Find Academic Supervisors

• Medical Imaging
• Computer Vision
• Deep Learning
• +4 more

Dr Abdullah Nazib is a Research Fellow at Queensland University of Technology (QUT) in the Faculty of Engineering, School of Electrical Engineering & Robotics. His research focuses on applying advanced computational techniques to medical imaging challenges, particularly in cancer detection and treatment planning. His research interests include: Medical image registration and segmentation Deep learning architectures for healthcare applications 3D medical image analysis Prostate cancer detection and grading Organ segmentation for radiation therapy Radiomics-based diagnostic systems Analysis of Dr Nazib's publication history reveals a clear evolution from general computer vision research toward specialized medical applications. His recent work demonstrates significant contributions to uncertainty quantification in medical image segmentation and multimodal learning approaches for diagnostic imaging. The 2024 publications particularly highlight his focus on clinically relevant applications with immediate potential impact on cancer diagnosis and treatment planning. His collaborative research network includes strong partnerships with Dr Fookes, Dr Perrin, and other members of QUT's biomedical imaging group, reflecting an interdisciplinary approach that bridges computer science, engineering, and clinical medicine to develop practical AI solutions for healthcare challenges.

Shuyan LiView profile

Lecturer

• Unsupervised Learning
• Digital Twins
• Video Understanding
• +4 more

Shuyan Li is a Lecturer at the School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast. Her research focuses on foundational computer vision methods and their healthcare applications, including unsupervised learning, multi-modal learning, and medical imaging analysis. She actively mentors early-career researchers through the Cambridge Trinity College Postdoctoral Mentorship Program and serves as Director of the Tsinghua Alumni Association (UK) and Secretary-General of the UK Association of Distinguished Young Scholars. Education Background: While specific degree details are not explicitly listed, Dr. Li has received prestigious awards such as the First Prize Scholarship (Tsinghua University, 2020) and the National Scholarship (Ministry of Education, PRC, 2013), indicating a strong academic foundation. Research Interests: Central themes include unsupervised learning, digital twins for construction and healthcare, video understanding, representation learning, and domain adaptation. Her work bridges theoretical advancements and practical applications, such as medical image translation and point cloud-based building digitization. Awards and Recognition: Key achievements include the Athena Postdoctoral Fellowship (NSF AI Research Center), Forbes’ Top 100 Most Influential Chinese (2024), and the Excellent Doctorate Dissertation Award (2023). She is also a Guest Editor for Innovation and Technology of Computer Vision . Advising & Grants: Currently supervising PhD students Ben Redden (UK) and Shurui Xu (China). She offers multiple funded PhD opportunities, including EPSRC and CSC scholarships, and collaborates with institutions like Cambridge and UCL. Labs & Collaborations: Active in interdisciplinary projects involving digital twin construction, medical AI, and point cloud analysis. Recent collaborations include work with the University of Cambridge on digital construction modules and Newcastle University on AI-driven data analysis.

• Statistical Learning
• High-Dimensional Data Analysis
• Network Statistics
• +7 more

Charles Bouveyron is a Full Professor of Statistics at Université Côte d'Azur , Nice, France, and holds a Chair in Artificial Intelligence. He serves as Director of the Institut 3IA Côte d’Azur and leads the Inria research team MAASAI on Statistical Learning and Artificial Intelligence. He is an associate editor for The Annals of Applied Statistics and founded the Statlearn workshops . Research Interests: Statistical learning in high dimensions Learning on networks and functional data Deep latent variable models Adaptive learning with uncertain labels Applications in Medicine, Image Analysis, Astrophysics, and Humanities Notable Contributions: Developed multiple R packages including HDclassif , FisherEM , and FunLBM . Created the Linkage.fr platform for network analysis with textual edges. PhD Students: Current: Seydina Niang (Deep Generative Models), Kilian Burgi (Marine Diversity Monitoring), Baptiste Pouthier (Multimodal Learning) Former: Giulia Marchello (Dynamic Networks), Rémi Boutin (Network Analysis), Dingge Liang (Recommender Systems), Nicolas Jouvin (Latent Variable Models), Alexandre Saint-Dizier (Image Aggregation), Warith Harchaoui (Optimal Transport), Pierre-Alexandre Mattei (Sparse Clustering), Rawya Zreik (Temporal Networks), Anastasios Bellas (Anomaly Detection), Camille Brunet (Sparse Clustering) Contact: Email: charles.bouveyron@univ-cotedazur.fr / charles.bouveyron@inria.fr Postal: Equipe Maasai, Inria Sophia Antipolis, 2004 route des Lucioles, 06902 France

Arjun P. AthreyaView profile

Associate Professor

• Artificial Intelligence
• Digital Health
• Precision Medicine
• +6 more

Arjun P. Athreya, Ph.D., M.S., is an Associate Professor at Mayo Clinic's Department of Molecular Pharmacology and Experimental Therapeutics , specializing in Artificial Intelligence and Digital Health for precision medicine. With a background in Electrical and Computer Engineering, he bridges engineering ecosystems with clinical practice to optimize treatment selection and management through data integration from genomics , wearables , and electronic health records . Ph.D., University of Illinois Urbana-Champaign (Electrical and Computer Engineering) M.S., Carnegie Mellon University (Computer Software Engineering and Electrical and Computer Engineering) B.E., RV College of Engineering, VTU (Electrical and Telecommunication Engineering) His research spans precision care for chronic conditions like major depressive disorder and bipolar depression , digital health technologies for pediatric mental health , and provider well-being solutions. He leads projects on multi-omic biomarker discovery for diseases such as primary sclerosing cholangitis and pancreatic ductal adenocarcinoma , integrating AI with clinical workflows. Dr. Athreya’s work emphasizes participatory design in decentralized digital health studies and ethical AI applications. Current projects include National Institute of Nursing Research -funded studies on smartwatch adherence for burnout prediction and National Science Foundation initiatives to implement patient-specific therapy profiles in EHRs for genomic medicine. His research team collaborates with psychiatrists, gastroenterologists, and bioethicists across Mayo Clinic. Scientific Awards: American Society for Clinical Pharmacology and Therapeutics Presidential Trainee Award (2017-2019) Jason Morrow Memorial Award (2017) IEEE Best Paper Awards (2016, 2017) Rambus Computer Engineering Fellowship (2017) Carnegie Mellon Dean’s Fellowship (2011-2013)

• Cardiac Electrophysiology
• Deep Learning in Medicine
• Computational Modeling
• +3 more

Sofia Monaci is a researcher specializing in computational cardiology and medical AI applications. Her work focuses on integrating deep learning with physics-based models to improve ventricular tachycardia localization and ablation planning using electrocardiogram (ECG) data from implanted devices. Holds a PhD in Biomedical Engineering/Physiology (2023) Thesis supervised by Prof. Mark Bishop and Prof. Andrew King Key research areas include cardiac electrophysiology modeling, AI-driven diagnostics, and computational tools for arrhythmia treatment. Her publications emphasize automated localization of ventricular tachycardia foci and optimizing ablation strategies through multi-modal data fusion. Publications (2020-2022) consistently address improving arrhythmia management via advanced signal processing and machine learning techniques applied to clinical datasets from implanted cardiac devices.

• Face Recognition
• Biometrics
• Synthetic Data Generation
• +4 more

Ivan DeAndres-Tame is an Assistant Professor in the Department of Signal Theory and Communications at the School of Engineering, Universidad Autónoma de Madrid. His research focuses on advancing biometric systems with particular emphasis on face recognition and behavioral biometrics using cutting-edge computer vision and machine learning techniques. His research interests span face recognition systems, synthetic data generation for biometric applications, explainable AI in biometric contexts, and keystroke verification systems. DeAndres-Tame has made significant contributions to the field through his leadership in major international challenges including the FRCSyn Challenge at CVPR and WACV, and the Keystroke Verification Challenge at IEEE BigData. His work bridges theoretical advances with practical applications in biometric security systems. Analysis of his recent publications reveals a strong focus on evaluating and improving face recognition systems using synthetic data, with a growing interest in the intersection of large language models and biometric systems. His research demonstrates consistent innovation in benchmarking methodologies and evaluation frameworks for biometric systems. Throughout his career, DeAndres-Tame has collaborated extensively with leading researchers in the biometrics community, including Ruben Tolosana, Rubén Vera-Rodríguez, Aythami Morales, Julian Fiérrez, and Javier Ortega-Garcia, contributing to the advancement of biometric technologies through both theoretical and applied research.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +4 more

Prof. Dr. Mario Fritz is a leading academic at the CISPA Helmholtz Center for Information Security and Saarland University , with a focus on Trustworthy Information Processing . His work sits at the intersection of AI, Machine Learning, Security, and Privacy, addressing challenges in foundation models, health data, and adversarial robustness. Key Projects : ELLIOT (Multi-Modal Foundation Models), ELSA (Secure AI), PriSyn (Synthetic Health Data), AIgency (Generative AI in Cybersecurity), HMSP (Medical Security) Research Themes : AI/ML security, privacy-preserving techniques, causal modeling, healthcare applications, and ethical AI Recent Publications : Focus on LLM sampling, causal inference, model stealing, and privacy-aware document analysis Collaborations : European Laboratory for Learning and Intelligent Systems (ELLIS), BMBF-funded initiatives, GHGA (Human Genome Archive) Academic Leadership : As a Professor , he coordinates large-scale EU projects and mentors emerging researchers in AI ethics and security.

• Machine Learning
• Information Theory
• Deep Learning
• +4 more

Giulio Franzese is an Assistant Professor in the Data Science department at EURECOM. His research focuses on generative models, information theory, deep learning, and their applications in telecommunications and AI-driven networks. He has contributed to advancements in diffusion processes, multi-modal data alignment, and 6G network architectures. Notable projects include the ADROIT6G initiative for next-generation networks and methodologies like INFO-SEDD for scalable information metric estimation. His work spans theoretical foundations (e.g., infinite-dimensional generative models) and applied systems (e.g., GNSS for rail transportation). His publications reflect interdisciplinary strengths in machine learning, statistical theory, and engineering applications. He holds a robust publication record with over 30 articles since 2014, including impactful works on uncertainty quantification in deep learning and entropy-based optimization. Key Research Themes: Generative Diffusion Models Information-Theoretic Metrics AI for 6G Networks Selected Projects: ADROIT6G: Distributed AI-Driven 6G Architecture RFMI: Text-to-Image Alignment Framework

Laura ErmertView profile

Researcher

• Seismic Interferometry
• Ambient Noise Monitoring
• Computational Seismology
• +3 more

Dr. Laura Ermert is an Established Researcher at the Swiss Seismological Service (SED) of ETH Zurich. Her work focuses on computational seismology, ambient noise monitoring, and seismic interferometry. She holds a PhD from ETH Zurich (2013–2018) under Prof. Andreas Fichtner, followed by postdoctoral positions at the University of Washington, Harvard University, and the University of Oxford. Her research explores shallow structure monitoring, basin resonances, and the influence of ocean microseism sources on ambient noise cross-correlation amplitudes. She has contributed to projects like the DEEP initiative for geothermal reservoir modeling and collaborated on Mexico City basin subsidence studies funded by the Packard Fellowship. Dr. Ermert co-developed Python tools like noisi for ambient noise analysis and pioneered methods for ambient seismic source inversion. Her studies include modal analysis of Alpine valley resonances and global-scale ambient noise inversion. She actively promotes gender equity in seismology through collaborative initiatives.

Sean BechhoferView profile

Professor

• Semantic Web
• Ontologies
• Knowledge Representation
• +3 more

Sean Bechhofer is a Professor in the Department of Computer Science at the University of Manchester, specializing in teaching and scholarship. His research focuses on Semantic Web technologies, ontologies, knowledge representation, and linked data. He has held roles including Discipline Head of Education for Computer Science (2019–2023) and contributed to projects like the Information Management Group and WF4EVER initiative. Education: BSc Mathematics from the University of Bristol (1988), postgraduate work in Category Theory at the University of Manchester. Research interests include ontology engineering, semantic web languages, and workflow preservation. He co-developed the OWL API and contributed to standards like SKOS and the Research Object Model. Teaching responsibilities include courses on Operating Systems, Computational Thinking, and Algorithms. Key projects include the SCAPE preservation framework and myExperiment workflow repository. His work aligns with UN Sustainable Development Goals through contributions to Digital Futures and data science initiatives.

• Neonatal Neurology
• Electroencephalography (EEG)
• Brain Development
• +4 more

Anneleen Dereymaeker is an Assistant Professor at the KU Leuven Faculty of Medicine , affiliated with the Department of Development and Regeneration . Her work focuses on neonatal neurology , EEG analysis , and neurodevelopmental outcomes in preterm infants. Her research spans Developing automated neuromonitoring systems (NEONAID project) Investigating neurovascular coupling in hypoxic-ischemic encephalopathy Advancing EEG-based predictive models for brain development Recent publications highlight her contributions to neonatal brain monitoring , with a focus on Machine learning for artifact detection Microstate analysis of preterm brain maturation Systematic reviews on cerebral oxygenation She teaches communication courses in specialized medical fields and serves on senior academic committees within the Faculty of Medicine.

Xiaolin WuView profile

Professor

• Image Processing
• Multimedia Coding and Communication
• Computer Vision
• +7 more

Dr. Xiaolin Wu is a Professor in the Department of Electrical & Computer Engineering at McMaster University's Faculty of Engineering. With expertise in image processing, multimedia coding, computer vision, and artificial intelligence, he has made groundbreaking contributions to visual/multimedia computing and communication. His research has resulted in over 350 publications and four patents, including the renowned CALIC algorithm for lossless image coding and L3 codec for digital cinema. B.Sc. from Wuhan University (1982) Ph.D. from University of Calgary (1988) Dr. Wu's research spans critical areas such as image restoration (demosaicing, denoising, superresolution), multimedia coding (JPEG, wavelet transforms), and information display (Temporal Psychovisual Modulation). His work on TPVM, featured in MIT Technology Review, redefines display technology by leveraging human vision properties for multi-user VR/AR experiences. Recent publications focus on deep learning applications for image/video restoration, including multi-modality approaches and l∞-constrained compression. His algorithms, such as fast color quantizers and compressive sensing recovery methods, have been widely adopted in industries like medical imaging and digital cinema. Dr. Wu also holds an NSERC Senior Industrial Research Chair and has collaborated with global tech leaders including Microsoft, Nokia, and Huawei. IS&T/SPIE Best Paper Award UWO Distinguished Research Professor Award McMaster Distinguished Engineering Professor Award IEEE Fellow As an educator, Dr. Wu teaches courses in image processing, discrete methods, numerical analysis, and parallel computing, emphasizing algorithm design, data structures, and real-time systems. His work bridges academic research with industrial applications, ensuring practical impact across biomedical imaging, digital security, and smart display technologies.

Gita AlaghbandView profile

Professor

• High-Performance Computing
• Artificial Intelligence
• Machine Learning
• +7 more

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.

Luis Antonio Belanche MuñozView profile

Professor

• Machine Learning
• Kernel Methods
• Neural Networks

Luis Antonio Belanche Muñoz is a Professor at the Department of Computer Science , Faculty of Informatics of Barcelona (FIB) , Universitat Politècnica de Catalunya (UPC) . He is affiliated with research groups SOCO - Soft Computing and IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group . His career spans over 25 years, with 216 documented activities. His research focuses on Machine Learning , Kernel Methods , and Neural Networks . He has pioneered techniques in feature selection, similarity measures, and hybrid models connecting deep learning with kernel methods. His work applies to diverse domains including finance, microbiology, cancer diagnostics, and environmental engineering. Recent publications highlight trends in kernel matrix analysis using entropy, microbiome data integration , and drug resistance prediction in HIV. Earlier work includes knowledge-based systems for wastewater treatment diagnostics and educational technologies for MOOC environments. He has collaborated with 75+ researchers across UPC's research network, contributing to projects funded under Spain's State Research Plans and Catalonia's RIS3CAT strategy. His 2011 thesis on Feature selection in brain tumor MRS data demonstrates interdisciplinary applications.

• Data Analysis
• Statistical Modeling
• Demand Modeling
• +3 more

Lidia Montero Mercade is a Professor at the Polytechnic University of Catalonia (UPC) within the Department of Statistics and Operations Research and affiliated with the Barcelona School of Informatics (FIB). She leads the IMP - Information Modeling and Processing research group and is a core member of inLab FIB. Her academic work spans data science, statistical modeling, demand forecasting, and simulation for sustainable urban mobility. Her research focuses on transport analytics , integrating spatiotemporal sequence analysis gender equality in mobility patterns dynamic origin-destination matrices simulation-optimization techniques smart city policy evaluation ICT data fusion for transport . Recent publications emphasize fragmented mobility, urban sustainability, and data-driven equity assessments. Key trends in her 15 most recent articles include gender-specific travel behavior , activity-based sequence modeling , and smart mobility policy evaluation using simulation tools. Awards highlight social inclusion (Premi UPC) and excellence in transport research (Premi Abertis). She contributes to academic governance as a scientific committee member of conferences like Euro Working Group on Transportation and SEFI.