جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Image Processing
• Medical Imaging
• +۳ مورد دیگر

Dr Ahmed Fetit is a Senior Lecturer and Senior Teaching Fellow at Imperial College London's Department of Computing (Faculty of Engineering), specializing in AI for Healthcare through the UKRI Centre for Doctoral Training. His research focuses on applying machine/deep learning to medical imaging, particularly in collaboration with NHS Trusts. He holds a PhD from the University of Warwick (2015) and degrees from the University of Birmingham (MSc, BEng). He is a Fellow of the Higher Education Academy (FHEA). Education: PhD: University of Warwick (2015), applications of machine learning to tumor diagnosis/prognosis via MRI MSc: University of Birmingham BEng (Hons): University of Birmingham Research Interests: Ahmed's work bridges AI and healthcare, emphasizing medical imaging applications such as MRI segmentation, adversarial robustness, fetal brain connectivity analysis, and retinal biomarker discovery. His projects often involve NHS collaborations to translate AI innovations into clinical practice. Recent Article Trends: Recent publications highlight advancements in CNN robustness (e.g., k-space artifact simulation), fetal/neonatal brain segmentation, and XAI explanations in healthcare imaging. His work also explores cross-modal MRI inference and cardiovascular risk stratification using retinal features. Awards: Fellow of the Higher Education Academy (FHEA) Advising & Grants: While specific grant details are not listed, his NHS collaborations imply involvement in funded healthcare AI projects. No advisees are explicitly mentioned. Labs/Teams: Active in the UKRI CDT in AI for Healthcare and the Department of Computing's Neuroimaging research groups.

• Machine Learning
• Data Mining
• Big Data
• +۴ مورد دیگر

Jesse Davis is a Professor at the Department of Computer Science , KU Leuven , actively contributing to the Machine Learning group and the Sports Analytics Lab . He is part of the Faculty of Engineering Science and the Leuven.AI Institute . Ph.D. in Computer Sciences from University of Wisconsin-Madison (2007) M.S. in Computer Sciences from University of Wisconsin-Madison (2005) B.A. in Computer Science from Williams College (2002) His research focuses on machine learning, data mining, big data analytics, and sports analytics, with significant work in: Transfer learning and Markov logic networks Anomaly detection and semi-supervised learning Medical NLP and biomechanical data analysis Soccer performance metrics and tactical analysis His recent work explores spatio-temporal data analysis in sports and explainable AI for medical applications, with collaborations spanning finance, healthcare, and semiconductor manufacturing. Notable scientific awards include: Best Paper Award (Applied Data Science Track) at KDD 2019 Best Technical Paper Award at Intelligence Analysis Workshop He advises numerous PhD and Master's students in areas like: Football analytics Tree ensemble compression Medical question-answering systems Biomechanical load prediction His lab develops tools such as: GSSL for Markov network structure learning TODTLER for transfer learning Alchemy system for Markov logic networks

• Computer Vision
• Medical Image Analysis
• Pattern Recognition
• +۵ مورد دیگر

Jaime S. Cardoso is a prominent researcher at the University of Porto and Institute for Systems and Computer Engineering, Technology and Science (INESC TEC) in Portugal. His extensive publication record spanning two decades demonstrates his leadership in computer vision, medical image analysis, and pattern recognition. His research primarily focuses on applying artificial intelligence to healthcare challenges, particularly in medical imaging and diagnostics. Cardoso's research interests center on explainable AI for medical applications, biometrics, and computer vision. His work bridges the gap between theoretical machine learning and practical medical solutions, with significant contributions to breast cancer diagnosis, medical image segmentation, and biometric security systems. He has developed innovative approaches to medical image analysis, including virtual staining techniques and privacy-preserving explanation methods for medical AI systems. His recent publications (2023-2025) reveal a strong emphasis on explainable AI in medical contexts, with multiple papers addressing how to make deep learning models more transparent and trustworthy for healthcare applications. He has also made significant contributions to face recognition technology, video anomaly detection, and specialized medical imaging techniques for breast cancer and neonatal EEG analysis. Among his scientific contributions are numerous collaborations with researchers across Portugal and internationally. His work has appeared in top-tier journals including IEEE Access, Medical Image Analysis, and Neurocomputing, reflecting the high impact of his research. Cardoso has supervised numerous students who have become established researchers in their own right, including Ricardo P. M. Cruz, Kelwin Fernandes, and Ana Filipa Sequeira. His research group appears to focus on the intersection of deep learning, medical imaging, and biometrics, with strong connections to clinical applications.

Arpan Gujaratiمشاهده پروفایل

استادیار

• Real-Time Systems
• Distributed Systems
• Fault Tolerance
• +۵ مورد دیگر

Arpan Gujarati is an Assistant Professor in the Department of Computer Science at the University of British Columbia (UBC), affiliated with the Systopia Lab. His research focuses on real-time systems, distributed systems, fault tolerance, and reliability analysis in cloud and cyber-physical systems domains. He holds a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and has postdoctoral and research experience at MPI-SWS and UBC. Education: PhD in Real-Time Systems (MPI-SWS/TU Kaiserslautern, 2020), Postdoctoral Researcher at MPI-SWS (2020), Research Associate at UBC (2022–2023), B.Sc. from Birla Institute of Technology and Science (BITS Pilani, India). Research interests include distributed real-time systems, reliability analysis of cloud applications, fault-tolerant machine learning, and scheduling algorithms. His work bridges theory and practice, addressing challenges in ultra-reliable CPS and resilient distributed systems. Awards: Best Dissertation Award (SIGBED, 2020), Best Paper Awards (RTSS 2022, ECRTS 2018), Distinguished Artifact Award (OSDI 2020). Advising: Supervises PhD and undergraduate students in distributed systems and resilience engineering. Active in UBC’s Computer Science Graduate Program, teaching courses like CPSC 416 (Distributed Systems) and CPSC 538G (Topics in Computer Systems). Labs/Teams: Leads the Systopia Lab, collaborating with industry partners on projects like robotic arm datasets and self-driving lab tools (RABIT). Involved in research groups focused on machine learning resilience and real-time systems.

• Autonomous Driving
• Computer Vision
• Robotics
• +۵ مورد دیگر

Holger Caesar is a tenured Assistant Professor at Delft University of Technology (TU Delft) in the Intelligent Vehicles Lab . He leads research on scalable approaches for autonomous vehicle perception, prediction, and data annotation, with a focus on sensor fusion, domain adaptation, and minimal supervision. His work has been cited over 15,000 times. Education PhD in Computer Vision, University of Edinburgh Prior studies at KIT Karlsruhe, EPF Lausanne, and ETH Zurich Research Interests span autonomous driving perception, weakly supervised learning, novel view synthesis (NeRFs), diffusion models, and collaborative perception. He emphasizes reducing reliance on manual annotations through active learning and partial labeling techniques. Recent Publications include work on 4D Gaussian Splatting, camera-radar fusion, open-set scene graph generation, and safety benchmarking. These reflect trends toward multi-modal perception, robust sensor fusion, and foundation models for scalable autonomous systems. Scientific Awards ELLIS Europe Scholar (2024) TU Delft Cohesion Grant (100k EUR, 2024) Climate Action Grant (30k EUR, 2024) TKI High Tech Systems Grant (502k EUR, 2022) AiNed XS Grant (80k EUR, 2023) Argoverse Scene Flow Challenge Winner (unsupervised track, 2024) Advising & Grants include EU Horizon funding for the MOSAIC project (1 PhD and 1 Postdoc, 2024), EU KDT Cynergie4MIE grant (450k EUR, 2024), and industrial collaborations with Bosch, Motional, and partners across Europe. Labs & Teams include the Intelligent Vehicles Lab at TU Delft and founding roles in Motional's Data Annotation, Autolabeling, and Data Mining teams. He co-organizes workshops at ICCV, CVPR, and NCCV, and leads the ELLIS Delft Unit seminar series.

Daniel Hashimotoمشاهده پروفایل

استادیار

• Computer Vision
• Artificial Intelligence
• Surgical Education
• +۴ مورد دیگر

Daniel Hashimoto is an Assistant Professor in the Department of Surgery at the Perelman School of Medicine, University of Pennsylvania. He directs the Penn Computer Assisted Surgery and Outcomes (PCASO) Laboratory, a multidisciplinary research group bridging surgery, computer science, and robotics. He also holds appointments in the School of Engineering and Applied Science, focusing on General Robotics, Automation, Sensing, and Perception. His research centers on applying artificial intelligence (AI), computer vision, and robotics to enhance surgical decision-making and education. Key areas include intraoperative data analysis, digital health literacy, and automated feedback systems for surgical technique improvement in clinical and simulated environments. Recent publications highlight his work on AI-driven surgical video analysis, federated learning for medical data, and ethical considerations in robotic surgical training. The PCASO Lab collaborates on validating AI tools for procedures like cholecystectomy and endoscopic myotomy while addressing data quality and privacy frameworks. Scientific Awards: Linda Pechenik Montague Investigator Award (Perelman School of Medicine) Daniel mentors student researchers such as Vivek Singh, Aarush Sahni, and So Hee Ahn, who contribute to projects on robotic procedures and surgical education. The PCASO Lab emphasizes interdisciplinary collaboration between clinicians, engineers, and data scientists.

Hanbaek Lyuمشاهده پروفایل

استادیار

• Discrete Probability
• Matrix Factorization
• Machine Learning
• +۳ مورد دیگر

Hanbaek Lyu is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison, with an affiliation in the Department of Computer Science and membership in the Institute for Foundations of Data Science. His research spans discrete probability, matrix factorization, and machine learning, focusing on large discrete systems including interacting particle systems, networks, and structured random matrices. His educational background includes: Ph.D. in Mathematics, The Ohio State University (2018); Thesis: "Combinatorial and probabilistic aspects of coupled oscillators" (Advisor: David Sivakoff) B.S. in Mathematics, Seoul National University Lyu's research bridges theoretical probability and practical machine learning, with emphasis on optimization for dependent data and complex systems. His work develops foundational algorithms for matrix/tensor factorization while exploring synchronization phenomena in oscillator networks and phase transitions in particle systems. Recent publications highlight interpretable models for biological data and rigorous convergence guarantees for nonconvex optimization. Analysis of his 15 most recent publications reveals three dominant threads: (1) optimization theory for constrained nonconvex problems applied to dictionary learning, (2) interacting particle systems and random matrix theory with combinatorial aspects, and (3) interpretable latent models for network dynamics and genomics. His work consistently combines probabilistic methods with computational applications. Lyu leads two active NSF grants: DMS-2206296 (2022-2025): "Online Dictionary Learning for Dependent and Multimodal Data Samples: Convergence, Complexity, and Applications" DMS-2010035 (2020-2023): "Combinatorial and Probabilistic Approaches to Oscillator and Clock Synchronization" He currently mentors five doctoral students across Mathematics and Computer Science departments, organizes UW-Madison's probability seminar, and collaborates with over 30 researchers including Janko Gravner, Lionel Levine, and Wenpin Tang on interdisciplinary projects spanning genomics, network science, and statistical physics.

Marc Chaumontمشاهده پروفایل

دانشیار

• Visual Data Analysis
• Remote Sensing
• Steganography
• +۱۰ مورد دیگر

Marc Chaumont is an Associate Professor at the University of Nîmes since 2005 and a senior researcher at LIRMM Montpellier. He holds an HDR (Habilitation à Diriger des Recherches) and is an IEEE Senior Member. Since October 2024, he has been an associate collaborator at IRISA laboratory in Vannes, France. His career spans academic research, teaching, and interdisciplinary collaborations with institutions like MARBEC, CIRAD, and CNRS. PhD in Computer Science from IRISA Rennes (2003) Engineer Diploma from INSA Rennes (1999) His research focuses on visual data analysis , remote sensing , and digital forensics , particularly in steganography and steganalysis . He has contributed to creating large-scale databases like LSSD , a 2 million JPEG image dataset for deep-learning steganalysis. His work extends to AI applications in marine ecology, medical imaging, and poverty estimation from satellite data. Recent publications highlight collaborations across disciplines, including transformer models for socioeconomic indicator prediction, 3D fish tracking in coral reefs, and self-supervised encoder pretraining for chronic wound segmentation. He has supervised interns like Ayman El Mannouy (2024) and Abir Zahi (2023) in projects related to weakly supervised segmentation and marine biodiversity. IEEE Senior Member (2020) Top-3% Best Reviewer at IEEE ICIP 2020 Marc has taught courses ranging from signal processing to image compression since 1999. His technical contributions include software tools for image databases and steganalysis algorithms. He actively participates in European projects like DFUC'2024 and ConvEntion for astronomical data classification.

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۱۲ مورد دیگر

Slawomir Nowaczyk is a Professor at the School of Information Technology , Halmstad University. His research focuses on Artificial Intelligence , Machine Learning , and Data Mining , particularly for Streaming Big Data and Knowledge Representation with Weakly-Supervised Models . Practical applications: Predictive maintenance, healthcare informatics, smart industry, and energy systems Developing interestingness metrics for distributed data analysis and self-organization in AI systems Publication Trends : Recent work spans Explainable AI , spatiotemporal forecasting , feature selection , and smart city applications. Key areas include healthcare diagnostics , transportation optimization , and industrial fault detection . Academic Leadership : Serves as Research Leader for the School of Information Technology. Supervises six PhD students and co-supervises one additional student across academic and industrial domains.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۹ مورد دیگر

Qianru Sun is an Associate Professor at the School of Computing and Information Systems, Singapore Management University (SMU), and a Lee Kong Chian Fellow. Her research focuses on artificial intelligence, machine learning, computer vision, and semantic segmentation, with a particular emphasis on few-shot learning, meta-learning, and domain adaptation in remote-sensing imagery. Recent publications highlight her work on visual chain-of-thought reasoning , 3D scene understanding via 2D models , and parameter-efficient fine-tuning of large models. Her research also extends to weakly-supervised semantic segmentation and cross-modal video retrieval . Scientific Awards : Lee Kong Chian Fellow (2021–2023, 2025–2027) Outstanding Service Award by MMAsia (2020) Outstanding Reviewer at NeurIPS'21 and ICLR'21 Students : PhD students: Luo Zilin, Tian Zichen, Ying Jiahao Master’s students: Bao Chunhui, LOH Yi Lin, MA Li

Philip Jacksonمشاهده پروفایل

استاد

• Machine Audition
• Audio-Visual Machine Learning
• Spatial Audio
• +۹ مورد دیگر

Philip Jackson is a Professor in Machine Audition at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. His research focuses on audio-visual machine learning, spatial audio technologies, object-based media production, and responsible AI applications. He has over 200 publications and an h-index of 30, contributing to projects like Nephthys, BALTHASAR, and SAVEE. He served as an associate editor for Computer Speech and Language and reviewer for major journals like IEEE/ACM Transactions on Audio, Speech and Language Processing. Key research interests include sound localization, audio-visual tracking, reverberation modeling, and ethical implications of AI in media. His work spans datasets such as Tragic Talkers, SurrRoom, and ForecasterFlexOBM, advancing immersive audio-visual systems and personalized media experiences. Notable projects include spatial audio reproduction for VR, parametric room acoustics, and citizen council research on algorithmic transparency. He has advised over 20 PhD/Master’s students and collaborates with industry on applications like the Catalan Pavilion's Venice Biennale installation. His grants include UK postdoctoral fellowships and leadership in EU-funded initiatives like UDRC2 and QESTRAL.

Katherine Storrsمشاهده پروفایل

مدرس ارشد

• Visual perception
• Deep learning
• Psychophysics
• +۷ مورد دیگر

Katherine Storrs is a Senior Lecturer in the School of Psychology at the University of Auckland, New Zealand. She leads the Computational Perception Lab, where she combines computational modeling and psychophysical experiments to study visual perception. Her research is supported by a Marsden Fast Start grant, and she is actively involved in teaching and academic service. She earned her PhD in Psychological Science from the University of Queensland in 2015 and has held postdoctoral positions at Justus-Liebig University (Germany) and the MRC Cognition and Brain Sciences Unit (Cambridge, UK). She also worked as a Data Scientist at Twitter in London. Dr. Storrs' research focuses on how the visual system interprets material properties such as gloss, shape, and reflectance. She uses unsupervised deep learning models to simulate and predict human perception, particularly in ambiguous or complex visual environments. Her work bridges cognitive science, neuroscience, and artificial intelligence. Her most recent publications explore topics such as gloss perception, mental rotation, face similarity, and the role of statistical learning in shape encoding. These works frequently appear in high-impact journals like Nature Human Behaviour , PNAS , and Current Biology , demonstrating a strong trend toward using computational models to explain perceptual phenomena. Marsden Fast Start Grant (2021) Humboldt Research Fellowship (2019) UK National Finalist, FameLab (2017) Editorial Board Member, Nature Communications Psychology (2023–) Editorial Board Member, OpenMind (2022–) Social Media Editor, Perception and i-Perception (2020–) She supervises graduate students and teaches courses including PSYCH 306 (Research Methods), PSYCH 775 (Visual Perception in Brains and Machines), and PSYCH 109. She also mentors students in honours, master's, and PhD programs. Her lab is actively involved in interdisciplinary collaborations, particularly with researchers in machine learning and computational neuroscience. The lab is funded by the Marsden Fund and supported by access to high-performance computing resources for training deep neural networks.

Yangyang Shuمشاهده پروفایل

مدرس

• Machine Learning
• Computer Vision
• Weakly Supervised Learning
• +۷ مورد دیگر

Dr. Yangyang Shu serves as an Associate Lecturer in the School of Systems and Computing at the University of New South Wales (UNSW) Canberra campus. Previously, he held a Research Fellow position at the Australian Institute for Machine Learning (AIML) at the University of Adelaide, where he contributed to the Centre for Augmented Reasoning (CAR) project. His academic foundation includes a Ph.D. in Computer Science from the University of Technology Sydney. His research spans cutting-edge areas in artificial intelligence, with core expertise in Machine Learning and Computer Vision . Key focus areas include: Low-supervised paradigms (Weakly/Semi-/Self-Supervised Learning) Rationale-Guided Machine Learning systems Generative AI applications Machine Learning in Music and affective computing Fine-grained visual recognition in data-scarce environments Dr. Shu's publication record demonstrates consistent contributions to top-tier venues including CVPR, ECCV, IEEE Transactions on Multimedia, and Pattern Recognition. His work bridges theoretical advances in learning with practical applications in photo aesthetic assessment, semantic segmentation, and emotion recognition, showing particular strength in developing methods for low-data regimes and leveraging privileged information. He maintains active research collaborations through the School of Systems and Computing at UNSW Canberra, building on prior affiliations with AIML and the Centre for Augmented Reasoning. His current teaching responsibilities include ZEIT 2103 (Data Structures and Representation) for Semester 1, 2025.

Manfredo Atzoriمشاهده پروفایل

دانشیار

• Machine Learning
• Biomedical Data Analysis
• Computer Vision
• +۴ مورد دیگر

Manfredo Atzori serves as Associate Professor at the Department of Neuroscience, University of Padova, and has been a research scientist at the Institute of Information Systems of the University of Applied Sciences Western Switzerland (HES-SO Valais) since 2011. Education M.Sc. in Physics, University of Padova, 2006 Ph.D. in Bioengineering, University of Padova, 2009 Research Focus Dr. Atzori pioneers machine learning applications for biomedical multimodal data analysis. His work spans convolutional neural networks for surface electromyography (a field he helped establish globally in 2015), computer-aided cancer diagnosis in biopsies, and 3D-printed robotic prosthetic hands controlled via machine learning. He leads the Horizon 2020 ExaMode project for weakly-supervised knowledge discovery in digital pathology, integrating text and image analysis for medical diagnostics. Research Leadership As Scientific Coordinator of the Horizon 2020 ExaMode project (2019-present), he manages a seven-partner consortium advancing multimodal medical data analysis. He previously coordinated the Hasler Foundation's ProHand project for 3D-printed prosthetics and led the MeganePro Project (2016-2019), which investigated eye-hand coordination in robotic prostheses and neurocognitive effects of amputations using multimodal datasets. Since 2011, he has curated the Ninapro database—a globally utilized resource with thousands of users for robotic hand prosthesis research. Professional Recognition With over 80 peer-reviewed publications exceeding 2,000 citations, Dr. Atzori frequently presents as an invited speaker at international conferences. He serves on the editorial board of Scientific Data (Nature Publishing Group), reflecting his standing in computational biomedical research.

Paul Boniolمشاهده پروفایل

پژوهشگر

• Time Series Analytics
• Anomaly Detection
• Machine Learning
• +۲ مورد دیگر

Paul Boniol is a researcher at Inria, affiliated with the VALDA project-team—a collaboration between Inria Paris, École Normale Supérieure, and CNRS. His work focuses on time series analytics, anomaly detection, and machine learning applications. Ph.D. in Computer Science and Applied Mathematics (University of Paris, EDF R&D) Visiting Ph.D. at University of Chicago Education: Grenoble INP ENSIMAG Engineering School Research interests span: Unsupervised anomaly detection in large time series Time series management systems Machine learning for predictive maintenance Graph-based time series analysis Explainable AI for temporal data Recent publications emphasize advancements in: Weakly supervised anomaly localization Graph embedding techniques Model selection frameworks Interactive visualization tools Smart meter data analysis Scientific recognition: Paul Caseau Thesis Prize 2022 Lambdamu Congress Research-Industry Prize 2022 BDA & INFORSID Ph.D. Prizes 2022