Find Academic Supervisors

• Remote Sensing
• Forest Management
• Biodiversity Monitoring
• +4 more

Nicholas Charles Coops is a Professor and Department Head of Forest Resources Management at the University of British Columbia's Faculty of Forestry. He holds the UBC Chair in Forest Management and serves as Director of the Masters for Geomatics for Environmental Management program, while co-directing the Master of Sustainable Forest Management. Integrated Remote Sensing Studio (IRSS) Founder Canadian Forest Service Collaborator Developer of 3PGS and CAN-TG forest growth models Research Interests focus on: Linking vegetation pigments/structure to remote sensing data LiDAR applications for forest height and DEM modeling Dynamic Habitat Index (DHI) for biomass distribution analysis Machine learning for forest inventory and biodiversity monitoring Climate change impacts on boreal and temperate forests UAV-based phenotyping for forest management Recent Publications demonstrate expertise in LiDAR integration, satellite-based forest monitoring, and ecological modeling across diverse scales from individual trees to national datasets. Scientific Awards include: Seven consecutive Highly Cited Researcher designations (2019-2024) Canada Research Chair in Remote Sensing Royal Society of Canada Fellowship (2022) Marcus Wallenberg Prize (2020) UBC Killam Research Prize (2020) Canadian Remote Sensing Society Gold Medal (2020) Integrated Remote Sensing Studio leads collaborative projects with government agencies, industry partners, and international institutions to advance remote sensing applications in forestry, biodiversity conservation, and climate change mitigation.

Gerd BrunnerView profile

Associate Professor

• Peripheral Arterial Disease
• Magnetic Resonance Imaging
• Muscle Perfusion
• +3 more

Gerd Brunner serves as Associate Professor in both the Department of Medicine (Division of Cardiology) and Department of Cell and Biological Systems, focusing on advanced cardiovascular imaging research. His work bridges clinical cardiology with biomedical engineering through innovative MRI applications. His primary research interests include: Peripheral arterial disease pathophysiology and diagnostics Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) techniques Skeletal muscle perfusion quantification in vascular disease Machine learning integration for medical image analysis Diabetic ulcer microvascular assessment Cardiovascular biomarker development Recent publications demonstrate a clear evolution toward AI-enhanced imaging, with 2023-2025 works heavily featuring convolutional neural networks, texture analysis, and quantitative mapping techniques. This shift reflects strategic adaptation to computational advances in vascular diagnostics, particularly for peripheral artery disease assessment where traditional metrics show limitations. Dr. Brunner has secured significant NIH funding through the National Heart, Lung, and Blood Institute: Microvascular Perfusion Assessment of Ischemic Diabetic Ulcers Using MRI (2015-2020): Developed MRI protocols for perfusion quantification in diabetic foot complications Magnetic Resonance Imaging Based Calf Muscle Perfusion Assessment (2017-2018): Established DCE-MRI methodologies for symptomatic peripheral artery disease evaluation His collaborative network spans vascular surgery, endocrinology, and biomedical engineering, with research directly contributing to UN Sustainable Development Goal 3 (Good Health and Well-being) through improved cardiovascular diagnostics.

Jui-Kai WangView profile

Assistant Professor

• Ophthalmic Image Analysis
• Machine Learning
• Deep Learning
• +2 more

Jui-Kai Wang (Ray) is an Assistant Professor in the Department of Ophthalmology at UT Southwestern, joining in 2024. His research focuses on ophthalmic image analysis using machine learning and deep learning techniques across modalities like OCT, OCTA, LSFG, and color fundus photography. PhD in Electrical and Computer Engineering (2016), The University of Iowa MS in Computer and Communication Engineering, National Cheng Kung University BS in Electronic Engineering, Southern Taiwan University of Science and Technology Research spans ocular disease diagnosis , neurodegenerative imaging , and radiation therapy effects , leveraging multimodal imaging and advanced analytics. Recent work includes OCT layer segmentation, papilledema classification, and vascular biomarker discovery. Scientific contributions recognized through the Xtreme Research Award (Heidelberg Engineering, 2021) and ARVO travel grant (2019). Collaborative projects include NIH-funded research on glaucoma and radiation-induced vision loss.

Thomas WilkesView profile

Lecturer

• Volcanology
• Remote Sensing
• Machine Learning
• +3 more

Dr. Thomas Wilkes is a Lecturer in Volcanology at the School of Geography and Planning, University of Sheffield. He specializes in low-cost remote sensing technologies and machine learning applications for volcanic monitoring. His research has been instrumental in developing the PyCamPermanent software library for SO 2 camera systems, widely used by researchers globally. Education: MSci in Environmental Geoscience, University of Bristol (2014) PhD in Volcanology, University of Sheffield (2015-2018) Research Interests focus on: Volcanology and volcanic degassing Development of low-cost remote sensing instruments Machine learning for volcanic plume analysis Optical design and Python-based software engineering Applications of smartphone sensors in volcanic monitoring Recent Publications highlight advancements in permanent SO 2 camera installations, deep learning for plume segmentation, and low-cost UV spectrometers. His work bridges geoscience and engineering, emphasizing accessible technology for global volcanic surveillance. Scientific Awards: Leverhulme Early Career Fellowship Teaching: Dr. Wilkes contributes to course delivery in Applied Volcanology and other modules at the University of Sheffield, integrating fieldwork and lab-based instruction.

Ti BaiView profile

Lecturer

• Radiotherapy
• X-ray CT Imaging
• Medical Image Analysis
• +3 more

Ti Bai, Ph.D., is a Medical Physics Resident PGY-2 at UT Southwestern Medical Center , affiliated with the Department of Radiation Oncology and its Medical Artificial Intelligence and Automation (MAIA) Lab . He previously served as a postdoctoral researcher (2019–2021) and was promoted to Lecturer in 2021. Dr. Bai’s research focuses on low-dose X-ray CT imaging , medical image analysis , and deep learning algorithms for healthcare applications. His work bridges artificial intelligence and radiation oncology to improve clinical workflows and treatment accuracy. Key publication trends include advancements in CT image denoising , automated segmentation , and real-time tumor localization . His 2023–2022 articles highlight self-supervised learning , multi-spectral CT enhancement , and AI-driven contour editing for radiotherapy. Dr. Bai earned his Ph.D. at Xi'an Jiaotong University (2017), specializing in low-dose CT reconstruction , followed by two years at Baidu Research 's Institute of Deep Learning (2017–2019).

Claudio VinegoniView profile

Assistant Professor

• Optical Imaging
• Biomedical Imaging
• Machine Learning
• +5 more

Claudio Vinegoni is an Assistant Professor of Radiology at Harvard Medical School and Massachusetts General Hospital, with over three decades of expertise in optical imaging modalities and biomedical data analysis. His research focuses on system-level biological interactions in disease states like cancer and cardiovascular disorders, combining advanced microscopy with machine learning techniques. His lab develops novel optical imaging systems for in vivo and ex vivo applications, including fluorescence anisotropy , two-photon microscopy , and tensor imaging for whole-organ analysis. Recent work emphasizes deep learning integration with fluorescence data processing. Publications span Nature and Science journals, covering Drug-target engagement quantification Cardiac microstructure visualization High-resolution atherosclerosis imaging GAN-based microscopy enhancement His academic background includes a PhD and MS in Physics from University of Geneva and Universita di Trento, with thesis work on Raman spectroscopy and telecom fiber metrology.

Ismini LourentzouView profile

Assistant Professor

• Multimodal Machine Learning
• Vision-Language Models
• Interactive AI Agents
• +6 more

Ismini Lourentzou is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, leading the Perception and LANguage (PLAN) Lab. Her research focuses on multimodal machine learning, vision-language models, and human-centric embodied AI systems. School of Information Sciences, University of Illinois Urbana-Champaign Former Assistant Professor, Virginia Tech Department of Computer Science (2021-2023) Research Interests: Multimodal learning with limited supervision Interactive AI agents and human-agent communication Vision-language navigation and task-driven conversational systems Self-supervised learning and grounding vision-language models Applications in healthcare, misinformation detection, and manufacturing Scientific Contributions: Recipient of DARPA, NSF, and Amazon funding Developed frameworks for vision-language co-segmentation and embodied navigation Keynote speaker at ACM PETRA 2022 on supervision signals in healthcare AI Awards & Recognitions: Best Vice Co-Chair at IEEE BigData 2023 Outstanding New Assistant Professor at Virginia Tech IBM Invention Plateau Award (2019)

Stergios ChristodoulidisView profile

Researcher

• Artificial Intelligence
• Medical Imaging
• Earth Sciences
• +7 more

Stergios Christodoulidis is a researcher specializing in artificial intelligence and its applications in medical imaging and earth sciences. His work spans deep learning methodologies for image analysis, including multiple instance learning, dosiomics, and registration techniques. He contributes to interdisciplinary research in computational biology, digital pathology, and radiomics. Core research in AI-driven medical image analysis Contributions to earth sciences through remote sensing and satellite imagery Focus on low-data regimes and uncertainty quantification in AI models His recent publications highlight innovative approaches in: Visually grounded bias discovery in clinical AI Conformal prediction adaptation for vision-language models Self-supervised learning for histopathology and radiology applications Integration of biological constraints in machine learning workflows

Samuel R. BarnesView profile

Assistant Professor

• Medical Imaging
• Neuroimaging
• MRI Technology
• +7 more

Samuel R. Barnes, PhD is an Assistant Professor in both the Radiology Department and Biomed Engineering Science Division at the University of Southern California's School of Medicine. He serves as Co-Director of Research in Radiology and maintains active research programs across multiple domains of medical imaging and neuroscience. Dr. Barnes earned his Doctor of Philosophy from Wayne State University in 2010, following a Master of Science from the same institution in 2007. His undergraduate degree is a Bachelor of Science in Engineering from Walla Walla University (2000). His research focuses on advanced MRI techniques for studying neurological conditions, with particular emphasis on vascular contributions to dementia, blood-brain barrier integrity, and traumatic brain injury. Dr. Barnes has developed expertise in susceptibility-weighted imaging, dynamic contrast-enhanced MRI, and computational analysis of brain imaging data. His work bridges engineering, physics, and clinical neuroscience to develop novel imaging biomarkers for neurodegenerative diseases. Analysis of his recent publications (2021-2025) reveals a strong trajectory from fundamental MRI methodology development toward clinical applications in neurodegenerative diseases. His work increasingly focuses on the intersection of vascular health, brain imaging, and cognitive decline, with significant contributions to understanding how blood-brain barrier dysfunction contributes to Alzheimer's disease and related dementias. He has also expanded into computational approaches including deep learning for medical image analysis. Dr. Barnes is Principal Investigator or key investigator on numerous active research projects funded by major institutions including the National Institutes of Health, Department of Veterans Affairs, and various private organizations. His research portfolio includes studies on vascular contributions to dementia, pediatric traumatic brain injury, visceral adiposity measurement, and advanced MRI techniques. His laboratory appears to focus on advanced MRI techniques development and application to neurological disorders, with particular expertise in blood-brain barrier imaging, susceptibility-weighted imaging, and computational analysis of brain imaging data. The research group likely includes collaborators across radiology, neurology, biomedical engineering, and computational science disciplines.

Ribana RoscherView profile

Professor

• Data Science
• Machine Learning
• Biology
• +2 more

Prof. Ribana Roscher is a leading researcher at Forschungszentrum Jülich's Institute of Bio- and Geosciences (IBG) , specifically within the Plant Sciences (IBG-2) department. Her work bridges plant sciences and data science , focusing on sustainable agriculture and bioeconomy solutions. Expertise: Data Science, Machine Learning, Biology, Plant Research, Bioeconomy Contact: Building 06.2 / Room 305, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany Research Focus: Roscher's group develops data-driven models for analyzing plant structure and processes. Key areas include: Plant phenotyping Plant-environment interaction analysis Technologies for alternative biomass utilization Integration of molecular, physiological, and ecological approaches Her research emphasizes advanced machine learning methods to address field condition challenges, such as variable environmental factors and complex data structures. Current projects explore: Uncertainty quantification in agricultural decision-making Satellite image editing for climate-conditional analysis Explainable AI for harvest-readiness prediction Core-set selection for efficient land cover segmentation FAIR data principles in environmental risk mapping These methodologies are applied to problems like crop yield prediction , ocean eddy detection , and climate adaptation studies , supporting resource efficiency and climate-resilient agriculture.

Jess SnedekerView profile

Professor

• Orthopedic Biomechanics
• Tendon Disease and Healing
• Cell-Biomaterial Interactions
• +2 more

Prof. Dr. Jess Snedeker is a Professor at ETH Zurich and the University of Zurich, where he holds joint chairs in the Department of Health Sciences and Technology (ETH) and the Department of Orthopedics (UZH). He leads the Laboratory for Orthopedic Biomechanics, focusing on interdisciplinary research in musculoskeletal health. His research spans three primary domains: (1) mechanical and biological mechanisms of tendon disease and healing, (2) micro-scale cell-biomaterial interactions for therapeutic applications, and (3) clinical biomechanics for innovating orthopedic implant design. This work integrates engineering principles with medical science to address complex challenges in tendon repair and regenerative medicine. Snedeker's recent publications (2023–2025) emphasize tendon biomechanics , bioprinting , surgical implants , and AI-driven pathology analysis , reflecting a strong focus on translational solutions for musculoskeletal disorders. Recurring themes include growth factor therapies, biomaterial scaffolds, and computational modeling.

Anders Bjorholm DahlView profile

Professor

• Computer Vision
• 3D Image Analysis
• Volumetric Image Segmentation
• +3 more

Anders Bjorholm Dahl is a Professor in 3D Image Analysis at the Department of Applied Mathematics and Computer Science , Technical University of Denmark (DTU). He leads the Image Analysis and Computer Graphics section and the QIM Center for quantification of imaging data from MAX IV. His academic work bridges computer vision, image analysis, and structural characterization. PhD in Computer Vision Degree in Forestry His research focuses on learning-based 3D image analysis methods, including volumetric segmentation, feature-based representations, and graphical models. He contributes to quantitative imaging data analysis through the QIM Center, which supports advanced imaging techniques at MAX IV. As a LINXS Imaging Working Group Member and LINXS Fellow , he engages with interdisciplinary imaging research networks.

Benjamin RisseView profile

Professor

• Biomedical Image Analysis
• Machine Learning
• Computer Vision
• +7 more

Prof. Dr. Benjamin Risse leads the Computer Vision and Machine Learning Systems (CVMLS) group at the Institute of Geoinformatics, University of Münster . His interdisciplinary research bridges biomedical image analysis , machine learning , and virtual reality (VR) applications for medical education. Develops FIMTrack for high-throughput analysis of animal locomotion Focuses on VR-based medical training systems (e.g., brain death diagnostics) Pioneers deep learning in acute myeloid leukemia (AML) diagnosis and neuroscience-inspired AI Research Interests: Biomedical image analysis, tracking algorithms, behavioral quantification, machine learning for biological systems, VR in clinical education, and interdisciplinary imaging technologies. His work addresses algorithm limitations in ecology , neuroscience , and medical diagnostics . Recent Publications (2025–2024) span coherent nanophotonic networks , VR-based medical training , and Gaussian splatting for occupancy estimation , reflecting trends in AI-driven biomedical systems and cross-disciplinary innovation . Scientific Awards: Teaching Prize of the University of Münster (2021) Lehrpreis des Fachbereichs Mathematik/Informatik (2021) AI Starter Grant on neuroscience-inspired deep learning (2022) Advising & Grants: Mentors PhD students in AI applications for 3D printing , insect monitoring , and medical education . Holds grants from BMBF , DFG , and Else Kröner-Fresenius Foundation for projects like medical tr.AI.ning and inFlame Medical Scientists College . Labs & Collaborations: Part of the Cells in Motion graduate program and Health-AI Network . Develops FIM imaging systems for Drosophila studies and insect camera traps with the Institute for Landscape Ecology . Collaborates with University Hospital Münster on AI diagnostics.

Melanie Ganz-BenjaminsenView profile

Associate Professor

• Medical Image Analysis
• Machine Learning
• Neuroimaging
• +3 more

Melanie Ganz-Benjaminsen is an Associate Professor at the Department of Computer Science (University of Copenhagen) and a researcher at the Neurobiology Research Unit (Rigshospitalet, Copenhagen). With a background in physics and computer science, she focuses on applying medical image analysis , machine learning , and statistical learning to neuroimaging data (PET & MRI) for clinical applications. PhD in Medical Image Analysis (University of Copenhagen, 2011) MSc in Physics (Karlsruhe Institute of Technology, 2007) Current positions: Associate Professor & Researcher Methodological focus: preprocessing , segmentation , registration of medical images Advocates for Open Science and Brain Imaging Data Structure initiatives Recent publications (2025) address: MRI motion artifact correction (Magma Journal) Multiverse analysis frameworks for preprocessing pipelines (Imaging Neuroscience) Neurodevelopmental studies linking serotonin morphology to adolescent behavior (NeuroImage: Clinical) NeuroMark PET atlas construction (bioRxiv) Colon segmentation pipelines (arXiv) Scientific contributions span radiological evaluation validation , hippocampal volume tracking , and AI ethics in healthcare . She organized the 2022 Responsible ML for Healthcare conference and has been featured in media regarding non-sedation medical imaging (2020).

• Machine Learning
• Information Retrieval
• Medical Data Analysis
• +3 more

Ervin Dervishaj is a PhD Fellow at the Machine Learning Section of the Department of Computer Science, University of Copenhagen. His research spans theoretical and applied machine learning, with a focus on recommendation systems, medical imaging, and computational modeling. The Machine Learning Section engages in interdisciplinary work across Information retrieval Medical data analysis Remote sensing Sustainability Biological data modeling Recent publications highlight expertise in Recommendation system interpretability (2025) GAN-based collaborative filtering (2022) Linguistic typology through language embeddings (2018) Medical imaging applications in osteoarthritis and neurodegenerative diseases (2016-2018) Optimization algorithms for adversarial learning (2016-2017) He contributes to projects involving the SCIENCE AI Centre and the TreeSense Centre for remote sensing applications.