Find Academic Supervisors

• Medical Technology
• Medical Signal Processing
• Medical Imaging
• +3 more

Thor Ole Gulsrud is an Associate Professor and Deputy Dean for Research and Innovation at the Faculty of Health Sciences, University of Stavanger. He previously led the Department of Quality and Health Technology (2018–2022) and served as Research Director for Technology at NORCE (formerly IRIS). His expertise spans medical technology, medical signal/image processing, and oil & gas. He holds a PhD in computer-aided breast cancer detection, collaborating closely with radiologists at Stavanger University Hospital. He also led the Drilling & Wells team at SINTEF and worked at National Oilwell Varco. His research focuses on medical imaging algorithms, data-driven healthcare solutions, and cross-industry technology transfer between oil/gas and medicine. Notable contributions include Norway Pumps & Pipes, a platform bridging these sectors. His education includes a PhD with a focus on computer-based breast cancer detection, alongside extensive postdoctoral experience in both academia and industry. Key research areas include medical image segmentation (e.g., prostate MRI, histopathology), AI-driven diagnostics, and application of oil/gas technologies in healthcare. He has authored/co-authored over 50 peer-reviewed papers and book chapters, with recent work in aquaculture health imaging and synthetic data generation for medical imaging. His contributions to tech transfer and interdisciplinary collaboration are highlighted through initiatives like Norway Pumps & Pipes, aiming to leverage oil/gas innovations for medical challenges. He has also been actively involved in innovation workshops, conferences, and media engagements discussing cross-sectoral R&D.

• Medical Optics
• Biomedical Engineering
• Photonics
• +3 more

Dr. Manish Verma is a Researcher at the Medical Optics group of ICFO - The Institute of Photonic Sciences. He holds a PhD in Physics from the Indian Institute of Technology (India). His work focuses on advancing optical technologies for biomedical applications, particularly in the areas of speckle contrast optical tomography and spectroscopy. Key research interests include cerebral blood flow imaging, wearable medical devices, and multi-mode fiber-based systems for clinical diagnostics. Education: PhD in Physics, Indian Institute of Technology (20XX) His research emphasizes developing low-cost, portable solutions for deep tissue blood flow monitoring and neuroimaging. He has contributed to improving the feasibility and performance of optical systems through anatomical modeling and algorithm optimization. Recent work includes wearable on-skin camera modules and high-density tomography arrays for adult brain studies. Publications highlight advancements in speckle statistics, differential pathlength formulations, and simulation-driven validation of optical systems. Collaborative efforts integrate multi-disciplinary approaches to address challenges in clinical neurology and exercise physiology.

Congyu WuView profile

Assistant Professor

• Mobile/wearable sensing for health and behavior detection
• Social signal processing and dynamic modeling
• Crowdsensing and social computing
• +3 more

Congyu Wu is an Assistant Professor at Binghamton University's School of Systems Science and Industrial Engineering. His research focuses on leveraging mobile/wearable sensing, social signal processing, and crowdsensing to advance healthcare, work performance, and community wellbeing. He holds a PhD in Systems Engineering from the University of Virginia and a BS in Industrial Engineering from Tianjin University. His key research areas include: Mobile/wearable sensing for health and behavior detection Social signal processing and dynamic modeling for group interactions Crowdsensing and social computing for population-level informatics He leads a lab exploring computational methods to address human-centric challenges, with affiliations to Google Scholar and ResearchGate. Recent work includes aiding a University of Texas study on Latina women's metabolic health risks. Advising and grant activities are ongoing.

• Nanoparticle Technology
• Biomedical Imaging
• Cancer Therapy
• +3 more

Andrei Zvyagin is an Associate Professor and Honorary Associate Professor at Macquarie University's School of Mathematical and Physical Sciences. He is affiliated with the BioFocus Research Centre and MQ Photonics Research Centre. His research focuses on multifunctional nanomaterials for biomedical applications, including imaging technologies, cancer therapy, and tissue engineering. Key projects include developing biodegradable nanoparticles for photothermal therapy and near-infrared imaging, as well as advanced hydrogel systems for wound healing and nerve repair. He has contributed to over 199 research outputs, with significant work in nanotechnology and theranostics. Projects such as 'Multifunctional nanomaterials for theranostics applications' highlight his commitment to translational medicine. Research interests span nanomaterial synthesis, optical bioimaging, and targeted drug delivery systems. Collaborations include work on quantum sensing platforms like rubyene and applications of machine learning in cancer diagnostics. Recent advancements include deferred registration imaging techniques and protein corona studies for nanoparticle stability. His work bridges material science with clinical challenges in oncology and regenerative medicine. Grants and projects include MQ Photonics initiatives and research on M2 macrophage therapy for tendon repair. He leads teams exploring novel drug delivery systems and 3D-printed biomaterials for neural regeneration. Future directions emphasize AI-driven diagnostics and quantum technologies for medical imaging.

• Oral and Maxillofacial Imaging
• Clinical Decision Support Systems
• Machine Learning
• +3 more

Eduardo Delamare is a Lecturer in Oral and Maxillofacial Radiology at the University of Sydney. He holds a BDS and MSc in Dentistry from the University of Rio Grande do Sul, Brazil, and teaches across all four years of the Doctor of Dental Medicine (DMD) program while also contributing to the Bachelor of Oral Health (BOH) curriculum. Education: University of Rio Grande do Sul, Brazil His research focuses on integrating Oral and Maxillofacial Imaging with Clinical Decision Support Systems through Machine Learning. This work aims to improve diagnostic accuracy and training in dental imaging. Eduardo's publications highlight expertise in Cone-Beam Computed Tomography (CBCT) applications, including airway assessment, cephalometric analysis, and third molar topography. Recent studies explore 2D-to-3D image reconstruction and systematic error analysis in panoramic imaging using hybrid neural network architectures. As a member of the Charles Perkins Centre, he contributes to interdisciplinary research initiatives at the University of Sydney. His academic work bridges advanced imaging technologies with practical dental education strategies.

• Medical Imaging
• Machine Learning
• Temporal Data Analysis
• +3 more

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

• Orthodontics
• Orthognathic Surgery
• Malocclusion Epidemiology
• +5 more

Mithran Goonewardene is a Senior Lecturer in the Oral Developmental and Behavioural Sciences department at the Dental School, University of Western Australia . His research focuses on orthodontic biomechanics, orthognathic surgery outcomes, and craniofacial deformity analysis. He leads projects funded by organizations including Johnson & Johnson Medical and the Australian Society of Orthodontists. Notable collaborations include work on surgical simulation technologies and guided bone regeneration membranes. Research interests span malocclusion epidemiology , orthodontic-periodontal interactions , sleep apnoea treatment , and cell biology of stressed connective tissues . He has published extensively on topics like mandibular autorotation accuracy, aesthetic dental implant preferences, and pulp blood flow dynamics post-surgery. Over 67 peer-reviewed articles and 10 grants highlight his contributions to oral health science. Grants include studies on 3D computer-aided surgical planning (Johnson & Johnson Medical, 2019–2021) Pulp blood flow changes during maxillary expansion (Burstone Foundation, 2018–2019) Facial soft tissue modeling for deformity analysis (Australian Society of Orthodontists, 2014–2015) Labs/Teams: Involved in interdisciplinary teams addressing complex dentofacial problems, combining orthodontic and surgical approaches. Active in developing novel collagen-based materials for bone regeneration.

Sandun PereraView profile

Associate Professor

• Supply Chain Management
• Operations Management
• Healthcare Operations
• +5 more

Dr. Sandun Perera is an Associate Professor of Business Analytics and Operations at the University of Nevada, Reno's College of Business. He holds leadership roles as Executive Director of the Human-Centered AI Society and Co-Chair for major academic conferences (DSI 2025, POMS 2025). His research spans Supply Chain Management, Healthcare Operations, AI integration, and Disruptive Technologies, with publications in top-tier journals like Financial Times Top 50 and UTD Top 24 lists. He has collaborated with multinational firms, hospitals, and pharmaceutical companies, addressing real-world operational challenges. Education: Ph.D., Operations Management, University of Texas at Dallas (2015) MBA and M.S. in Supply Chain Management, University of Texas at Dallas (2014–2012) Advanced coursework in Finance, Economics, and Mathematics (University of Zurich, Florida Atlantic University) B.S. in Finance and Computational Mathematics, University of Colombo (2004) His research interests focus on leveraging AI and analytics to optimize healthcare systems, supply chains, and sustainability networks. He explores blockchain applications, telemedicine integration, and environmental resource management. Recent work analyzes network effects in shipping platforms and the strategic use of bundling in supply chains. Dr. Perera has received prestigious awards including the INFORMS Financial Services Best Paper Award and the Outstanding Faculty Research Award. He actively contributes to academic publishing as a Guest Senior Editor for Production and Operations Management and an Associate Editor for Transportation Research Part E and OMEGA. Teaching excellence is reflected in his Bill Parish Award and courses on Global Supply Chains, Sports Analytics, and Predictive Analytics. His grants and collaborations include projects on elective surgery scheduling, telehealth platform coordination, and Fintech's impact on climate change. Leadership roles include executive positions in AI initiatives and conference co-chairships, emphasizing professional development and industry engagement. He advises initiatives on aligning development aid with Sustainable Development Goals and improving healthcare workforce efficiency.

Matthew Sherman BrownView profile

Professor

• Medical Imaging
• Artificial Intelligence in Radiology
• Quantitative CT Analysis
• +6 more

Matthew Sherman Brown is a Professor in the Department of Radiological Sciences at the David Geffen School of Medicine, UCLA . His work bridges medical imaging , artificial intelligence , and clinical translation to advance diagnostic and therapeutic strategies in pulmonary diseases, oncology , and radiology . Research Focus : Quantitative CT analysis for lung diseases (e.g., IPF, emphysema), AI-driven medical imaging, image harmonization, and radiomics. Grants : NIH R01HL153964 (COPD treatment planning), NIH U01CA181156 (CT response assessment), and NIH R01CA088973 (lung cancer screening). Recent publications highlight his leadership in integrating deep learning and domain knowledge to develop tools like SimpleMind for explainable AI and CT-Norm for image harmonization. His work on quantitative fissure integrity scores and lung fibrosis progression models has transformed treatment planning in pulmonary conditions. Clinical Impact : Key contributions to AI adoption in radiology workflows, improving emphysema valve response prediction, and standardizing CT protocols for reproducibility.

• Biomedical Imaging
• Optical Coherence Tomography
• Ophthalmic Diagnostics
• +2 more

Myeong Jin Ju is Assistant Professor in the Department of Ophthalmology & Visual Sciences at the University of British Columbia, with joint appointment in the School of Biomedical Engineering. Research focuses on developing advanced optical imaging technologies for vision science applications. Laboratory specializes in optical coherence tomography (OCT) systems design, signal processing algorithms, and multi-modal imaging integration. Current work aims to improve clinical ophthalmic diagnostics through high-resolution retinal and choroidal visualization, automated pathology quantification, and functional assessment techniques. Applications target diabetic retinopathy, age-related macular degeneration, and inherited retinal diseases. Recent innovations include MHz-range OCT systems, polarization-diversity techniques, and computational approaches for motion correction and image enhancement. Research bridges engineering innovation with preclinical and clinical translation in vision science.

Chongle PanView profile

Associate Professor

• Bioinformatics
• Machine Learning
• Proteomics
• +5 more

Chongle Pan is an Associate Professor of Microbiology and Computer Science at The University of Oklahoma. He holds a Ph.D. from the University of Tennessee (2006) and a B.S. from East China Normal University (2001). His research bridges computational and biological sciences, focusing on explainable machine learning, parallel computing for biomedical informatics, human microbiome dynamics, and environmental microbiome modeling. Key projects include studying probiotic/prebiotic impacts on human health and developing mechanistic models for wetland sediment communities' greenhouse gas emissions. His lab employs proteomic stable isotope probing, integrated -omics, and synthetic microbiome approaches. Research interests include computational biology, proteomics, and environmental microbiology. Notable contributions include genome-resolved proteomic studies of soil microbial communities and cloud computing-based proteomics analysis tools like CloudProteoAnalyzer. Collaborations involve institutions like Lawrence Berkeley National Lab and Pacific Northwest National Lab. Publications highlight advancements in metaproteomics, microbial community modeling, and biomedical imaging technologies. His work addresses environmental bioremediation challenges and computational solutions for genomic data analysis. The Pan Lab's research emphasizes interdisciplinary innovation at the interface of computer science and life sciences. Grants and funding sources support his work in computational biology and microbial ecology. No specific awards are listed, but his extensive publication record reflects sustained academic impact. Students are advised through collaborative interdisciplinary programs combining computer science and microbiology disciplines.

• Machine Learning
• Computer-Aided Design (CAD)
• Embedded Systems
• +3 more

Nicolo' Bellarmino is a Research Fellow and Research Assistant at the Department of Automatic Control and Computer Science (DAUIN) of Politecnico di Torino. His roles include teaching and collaborating in courses such as System Programming, Computer Science, and Future of Work across Computer Engineering and Aerospace Engineering programs. He is affiliated with the CAD - Electronic CAD & Reliability Group (DAUIN), focusing on research in machine learning, embedded systems, and hardware testing. His research interests center on computational intelligence applied to computer-aided design, particularly in machine learning techniques for microcontroller performance screening. He has also explored neural network resiliency, fault detection in CNNs, and device-aware testing methodologies. Recent work emphasizes compressed sensing, feature selection, and transfer learning in resource-constrained systems. Bellarmino's publications span conference proceedings and journals, addressing challenges in embedded systems testing, neural network optimization, and biomedical applications like COVID-19 detection via exhaled breath analysis. His contributions bridge theoretical machine learning advancements with practical hardware validation and optimization. He actively contributes to academic teaching, maintaining an email contact at nicolo.bellarmino@polito.it for professional inquiries.

• emotional and behavioral aspects of chronic medical conditions
• patient engagement in disease self-management
• depression detection and treatment in primary care
• +3 more

David W. Hindman, PhD serves as a Clinical Assistant Professor in the Department of Family Medicine at Charles R. Drew University of Medicine and Science School of Medicine. His academic appointment focuses on integrating behavioral health within primary care settings, particularly for underserved populations. Dr. Hindman's research interests center on clinical research examining the emotional and behavioral aspects of chronic medical conditions and patient engagement in disease self-management. His recent work has concentrated on improving depression detection and treatment in primary care settings and designing integrated behavioral health services delivery systems, with special emphasis on medically underserved populations. His publication record demonstrates expertise in mental health services research, chronic disease management, and health disparities affecting minority communities. His research portfolio shows consistent focus on safety net healthcare systems serving vulnerable populations, with publications spanning topics from diabetic retinopathy detection through machine learning to schizophrenia hospitalization trends and depression management in primary care settings. The work frequently addresses disparities in healthcare access and quality for African American and Latino populations. Scientific Awards: 1995 Class Honors, University of Michigan 1998 Gene Westley Pfrimmer Award, Fuller Theological Seminary-Graduate School of Psychology 2005 Chairman's Newcomer's Award, Charles Drew University of Medicine and Science-Department of Family Medicine 2006 Faculty Role Model Award, Charles Drew University of Medicine and Science-Department of Family Medicine 2007 Chairman's Award, Charles Drew University of Medicine and Science-Department of Family Medicine 2009 Distinguished Service Award, California Psychological Association-Division II (Education and Training) Dr. Hindman has collaborated extensively with researchers including Bazargan, Ani, Ogunyemi, and others working at the intersection of behavioral health and primary care. His work demonstrates consistent engagement with practical clinical problems affecting safety net healthcare systems serving medically underserved communities. While specific grant funding isn't detailed in the available information, his publication record suggests ongoing research activity focused on improving mental health integration within primary care settings.

Friso De BoerView profile

Professor

• Machine Learning
• Biomedical Engineering
• Signal Processing
• +3 more

Professor Friso De Boer is a faculty member in the College of Engineering and Information Technology at Charles Darwin University (CDU). His primary research focuses on detecting abnormalities in biosignals using advanced signal processing techniques and artificial intelligence, including machine learning and neural networks. Key applications include EEG-based binaural hearing research, APG/ECG analysis for heat stress detection, and AI-driven diagnosis of diseases like breast cancer, cardiovascular disease, and kidney disease. He leads the Biomedical Engineering and Health Informatics Research Group at CDU, offering PhD opportunities in these areas. Prospective students are encouraged to contact him directly for further collaboration. His research interests span biomedical engineering, medical imaging, and health informatics. Notable projects involve developing novel AI models (e.g., ECgMLP, LungNet22) for disease diagnosis and improving diagnostic accuracy through hybrid approaches like graph theory and deep learning. He also explores ergonomic risk prediction via 3D pose analysis and gesture recognition systems for interactive displays. Publications highlight advancements in medical AI, including skin cancer detection, endometrial cancer diagnosis, and automated respiratory disease analysis using XAI principles. His work emphasizes real-world clinical applications, such as improving ICD coding accuracy and enhancing CT scan interpretation for pediatric bronchiectasis. Professor De Boer collaborates on interdisciplinary projects, blending engineering principles with healthcare challenges. His contributions span from foundational signal processing research to applied AI solutions in clinical settings, reflecting a commitment to advancing biomedical technology and patient outcomes.

Sokratis MakrogiannisView profile

Professor

• Medical Image Computing
• Machine Learning
• Biomedical Imaging
• +3 more

Sokratis Makrogiannis is a Professor in the Division of Physics, Engineering, Mathematics, and Computer Science at Delaware State University, where he directs the Mathematical Imaging and Visual Computing Group. His research focuses on medical image computing, machine learning, and visualization, with applications in biomedical imaging and computer vision. He holds a PhD in Electronics and Informatics from the University of Patras, Greece, and completed postdoctoral fellowships at the University of Pennsylvania and Wright State University. His work bridges industrial collaborations (e.g., GlaxoSmithKline, General Electric) and academic research, emphasizing computational neuroanatomy, image analysis algorithms, and medical imaging techniques. He has secured funding from NIH, NSF, DoD, and the State of Delaware. His teaching spans advanced topics like image processing, pattern recognition, and discrete mathematics. Collaborators include leading institutions such as the University of Delaware, Lund University, and the National Institute on Aging (NIH). Research interests include image analysis, machine learning applications in healthcare, and automated tissue quantification. His publications address cell segmentation, osteoporosis classification, and MRI/CT segmentation techniques. He serves as editor for journals like BioMedical Engineering OnLine and Frontiers in Computer Science, contributing to academic dissemination and peer review. The MIVIC Lab under his leadership develops innovative imaging solutions for clinical and environmental challenges.