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Xuesong LiView profile

Research Fellow

• Machine Learning
• 3D Reconstruction
• Object Detection
• +5 more

Xuesong Li is a Research Fellow at Australian National University (ANU) and CSIRO, focusing on machine learning and 3D scene reconstruction. His research integrates autonomous systems, dynamic/static scene modeling, and object detection through innovative approaches like Neural Radiance Fields (NeRF). Previously, he worked at Huawei (Shanghai) on autonomous driving technologies involving LiDAR point cloud analysis. He holds a Ph.D. in Robotics from UNSW Sydney (2020), with a thesis centered on object detection for intelligent robots. Education: Ph.D. in Robotics, UNSW Sydney (2020) Research Interests: Machine learning applications in 3D reconstruction (e.g., dynamic/static scenes, reflective objects), spatial transcriptomics analysis, crop biomass estimation via multi-modal datasets, and autonomous system development. His work bridges computer vision with real-world challenges in agriculture, robotics, and medical imaging. Publications Trends: Recent work emphasizes cross-domain knowledge transfer, long-tailed recognition, and real-world 3D reconstruction challenges. Notable contributions include the MMCBE dataset for crop biomass prediction and AGP-Net for spatial transcriptomics analysis. Awards: None explicitly mentioned. Advising & Collaborations: Actively recruiting visiting PhD and master’s students in machine learning and 3D reconstruction. Collaborates with institutions like CSIRO and Huawei, focusing on applications in autonomous systems and precision agriculture. Labs/Teams: Involved with ANU’s Division of Ecology and Evolution and CSIRO’s research groups, leveraging interdisciplinary approaches to tackle complex problems in robotics and environmental science.

Fabrice MarandolaView profile

Associate Professor

• Percussion
• Ethnomusicology
• Contemporary Music
• +5 more

Fabrice Marandola is an Associate Professor and Area Coordinator (Percussion) at McGill University’s Schulich School of Music. He holds a PhD in Ethnomusicology from the Sorbonne (2003) and has performed extensively with orchestras such as the National Orchestra of France and the New Philharmonic Orchestra of Radio France. His research focuses on percussion performance, ethnomusicological studies of Central African music, and the intersection of music with new technologies. He has been a key figure in Montreal’s contemporary music scene since 2005, co-founding the Sixtrum Percussion Ensemble and serving as Associate Director of the CIRMMT from 2009-2014. His awards include Prix Opus for innovative performances and a Senior Research Chair (Sorbonne-Universités, 2015-2016). His pedagogy emphasizes exploration and creativity, integrating gesture analysis and eye-tracking technologies. He directs the McGill Percussion Ensemble and has contributed to over 30 peer-reviewed publications, including studies on Bedzan Pygmy polyphonies and cross-cultural percussion techniques. Education: PhD in Musicology (Sorbonne, 2003) DEA in Music History (Sorbonne, 1994) Master’s in Music (Sorbonne, 1993) Conservatory Diplomas (Paris, 1991-1997) Research Interests: Marandola’s work spans percussion performance science , ethnomusicology of Central Africa , and music-technology interfaces . He pioneers methodologies combining experimental techniques with traditional fieldwork, particularly in analyzing Bedzan Pygmy vocal polyphonies and Zandé xylophones. His recent projects explore eye-hand synchronization in xylophone performance and haptics-based music education tools. Grants & Leadership: - Elected President of Le Vivier’s board (2014-2017) - Co-directed the Rythmopolis festival (2018-19) - Principal investigator on DiP-Core documentation project Labs & Teams: - CIRMMT (Centre for Interdisciplinary Research in Music Media and Technology) - Sixtrum Percussion Ensemble - Akrostick Percussion Duo - McGill Percussion Ensemble

• Clinical Sciences
• Neurosciences
• Cardiorespiratory Medicine and Haematology
• +3 more

Dr. David Owen is a Clinical Professor in Molecular Pharmacology and Experimental Medicine at Imperial College London’s Department of Brain Sciences (Faculty of Medicine). He leads the Clinical Studies division at the NIHR Imperial Clinical Research Facility, which bridges laboratory discoveries to clinical treatments. His research focuses on the mitochondrial 18kDa Translocator Protein (TSPO), exploring its roles in neurodegenerative diseases (e.g., MS, Alzheimer’s) and pulmonary vascular disorders. He also contributes to medical education via Pharmacology teaching at the Imperial Medical School and directs the MRES in Clinical Research program. Key affiliations include the Centre for Advanced Therapeutics, Immuno-Pathology Network, and Section of Neurology. He chairs critical committees: North West London Integrated Formulary (2.6M patients) and Subregional IVIG stewardship across 6 NHS Trusts. His work integrates clinical practice (20% time in acute medicine) with translational research, emphasizing TSPO as a potential drug target and imaging biomarker. Research interests span neuroinflammation, imaging technologies (PET, MRI), and therapeutic development. Recent work includes WAND multi-modal brain datasets, MS pathology analyses, and TSPO’s role in microglial biology. He collaborates on open-source neuroimaging tools like the Welsh Advanced Neuroimaging Database.

Mingbo CaiView profile

Assistant Professor

• Cognitive Neuroscience
• Brain-Inspired AI
• Reinforcement Learning
• +5 more

Mingbo Cai is an Assistant Professor in the Department of Psychology within the College of Arts and Sciences at the University of Miami. His research focuses on the intersection of human cognition and machine intelligence, leveraging computational models and experiments to study learning, decision making, and spontaneous thoughts. He develops tools like the Brain Imaging Analysis Kit (BrainIAK) to advance neuroimaging research. Key interests include reinforcement learning, Bayesian inference, and computational psychiatry. His lab explores how the brain constructs cognitive maps and processes spatial navigation through fMRI studies, emphasizing the role of predictive learning principles. Recent work includes studies on 3D object perception, unsupervised learning inspired by infant cognition, and decoding spontaneous thoughts via neuroimaging. Cai has published extensively on neural representation analysis, fMRI simulation, and transfer learning, with contributions to both theoretical neuroscience and practical machine learning applications. Current opportunities include PhD admissions (fall 2025) and a postdoc position focusing on fMRI studies of spontaneous thoughts. Collaborations leverage interdisciplinary approaches to bridge cognitive models with AI advancements, aiming to understand how neural systems achieve adaptive behavior through structured computations.

Odelia SchwartzView profile

Associate Professor

• Machine Learning
• Computational Neuroscience
• Medical Informatics
• +4 more

Odelia Schwartz is an Associate Professor in the Department of Computer Science at the University of Miami, College of Arts and Sciences. She also serves as Director of Undergraduate Studies for Computer Science and holds a secondary faculty appointment in Biology. Her research focuses on computational neuroscience, machine learning applications in healthcare and biology, and the intersection of artificial intelligence with visual and neural processing systems. Key research areas include machine learning analysis of medical signals (e.g., ECG for atrial fibrillation prediction), computational modeling of biological systems (e.g., endosymbiont population dynamics via microscopy image analysis), and hierarchical neural network models for visual cortex understanding. Her work integrates statistical methods with deep learning to bridge computational models and biological/neurological phenomena. Publications highlight applications in cardiology, neurotrauma recovery prediction, and visual cortex modeling. While no specific awards are listed, her contributions span interdisciplinary fields at the university and collaborative research institutions. Advising no listed students, but actively engages in graduate training through her faculty roles. No specific labs/teams are mentioned, though collaborations with medical and biological departments are evident.

Ubbo VisserView profile

Associate Professor

• Artificial Intelligence
• Robotics
• Semantic Web
• +5 more

Ubbo Visser is an Associate Professor and Director of Graduate Studies in the Department of Computer Science at the University of Miami's College of Arts and Sciences. He is also a Fellow of the CNSA. His research focuses on Artificial Intelligence, Robotics, and Semantic Web technologies, with applications in healthcare, criminal justice, and competitive gaming. He leads projects in RoboCup robotics competitions and has developed novel AI tools for cognitive testing (e.g., LASSI-BC) and digital health agents for behavioral interventions. Key research areas include ontology engineering (e.g., Drug Target Ontology, BioAssay Ontology), human-robot interaction, and multi-agent systems. His work integrates symbolic and sub-symbolic AI methods to address real-world challenges such as algorithmic fairness in facial recognition systems and dynamic motion control in humanoid robots. He has secured funding from NIH, NSF, and industry partners, and serves on editorial boards for advanced robotics and AI journals.

Tim WallView profile

Professor

• Popular Music Cultures
• Media Studies
• Cultural History
• +7 more

Tim Wall is Professor of Radio and Popular Music Studies at the Birmingham Centre for Media and Cultural Research , Birmingham City University. His career spans academic research, editorial leadership, and collaborative industry work with the BBC and independent music enterprises. Research areas: Popular music history, media theory, radio broadcasting, and cultural representation Key projects: Global Reggae Research Project, social media citizen journalism studies in Middle Eastern and North African contexts His publications and projects reveal a focus on media historiography , musical subcultures , and transnational broadcasting . Recent work examines Caribbean diasporic podcasts, early BBC jazz programming, and the political economy of digital media. Scientific Recognition : AHRC Knowledge Transfer Fellowship Editorial leadership: The Radio Journal Foundational work on Northern Soul and Reggae studies

Ren NgView profile

Professor

• Imaging Systems
• Computational Photography
• Optics
• +5 more

Ren Ng is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. He is affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) and the Visual Computing Lab (VCL). His research focuses on imaging, graphics, computer vision, human vision, and artificial intelligence, with notable contributions to computational photography, light field cameras, and neural radiance fields (NeRF). Ng previously founded and served as CEO of Lytro, Inc., which commercialized his Ph.D. research in light field photography. Ng holds a Ph.D., M.S., and B.S. in Computer Science and Mathematical Sciences from Stanford University. His awards include the ACM Doctoral Dissertation Award, Sloan Research Fellowship, and multiple industry recognitions such as MIT Tech Review's TR35 and Fast Company's 100 Most Creative People in Business. His research spans cutting-edge topics like color vision modeling, neural rendering, and lensless 3D imaging. Recent work explores stimulating photoreceptors for novel color perception and developing frameworks for human brain-based color vision emergence. Ng advises students in advanced imaging and computational techniques, with publications in top venues like Science Advances , ACM Transactions on Graphics , and NeurIPS . Key Contributions: Pioneered the Lytro light field camera technology Co-developed the NeRF framework for 3D scene representation Advanced computational imaging techniques (e.g., DiffuserCam) Grants & Labs: Active in NSF-funded projects and collaborates with institutions like Stanford and UC Berkeley labs. His research impacts fields from medical imaging to consumer electronics.

Hyung Jin ChangView profile

Associate Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +2 more

Dr Hyung Jin Chang is an Associate Professor in the School of Computer Science at the University of Birmingham . He earned a PhD in machine learning and computer vision from Seoul National University in 2013, following a BSc in Electrical and Computer Engineering from the same institution in 2006. Research focuses on artificial intelligence , computer vision , robotics , and human-robot interaction , particularly human-centred visual learning for hand/body pose estimation, gaze tracking, and action/internal state understanding. Key projects include EU FP7 GRANT 612139 , EU H2020 GRANT 643783 , and industry collaborations with Samsung Electronics and Samsung GRO Grant . Recent publications address advancements in unsupervised learning , domain adaptation , 3D hand-object interaction modeling , and multimodal gaze following in conversational scenarios. His work bridges theoretical AI research with practical applications in vision-based robotics and medical image analysis, supported by both academic and industrial grants.

• Computer Vision
• Deep Learning
• 3D Point Cloud Processing
• +7 more

Yusuf Hüseyin Şahin is an Assistant Professor in the Department of Computer Engineering at Istanbul Technical University, Faculty of Computer and Informatics. He earned all his academic degrees—B.Sc., M.Sc., and Ph.D.—from the same institution in Computer Engineering. His research lies at the intersection of computer vision, deep learning, and 3D data processing, with applications in medical imaging, architectural heritage, and drone-based vision systems. B.Sc., M.Sc., Ph.D. in Computer Engineering, Istanbul Technical University His primary research interests include 3D point cloud processing, deep learning, image segmentation, adversarial attacks, and medical image analysis. He has published extensively on these topics, particularly focusing on point cloud registration, segmentation, and classification using neural networks. His recent work explores uncertainty modeling, active learning, and generative models for both synthetic data creation and real-world applications. The trend in his publications from 2017 to 2024 shows a clear progression from foundational work in CNN-based 3D classification and cerebral vessel analysis to advanced topics such as dynamic graph networks, conformal prediction, and heritage digitization. His work bridges theoretical machine learning with practical applications in healthcare and cultural preservation. He is currently leading a research project titled "Konformal Tahmin ile Sıcaklık Tahmin Modellerinde Doğruluğun Arttırılması" (Improving Temperature Prediction Accuracy Using Conformal Forecasting), funded under the SRP program from 2025 to 2026. This indicates an expanding interest in predictive modeling and uncertainty quantification. While no scientific awards are listed in the provided texts, his h-index of 5 and 293 citations on Scopus reflect an active and growing research profile. Dr. Şahin teaches undergraduate courses such as Data Structures (BLG 223E) and Object-Oriented Programming (BLG 252E). He has no listed advisees or thesis supervision records. He is part of a collaborative research network involving Gozde Unal and other researchers in medical and architectural computer vision. His lab activities appear to focus on deep learning for 3D data, with emphasis on robustness, efficiency, and real-world deployment.

• Cognitive Psychology
• Gerontology
• Psychology of Communication
• +12 more

Louise Phillips is a Professor and Chair in Psychology at the University of Aberdeen , affiliated with the School of Psychology . Her research focuses on adult aging, cognition, emotion, and social functioning, particularly in the context of intergenerational communication and neurocognitive changes. Education : BSc (Hons) in Psychology (University of Edinburgh, 1990), PhD in Psychology (University of Manchester, 1993) Professional Affiliations : Vice-President of the European Cognitive Aging Society (EUCAS), Member of the British Psychological Society, Member of the Experimental Psychology Society Internal Roles : Course Coordinator for PS3011 Psychological Assessment, Steering Group Member for Aberdeen Birth Cohort and Aberdeen Children of the 1950s Her research investigates age-related differences in emotional expression and interpretation , gaze following , theory of mind , and prospective memory . She explores how cognitive decline, motivation, and generational changes influence social communication. Her interdisciplinary work integrates psychology with genetics, brain imaging, and health sciences. The recent publications highlight trends in aging and social cognition , emotion regulation , AI bias in human interaction , and epigenetic influences on brain aging . Her work spans cognitive psychology, gerontology, affective neuroscience, and interdisciplinary aging research. Scientific Funding and Grants: Leverhulme Trust (2023–2025): Theory of Mind in older adults – £188,476 Dunhill Medical Trust (2022–2026): Interventions to improve quality of life in old age – £199,564 Newton-Ungku Omar Fund (2018–2020): Social cognition in UK-Malaysia older adults – £128,461 ESRC (2017–2021): Adult aging and social attention – £500,492 ESRC/BBSRC (2015–2018): Imprinting methylation and later cognition – £446,636 She mentors PhD students through funded projects and leads collaborative research. She teaches Lifespan Development , Psychological Assessment , Emotion and Aging in Social Cognition , and The Science of Aging . She leads the Aging, Cognition and Emotion Lab , which conducts experimental and interdisciplinary research on aging and social communication.

• Geospatial Digital Twins
• Virtual Geographic Environments
• Remote Sensing
• +7 more

Jun Zhu is a professor at the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China. His research is centered on geospatial digital twins, virtual geographic environments, and intelligent visualization for disaster risk management, with strong interdisciplinary work in AI, remote sensing, and VR-based simulation. His research interests include: Geospatial Digital Twins Virtual Geographic Environments AI for Remote Sensing 3D and VR-based Disaster Visualization Knowledge Graphs in GIS Public Risk Communication The recent articles (2023–2025) demonstrate a strong trend in integrating large language models, knowledge graphs, and deep learning with geospatial data to build intelligent, interactive, and immersive systems for infrastructure monitoring, disaster simulation, and public engagement. His work emphasizes data-knowledge fusion, human-centered visualization, and real-world applicability in urban and environmental contexts. Scientific Awards: No awards listed in the provided text. Advising and Grants: While Jun Zhu has extensive collaboration with researchers such as Weilian Li, Qing Zhu, Yakun Xie, and Jianbo Lai, and appears to lead research projects, there is no explicit mention of student advising, grant funding, or project titles in the provided data. Labs and Teams: Jun Zhu is likely part of a research group focused on digital twins and geospatial AI at Southwest Jiaotong University, collaborating closely with colleagues in geoinformatics and remote sensing, though specific lab names are not mentioned.

Homa AlemzadehView profile

Associate Professor

• Cyber-Physical Systems
• Dependable Computing
• Artificial Intelligence and Machine Learning
• +5 more

Homa Alemzadeh is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Virginia, with a courtesy appointment in Computer Science. She is affiliated with the UVA Link Lab, focusing on Cyber-Physical Systems (CPS). Her research bridges dependability, security, and data science, emphasizing resilient CPS design for medical devices, surgical robots, and autonomous systems. She holds a Ph.D. from the University of Illinois and B.Sc./M.Sc. from the University of Tehran. Education : Ph.D., Electrical and Computer Engineering, University of Illinois (2016) M.Sc., Computer Engineering, University of Tehran (2008) B.Sc., Computer Engineering, University of Tehran (2005) Research Interests : Dependable and Secure Computing, AI/ML, Robotics, Smart Health, and CPS resilience. Her work addresses safety and security in medical devices, autonomous systems, and emergency response technologies. Recent projects include runtime safety assurance in medical CPS and strategic resilience in autonomous driving. Awards : 2022 NSF CAREER Award, 2017 William C. Carter Dissertation Award, and 2014 J. Maxwell Chamberlain Memorial Paper Award. Grants & Projects : NSF CAREER Grant on medical CPS safety, NIST-funded AR for emergency services, and collaborative NSF research on autonomous vehicle resilience. Active collaborations span academia and industry, including work with IBM Research. Labs & Teams : Link Lab (UVA), focusing on multi-disciplinary CPS research. Supervises a dynamic team of PhD and undergraduate researchers, emphasizing hands-on projects in embedded systems and CPS validation.

• Applied Artificial Intelligence
• Machine Learning
• Robotics and Autonomous Systems
• +6 more

Dr. Yining Hua is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he is actively involved in research and teaching in computing science. He also holds an honorary researcher position at the University of Glasgow. His academic journey includes a B.Eng. in Information Security from Northeastern University, China, and a PhD in Computer Science from Loughborough University, UK. Prior to joining Aberdeen, he served as a postdoctoral researcher at the University of Glasgow and as a lecturer at the University of Roehampton and the University of Lincoln. His research focuses on cutting-edge areas in computing, including: Applied Artificial Intelligence and Machine Learning Robotics and Autonomous Systems Computer Vision Information Security and Blockchain Future Computer Networks and Internet of Things Dr. Hua's recent publications reveal a strong trend in applying AI to medical imaging, autonomous driving, and IoT systems. His work spans biomedical informatics, energy-efficient EV systems, and domain adaptation techniques for real-world deployment. He frequently publishes in high-impact IEEE journals and conferences, demonstrating consistent contributions to both theoretical and applied AI. His scientific contributions are reflected in numerous peer-reviewed articles, though no specific awards or fellowships are mentioned in the provided text. Dr. Hua is actively mentoring and accepting new PhD students in computing science, indicating a vibrant research group. He has not received any named grants in the text, but his publication output suggests active research funding. He is affiliated with research teams working on AI for healthcare, autonomous systems, and secure distributed networks, particularly within the University of Aberdeen’s computing science environment.

• Machine Learning
• Computer Vision
• Medical Image Analysis
• +6 more

Matthew Blaschko is a Senior Lecturer BOF at KU Leuven, affiliated with the Department of Electrical Engineering (ESAT) within the Faculty of Engineering Sciences. He directs the KU Leuven ELLIS unit and serves as a fellow in the ELLIS Health program, part of the European Laboratory for Learning and Intelligent Systems. As a Core PI in the Flanders AI Research Program, he leads work packages for Decision Support Systems and Medical Imaging. He is also a member of the KU Leuven Institute for Artificial Intelligence and co-leads the working group on Machine Learning and Data Science. Habilitation (HDR) from École Normale Supérieure de Cachan Newton International Fellow at University of Oxford Dr. rer. nat. from Max Planck Institutes Tübingen (awarded by Technische Universität Berlin) M.S. from University of Massachusetts Amherst B.S. from Columbia University Blaschko's research focuses on machine learning, computer vision, and medical image analysis, with particular expertise in uncertainty quantification in deep neural networks and trustworthy AI for healthcare applications. His work bridges theoretical foundations with practical implementations, developing methods for calibration, uncertainty estimation, and efficient model deployment. He has made significant contributions to neural network architectures, loss functions, and evaluation metrics for medical imaging tasks, with applications spanning Alzheimer's disease research, surgical phase recognition, and ophthalmic image analysis. His recent publications reveal a strong emphasis on calibration methods, uncertainty quantification, and medical applications of AI. The research spans diverse areas including Alzheimer's disease analysis, Bayesian optimization, novel view synthesis, knowledge extraction from text, and surgical phase recognition. Many papers focus on improving model reliability and safety for healthcare applications, reflecting his commitment to developing trustworthy AI systems that can be deployed in clinical settings. Best Student Paper Award, ECCV 2008 Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award Best paper award, CVPR 2008 Best paper award, Benelearn 2014 Blaschko actively mentors numerous PhD and MSc students, with current advisees working on topics ranging from uncertainty in deep neural networks to medical image analysis and AI for healthcare. He leads multiple significant research projects including 'onzekerheid in diepe neurale netwerken' (2025-2029), 'Trustworthy AI for Medical Image Analysis and Computer Vision' (2025), and 'Van metingen naar biomarkers in medische beeldanalyse' (2024-2028). His research is supported by the Flanders AI Research Program and other substantial funding sources. As director of the KU Leuven ELLIS unit and a key member of the KU Leuven Institute for Artificial Intelligence, Blaschko leads a vibrant research team focused on machine learning and data science. His laboratory develops cutting-edge AI technologies with practical applications, particularly in healthcare. Technology from his research has been incorporated into MONA, software for ophthalmic image analysis, demonstrating the real-world impact of his work.