scientist Training Program. He completed residency at USC Roski Eye Institute and glaucoma fellowship at UCSD Shiley Eye Institute. Awards include Newsweek's America's Best Eye Doctors (2021-2023) and multiple teaching honors. America's Best Eye Doctors - Newsweek (2021-2023) USC Ophthalmology Attending Teaching Award (2019) USC Ophthalmology Resident Teaching Award (2016) Dr. Alfred P. Steiner Dean's Day Research Award (2011)

Steven Verstocktمشاهده پروفایل

استاد

• Multimodal Data Processing
• Video Analysis
• Computer Vision
• +۲ مورد دیگر

Steven Verstockt is a Professor at Ghent University’s Department of Electronics and Information Systems, affiliated with the Internet Technology and Data Science Lab (IDLab) and the Multimedia Lab. His research spans multimodal data processing computer vision video analytics geospatial information technology sports data science domains. He leads interdisciplinary projects in sports, media, and cultural heritage, focusing on spatio-temporal data collection, classification, and visualization. Recent research trends highlight his work in CO2-based occupancy detection BIM semantic enrichment multi-camera sports analytics geosocial media mining historical archive digitization hyperspectral image classification published across 2025–2024. His team combines computer vision, machine learning, and geospatial tools for real-world applications.

Jing Zhouمشاهده پروفایل

استاد

• Control Theory
• Robotics and Automation
• Offshore Mechatronics
• +۴ مورد دیگر

Prof. Jing Zhou is a Professor in the Department of Engineering Sciences at the University of Agder, Norway, and serves as Research Director of the Top Research Centre in Mechatronics and Head of the Robotics and Automation Group. She holds a Ph.D. from Nanyang Technological University (2006) and has extensive industry experience, including roles at the International Research Institute of Stavanger (NORCE) and the Norwegian University of Science and Technology (NTNU). Her research focuses on control theory, robotics, offshore mechatronics, and learning-based systems. She leads major projects such as DEEPCOBOT (2020-2025) and INMOST (Indo-Norwegian collaboration), and has authored three influential books on adaptive control. Her academic roles include IEEE Industrial Electronics Society (IES) AdCom Member, Women-in-IES Chair, and editorial positions in top journals like IEEE Transactions on Automatic Control. She has organized major conferences like IEEE ICIEA 2024 and contributed to over 150 publications. Awards include membership in the Norwegian Academy of Technological Sciences and Agder Academy of Sciences. Prof. Zhou’s research spans adaptive control for nonlinear systems, robotics autonomy, offshore crane control, and drilling technology. Her work integrates AI and reinforcement learning to address challenges in industrial automation and environmental monitoring. She advises numerous PhD students and collaborates globally on projects like ImpactWind and INDURB, advancing sustainable energy and smart industrial systems.

Roberto Casulaمشاهده پروفایل

استادیار

• Biometrics
• Fingerprint Spoofing
• Adversarial Machine Learning
• +۶ مورد دیگر

Roberto Casula is an Assistant Professor at the University of Cagliari, specializing in Biometrics and Fingerprint Spoofing. He leads research in liveness detection, adversarial machine learning, and explainability. His work focuses on enhancing security in biometric systems through innovative methods like 3D face reconstruction and fusion techniques for surveillance applications. Education: Bachelor's in Electronic Engineering (University of Cagliari, 2015) Master's in Telecommunications Engineering (University of Cagliari, 2019) PhD in Biometrics, PRALab (2023), thesis: The Art of Fingerprint Spoofing Research Interests: Fingerprint presentation attack detection Adversarial attacks and deepfake mitigation Explainability in security systems Crowd analysis and surveillance 3D face biometrics and fusion methods Competitions & Leadership: Organized the LivDet competition (2021-2023), overseeing spoof creation and sensor integration. Pioneered realistic fingerprint spoofing techniques like ScreenSpoof. Collaborates with PRA Lab and PRALab on biometric security challenges. Labs & Teams: Active member of PRA Lab and PRALab, focusing on fingerprint spoofing, liveness detection, and adversarial machine learning applications.

Ambra Demontisمشاهده پروفایل

استادیار

• Adversarial Machine Learning
• Cybersecurity
• Deep Learning Security
• +۸ مورد دیگر

Ambra Demontis is an Assistant Professor at the University of Cagliari, Italy, affiliated with the Department of Electrical and Electronic Engineering. She holds an M.Sc. (Hons.) and Ph.D. in Computer Science and Electronic Engineering from the same institution. Her research focuses on enhancing the security of machine learning algorithms against adversarial attacks, including evasion, poisoning, and backdoor attacks. She actively contributes to the academic community as an Associate Editor for the International Journal of Machine Learning and Cybernetics and the Elsevier Pattern Recognition Journal , and has organized workshops like AISec (2019-2022). She is a member of IEEE, ACM, and IAPR, and chairs the IAPR Technical Committee 1. Her educational background includes advanced degrees in Computer Science and Electronic Engineering from the University of Cagliari (M.Sc. 2014, Ph.D. 2018). She serves on conference program committees and has held roles such as Area Chair for ICPR and Track Chair for various events. Her research interests span adversarial machine learning, malware detection, and optimizing neural networks for security and energy efficiency. Key research themes include defending against adversarial examples, improving robustness in deep learning systems, and addressing vulnerabilities in real-world applications like autonomous driving and IoT devices. She has developed frameworks such as BUILD and Attackbench for incremental learning and attack evaluation, respectively. Her work emphasizes practical defenses and theoretical insights into adversarial attack mechanisms. Dr. Demontis’ contributions also include advancing hyperparameter optimization for adversarial attacks and exploring the intersection of domain knowledge with adversarial defense strategies. She collaborates internationally, contributing to Python tools like secml for secure machine learning. Her research addresses both theoretical and applied aspects of AI security, aiming to bridge the gap between robustness and real-world deployment challenges.

• Age-Related Macular Degeneration
• Retinal Degeneration
• Pyroptosis
• +۵ مورد دیگر

Rakshanya Sekar is a final-year Medical Research PhD candidate at The Australian National University (ANU), affiliated with the ANU College of Science and Medicine, Biology Teaching & Learning Centre, and Eccles Institute of Neuroscience. As a casual session academic, she contributes to teaching and mentoring at the Research School of Biology. Her primary research focuses on developing extracellular vesicle-based therapeutics for age-related macular degeneration (AMD) and investigating mechanisms of retinal degeneration, including pyroptosis and inflammation. She actively promotes diversity in STEMM through roles as Neuroscience HDR representative, Future Research Talent ambassador, and organizer of the Brainteaser outreach event. Her research interests span neurodegenerative diseases, immunology, and oncology, with a focus on interdisciplinary approaches combining molecular biology and clinical applications. Recent work explores gliotic responses in neurodegeneration and the role of retrotransposons in oral cancers. Awards: None explicitly listed. Grants/Advising: No grants or advisee students mentioned. Labs/Teams: Clear Vision Research Lab (primary affiliation).

Matthew Danishمشاهده پروفایل

مدرس

• Units-of-measure Verification
• Static Analysis
• Programming Languages
• +۷ مورد دیگر

Dr Matthew Danish is a faculty member at the Department of Computer Science and Technology, University of Cambridge. His research spans multiple areas within computer science, with a focus on practical applications of theoretical concepts. He is based in room FE25 of the William Gates Building at 15 JJ Thomson Avenue, Cambridge CB3 0FD. Dr Danish's research interests encompass a diverse range of topics including Machine Learning and Artificial Intelligence, Programming Languages, Semantics and Verification, and Systems and Networking. His work bridges theoretical computer science with practical applications in urban computing, privacy-preserving systems, and edge computing. He has made significant contributions to the field of units-of-measure verification, developing techniques to ensure correctness in scientific computing. His recent publications (2019-2025) demonstrate a clear research trajectory focused on applying computer science to real-world problems. Danish has published extensively on units-of-measure verification, static analysis of scientific code (particularly Fortran), privacy-preserving systems for edge devices, and applications of computer vision in urban environments. His work often combines theoretical rigor with practical implementation, as evidenced by projects like DeepDish that run on off-the-shelf hardware like Raspberry Pi. Dr Danish has supervised or collaborated on numerous research projects related to smart environments, adaptive city platforms, and real-time data processing systems. His work on Cerberus demonstrates expertise in developing privacy-preserving solutions for crowd counting and localization, while his research on RACER shows capabilities in complex event recognition systems. Based in the William Gates Building, Dr Danish is part of Cambridge's vibrant computer science research community, contributing to multiple research themes within the department. His interdisciplinary approach connects computer science theory with applications in urban studies, public health, and environmental monitoring.