Find Academic Supervisors

• Molecular Cell Biology
• DNA Repair
• Cancer Biology
• +3 more

Henrik Peter Sahlin Pettersen is an Associate Professor at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Clinical and Molecular Medicine within the Faculty of Natural Sciences and Technology. His research focuses on molecular mechanisms of DNA repair, genomic instability in cancer, and applications of artificial intelligence in digital pathology. He has published extensively in high-impact journals such as Neuropathology and Applied Neurobiology , Diagnostic Pathology , and Nucleic Acids Research . Key research areas include the role of uracil-DNA glycosylases in DNA repair pathways, tumor-associated macrophage biology, and the development of machine learning tools for histopathological analysis. His work bridges molecular biology, clinical pathology, and computational methods, with recent studies investigating therapeutic effects on IBD-derived colonoids and T-cell dynamics in inflammatory bowel diseases. Dr. Pettersen collaborates with institutions like St. Olavs Hospital and SINTEF, emphasizing translational research in personalized medicine. He actively participates in academic outreach, delivering lectures on digital pathology, proteomics, and the societal impact of scientific advancements. His research has contributed to understanding genomic uracil’s role in lymphoma development and improving diagnostic accuracy through automated image analysis systems. Notable projects include developing the H2G-Net segmentation framework for gigapixel histopathology images and investigating the clinicopathological relevance of tumor-associated macrophages in meningiomas. His work highlights interdisciplinary approaches to address challenges in oncology, immunology, and medical technology.

• Biomedical Engineering
• Paediatrics and Reproductive Medicine
• Computation Theory and Mathematics
• +3 more

Priya Rani is a Lecturer in the School of Engineering at RMIT University. Her research focuses on interdisciplinary areas including Biomedical Engineering, Artificial Intelligence, and Neurosciences. She actively supervises research projects such as developing peripheral electronics for neural recording systems and applying machine learning for clinical delirium diagnosis. Dr. Rani’s work bridges computational methods with healthcare applications, emphasizing robust machine learning models and medical imaging analysis. She is affiliated with RMIT’s City Campus and contributes to advancing technologies for sustainable and intelligent systems. Research Interests: Her primary areas include computational theory, biomedical engineering, and AI-driven solutions for healthcare challenges. Notable projects involve semantic segmentation using attention-augmented networks and damage monitoring in advanced materials. Dr. Rani’s research trends highlight machine learning robustness, medical image analysis, and smart home technologies for aging populations. Supervision Focus: Neural recording systems, clinical AI applications Key Projects: Cortical neural interfaces, delirium prediction algorithms Technical Expertise: Machine learning, image processing, medical diagnostics Collaboration and Impact: Her work addresses real-world challenges through interdisciplinary approaches, with publications spanning medical IoT, nanosensors, and composite materials analysis. Dr. Rani’s contributions emphasize practical applications of AI and computational methods in healthcare and engineering domains.

Elizabeth HolmView profile

Professor

• Computational Materials Science
• Microstructural Evolution
• Machine Learning in Materials
• +3 more

Elizabeth Holm is Professor and Department Chair of Materials Science and Engineering at the University of Michigan. Her research employs computational methods to study microstructural evolution, with expertise spanning atomic-scale molecular dynamics to continuum-scale modeling. Education includes a PhD in Materials Science and Scientific Computing from University of Michigan. Research focuses on computational materials science with emphasis on grain boundary dynamics, machine learning applications in materials, and additive manufacturing characterization. Recent publications develop AI methods for microstructure prediction, grain growth modeling, and automated analysis of material images. Work integrates machine vision with physics-based models to understand material behavior across scales. National Academy of Engineering Member (2025) ASEE Mike Ashby Outstanding Materials Educator Award (2022) AIME Honorary Member (2022) ASM Edward DeMille Campbell Memorial Lecturer (2021) The Minerals, Metals & Materials Society Fellow (2019) Professor Holm leads research on computational design of hierarchical materials and has held leadership positions including President of The Minerals, Metals & Materials Society (2013).

Maryam Shafiei AlavijehView profile

Professor

• Non-Destructive Evaluation
• Ultrasonic Testing
• Machine Learning
• +3 more

Maryam Shafiei Alavijeh is a Professor at the University of Windsor, specializing in Non-Destructive Evaluation (NDE) and advanced materials research. She collaborates closely with Prof. Roman Maev and students like Vlad Tusinean on projects integrating ultrasonic testing, machine learning, and artificial intelligence for industrial applications. Their work focuses on improving pipeline safety, composite material analysis, and defect detection in critical infrastructure. Her research interests span non-destructive evaluation techniques, ultrasonic testing methodologies, and the application of AI in material science. Key projects include developing automated flaw detection systems for polyethylene gas pipes, optimizing chord transducer performance, and leveraging deep learning models for NDE 4.0 frameworks. In 2022, Prof. Shafiei Alavijeh and her team received recognition from the Canadian Institute for Non-Destructive Evaluation for their innovative contributions to NDE methodologies. Her work bridges academic research with industry needs, emphasizing practical solutions for structural integrity assessment and material characterization.

Henry HanView profile

Professor

• Data Science
• Fintech
• Artificial Intelligence
• +7 more

Dr. Henry Han is a Professor and holds the McCollum Family Chair in Data Science at Baylor University’s Hankamer School of Business, Department of Data Science. His interdisciplinary research spans data science, fintech, artificial intelligence, bioinformatics, health informatics, cybersecurity, and quantum computing. PhD, Applied Mathematical & Computational Sciences, University of Iowa (2004) MS, Computer Science, University of Iowa (2001) MS, Applied Mathematics, University of Iowa (2001) Dr. Han’s research integrates advanced machine learning, optimization, and data analytics techniques to solve complex problems in finance, healthcare, and cybersecurity. His work emphasizes manifold learning , deep neural networks , evolutionary computation , and graph-based learning for real-world applications such as high-frequency trading, automobile damage classification, and single-cell genomics. He applies these methods across domains including financial modeling, biomedical data analysis, and sports forecasting. His recent publications demonstrate a strong trend toward interdisciplinary machine learning , with applications in computational finance, bioinformatics, and intelligent systems. Many of his works utilize optimization-based deep learning and unsupervised representation learning to extract meaningful patterns from high-dimensional data. Dr. Han has received recognition through prestigious appointments: McCollum Family Chair in Data Science He actively collaborates on research projects and has secured funding for advanced data science initiatives, though specific grant details are not listed. His work supports both academic advancement and practical innovation in data-driven decision-making. He advises graduate students and contributes to the development of next-generation data science methodologies. Dr. Han is a key member of the data science research team at the Hankamer School of Business, contributing to interdisciplinary labs and research groups focused on fintech, AI, and health informatics.

Philipp SiebergView profile

Researcher

• Intelligent Transportation Systems
• Vehicle Dynamics
• Machine Learning
• +5 more

Dr.-Ing. Philipp Sieberg serves as a Researcher at the Chair of Mechatronics within the Faculty of Engineering at the University of Duisburg-Essen, Germany. His research focuses on intelligent transportation systems, vehicle dynamics, and applied artificial intelligence, with particular emphasis on hybrid methodologies that integrate physical models with machine learning approaches. Based in Room MD-226, he actively contributes to both academic and industrial advancements in automotive engineering through his publications and research projects. His research interests span intelligent transportation systems, vehicle dynamics control, machine learning applications, and hybrid estimation methods. Sieberg specializes in developing reliable AI-based virtual sensors for vehicle state estimation, creating model-based predictive control systems for active roll stabilization, and applying neural networks to complex automotive challenges. His work consistently addresses the critical balance between AI innovation and system reliability in safety-critical automotive applications, with significant contributions to steering system dynamics, wear mechanism classification, and autonomous vehicle development. Sieberg's recent publications (2022-2025) demonstrate a clear trajectory toward increasingly sophisticated hybrid AI methodologies in vehicle dynamics. His research shows growing emphasis on reliability assurance of AI components, multi-fidelity simulation approaches, and practical implementation of machine learning in hardware-in-loop test environments. The work spans fundamental research in neural network-based state estimation to applied solutions for steering systems, wear analysis, and autonomous inland waterway vessels, reflecting both theoretical depth and real-world applicability. Award for Particularly Outstanding Graduation, Department of Engineering, University of Duisburg-Essen Award for Outstanding Completion of Master's Program in Mechanical Engineering, Faculty of Engineering, University of Duisburg-Essen First Prize for Outstanding Master's Thesis 2017, Alumni Chair of Mechatronics eV, University of Duisburg-Essen Active involvement in IEEE Germany Section and Chair of IEEE ITSS German Chapter Sieberg contributes significantly to research projects including AutoBin (Autonomous Inland Waterway Vessel), driving simulators for the Chair of Mechatronics, and machine learning algorithms for mobile state prediction applications. His leadership extends to committee activities as Student Activities Chair of the IEEE ITSS German Chapter, where he bridges academic research with professional society engagement. Current teaching responsibilities include courses on highly automated driving systems and technical fundamentals of future vehicle systems for the Master's program in Automotive Engineering & Management Executive. Working within the Chair of Mechatronics research group, Sieberg collaborates extensively with colleagues including Dieter Schramm, Christian Hürten, and Alexander Haas. His research leverages advanced driving simulators and hardware-in-loop test benches, with strong connections to the AutoBin project consortium developing autonomous inland vessel technology. The research environment emphasizes interdisciplinary collaboration between mechanical engineering, computer science, and control systems specialists to address complex mobility challenges.

• Forest Growth
• Forest Monitoring
• Forest Inventory
• +9 more

Arne Nothdurft is a University Professor for Forest Monitoring at the University of Natural Resources and Life Sciences (BOKU) in Vienna, Austria, where he chairs the Institute of Forest Growth within the Department of Forest and Soil Sciences. With a career spanning over two decades in forest research and academic leadership, he has established himself as a leading expert in advanced forest inventory techniques and forest growth modeling. Professor Nothdurft's research focuses on the application of cutting-edge technologies in forest monitoring, particularly LiDAR and personal laser scanning systems for forest inventory. His work bridges the gap between traditional forestry practices and modern digital solutions, with emphasis on mixed species forest management, climate change adaptation, and the development of smart forestry systems. His research interests encompass forest growth modeling, tree species classification using point cloud data, and the development of spatial prediction models for forest inventory parameters. His recent publications demonstrate a strong trend toward integrating artificial intelligence with forestry applications, particularly in the analysis of 3D point cloud data from laser scanning technologies. His work spans both theoretical advancements in spatial statistics and practical applications for forest managers, with a particular focus on improving the accuracy and efficiency of forest inventory systems. Thurn und Taxis Förderpreis für die Forstwissenschaft (2008) Professor Nothdurft has supervised numerous master's and doctoral theses, primarily focused on the application of laser scanning technologies in forestry, forest inventory optimization, and growth modeling. His research is supported by multiple ongoing projects funded by Austrian research agencies and federal ministries, with a strong emphasis on practical applications for forest management. He leads the Institute of Forest Growth, which maintains the Lehrforst Rosalia long-term forest monitoring site, and collaborates extensively with the Institute of Forest Engineering on smart forestry initiatives.

• Head and Neck Surgery
• Optical Diagnostic Imaging
• Sinonasal Pathologies
• +3 more

Christian Betz is a full Professor and Director of the Department of Otorhinolaryngology at the University Medical Center Hamburg-Eppendorf (UKE), Faculty of Medicine, University of Hamburg. As Deputy Director of the Head and Neurocenter, he specializes in head and neck surgery, optical diagnostic imaging, and HPV-related cancers. Medical Specialist in Otorhinolaryngology Vice-Deanery for Research representative Active in the European Platform for Photodynamic Medicine International collaborations in LASER SURG MED and radiobiology His research focuses on: Optical metabolic imaging for tumor differentiation AI-driven computer-aided diagnosis of sinus anomalies DNA repair mechanisms in HNSCC Photodynamic therapy applications Post-COVID olfactory dysfunction Key scientific contributions include: Over 30 publications in LASER SURG MED and HEAD NECK-J SCI SPEC 2025 international multicenter studies on surgical outcomes 2024 leadership in AI integration for maxillary sinus classification Awards: Video Award for 5-ALA fluorescence endoscopy Best Oral Communication Prize for OCT applications

• Life Sciences
• Molecular Biology
• Amyotrophic Lateral Sclerosis (ALS)
• +4 more

Dr. Duncan Moore is a part-time Research Fellow at the Science Policy Research Unit (SPRU) within the University of Sussex Business School . His work bridges life sciences and science policy, with a focus on research evaluation and health-related innovation. Current projects: DARE (Diversity Approach to Research Evaluation) and SIRI (Serendipity in Research and Innovation) Collaborators: Michael Hopkins , Ohid Yaqub , Stuart Hogarth Education: MBiochem (Oxitec Ltd placement) PhD in Life Sciences (University of Sussex, focused on ALS/MND) His research spans molecular biology (DNA repair, ALS) and science policy (HIV funding, Zika vaccine dynamics, biomedical indexing). Recent publications analyze policy impacts on research direction and automated disease classification tools. Email: D.A.Moore@sussex.ac.uk

Asif KarimView profile

Lecturer

• Machine Learning
• Health Informatics
• Blockchain Applications
• +7 more

Dr. Asif Karim is a Research Active Lecturer in the Department of Information Technology at the Faculty of Science and Technology, Charles Darwin University, Australia. He has been a full-time lecturer since August 2021, following a sessional role from 2018 to 2021. Prior to his current position, he served as a lecturer at Daffodil International University and Uttara University in Bangladesh. His research focuses on the application of machine intelligence in health informatics and blockchain technologies. He has significant industry experience in Software Engineering and actively supervises postgraduate research students. Machine Learning Health Informatics Blockchain Applications Smart Contracts Deep Learning Anomaly Detection The recent publications of Dr. Karim span a diverse range of applications in artificial intelligence, particularly in healthcare and secure computing. His work includes developing efficient deep learning models for medical diagnosis, privacy-preserving classification of diseases from medical images, and anomaly detection in cybersecurity. He also explores applications in mobile cloud computing and agricultural technology, demonstrating a broad interdisciplinary approach to solving real-world problems using machine learning. Dr. Karim actively contributes to research projects and supervises postgraduate students. He led the project "Machine Learning Diagnostics System for Bronchiectasis" and has extensive experience in teaching undergraduate and postgraduate courses in computer science, including Machine Learning, Operating Systems, and Software Engineering. He is involved in organizing academic events, such as a digital awareness workshop for rural indigenous communities, reflecting his commitment to community engagement and technology outreach.

• Weigh-in-Motion Systems
• Vehicle Weighing
• Sensor Technology
• +5 more

Piotr Burnos serves as a Professor at the Department of Metrology and Electronics within the Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering at AGH University of Science and Technology in Kraków, Poland. His office is located in room 211 of building B-1 (second floor), with contact details including telephone +48 12 617 28 27 and email burnos@agh.edu.pl. Burnos specializes in Weigh-in-Motion (WIM) technology for vehicle enforcement, focusing on sensor accuracy under real-world conditions including temperature fluctuations and pavement mechanics. His research addresses critical challenges in dynamic vehicle weighing systems, particularly for administrative enforcement applications where measurement precision directly impacts road infrastructure protection and legal compliance. Key contributions include thermal error compensation, multi-sensor fusion, and Polish implementation frameworks. Analysis of his 15 most recent publications (2015-2018) reveals consistent advancement in WIM system reliability through temperature modeling, pavement interaction studies, and administrative integration. His work bridges metrological precision with practical enforcement needs, emphasizing Polish regulatory contexts while contributing to broader transportation engineering knowledge. No scientific awards were documented in the source materials. While specific student advisement or grant information isn't provided in available sources, Burnos' research demonstrates sustained focus on applied transportation engineering problems with direct societal impact through infrastructure protection and regulatory enforcement.

Martin BennettView profile

Professor

• Vascular Biology
• Atherosclerosis
• Cell Senescence
• +6 more

Martin Bennett holds the prestigious British Heart Foundation Chair of Cardiovascular Sciences at the University of Cambridge, where he serves as Head of the Section of Cardiovascular and Respiratory Medicine within the Department of Medicine. He directs three major research entities: the BHF Cambridge Centre for Research Excellence, the Cambridge Cardiovascular Interdisciplinary Research Centre, and the NIHR Cambridge BRC Cardiovascular and Respiratory Theme. His work bridges clinical cardiology practice at Cambridge University Hospitals and Royal Papworth Hospitals with fundamental vascular biology research. Professor Bennett trained in Cardiology in Birmingham and Cambridge, establishing himself as a leading expert in vascular biology. His research program centers on understanding how to improve arterial health in heart disease by stabilizing cells in the blood vessel wall to prevent dangerous narrowing of blood vessels. A major focus is on vascular smooth muscle cell proliferation, death, and senescence, particularly examining how DNA damage responses and sirtuins influence vascular disease progression. His team has identified important molecules that control cellular behavior in atherosclerosis. His recent publications demonstrate a strategic shift toward integrating artificial intelligence with advanced imaging techniques, particularly Optical Coherence Tomography (OCT). His research group has developed artifact-corrected AI systems that enhance plaque classification, identify high-risk features, and predict patient outcomes. The publications also reveal deep exploration of clonal vascular smooth muscle cell expansion, thrombin receptor signaling, and senescence mechanisms in atherosclerosis, connecting molecular pathways to clinical manifestations of cardiovascular disease. BHF Professorship of Cardiovascular Sciences Director of BHF Cambridge Centre for Research Excellence Director of Cambridge Cardiovascular Interdisciplinary Research Centre Director of NIHR Cambridge BRC Cardiovascular and Respiratory Theme Head of Section of Cardiovascular and Respiratory Medicine Professor Bennett leads an active research group at the Victor Phillip Dahdaleh Heart and Lung Research Institute (VPD-HLRI), where he supervises multiple research fellows and doctoral students. His group receives substantial funding from the British Heart Foundation and other major research councils. His laboratory employs cell and tissue culture, advanced imaging, molecular techniques including RNAseq and virus-mediated gene transfer, coupled with in vivo models of atherosclerosis and vessel injury. His clinical imaging studies utilize VH-IVUS and OCT with biomechanical modeling and AI-based diagnostic approaches. The Bennett research group operates within the Victor Phillip Dahdaleh Heart and Lung Research Institute, collaborating closely with clinicians at Addenbrooke's and Papworth hospitals. His team has established multiple ongoing projects including investigations into cell senescence in atherosclerosis, prevention of vascular damage after radiotherapy and chemotherapy, biomechanical indices for coronary lesion rupture risk, and novel mechanisms mediating stress-induced premature senescence in cardiovascular disease. Current technological developments focus on fully automated systems for coronary artery disease evaluation and interactive platforms for OCT image analysis.

Anita TorabiView profile

Professor

• Structural Geology
• Fault Mechanics
• Fluid Flow in Deformed Reservoirs
• +6 more

Anita Torabi is a Professor in Structural Geology at the University of Oslo , affiliated with the Department of Geosciences. Her research focuses on structural geology, fault mechanics, fluid flow in deformed reservoirs, CO2 storage, geothermal reservoirs, and seismicity. She employs advanced methods such as 3D seismic analysis, fault asperity studies, and machine learning for geological applications. Key affiliations: University of Oslo, NORCE (2015-2018), Uni Research CIPR (2008-2018) Education: PhD in Geosciences (2008) - University of Bergen Research Interests include: Fault mechanics and deformation processes Fluid flow in faulted reservoirs CO2 storage and green energy solutions Geomechanics and seismic risk assessment Machine learning in structural geology Basement rock fracture analysis Recent Publications highlight trends in: 3D fault characterization using deep learning Fluid dynamics in siliciclastic-carbonate systems Seismic attribute analysis for fault geometry Compaction bands in Tertiary-Quaternary sequences Ontology-driven geological modeling Machine learning applications for CO2 storage Research Leadership includes projects such as: CO2 Seal and Bypass – COPASS CO2SafeQuest (fault behavior for CO2 storage) gigaCCS@UiO (Center of Excellence in Carbon Capture) VICCO (volcanic-sedimentary CO2 storage) Pool Studies CO2 Storage research group

Evren M. OzbayogluView profile

Professor

• Drilling Engineering
• Wellbore Stability
• Multiphase Flow
• +12 more

Evren M. Ozbayoglu is the Jonathan B. Detwiler Endowed Chair Professor in Petroleum Engineering at The University of Tulsa, where he directs the TU Drilling Research Projects (TUDRP). Previously a faculty member at Middle East Technical University (METU) until 2009, he earned his B.Sc. (1996) and M.Sc. (1998) from METU and a Ph.D. in Petroleum Engineering from The University of Tulsa (2002). Research Focus : Drilling mechanics, wellbore stability, multiphase flow, non-Newtonian fluid dynamics, and machine learning applications in drilling operations. Notable Contributions : Co-author of specialized drilling engineering textbooks and over 200 peer-reviewed papers, with industry impact in underbalanced drilling, managed pressure drilling, and hole cleaning optimization. Recognition : Recipient of multiple SPE awards, Chevron Fellowships, and the Allen Chapman Distinguished Ph.D. Student Award. Expertise : Combines experimental and computational approaches to drilling challenges, with recent work on cement sheath integrity, thermal stability in HPHT drilling, and AI-driven drilling performance optimization. Current Projects emphasize real-time drilling data analysis, fluid dynamics modeling, and machine learning integration for predictive drilling solutions. His work bridges fundamental research with industrial applications through TUDRP.

• Medical Biology
• Machine Learning Applications in Medicine
• Medical Imaging Analysis
• +3 more

Muhammed Kamil Turan is an Associate Professor at Karabük University, Faculty of Medicine, Department of Basic Medical Sciences Medical Biology . He has held leadership roles including Deputy Dean (2013-2020) and Program Coordinator (2012-present). PhD in Medical Biology (2011) from Ondokuz Mayıs University Master's in Computer Engineering (2004) from Karabük University Medical Degree (2000) from Ondokuz Mayıs University His research focuses on machine learning applications in medical biology , with particular emphasis on sex/gender determination from CT images , DNA damage analysis , and automated biomedical imaging systems . He has developed innovative solutions for peripheral blood smear analysis, retinal microaneurysm detection, and low-cost medical device design. Recent research trends show strong integration of artificial intelligence in diagnostic imaging (cranial/pelvic CT analysis), bioinformatics (protein structure prediction), and biomedical instrumentation (automated microscopes, DNA electrophoresis systems). His 2025 work explores fractal network graphs in medical contexts. He serves as principal advisor for 11 graduate students and collaborates extensively with researchers across Turkey. His projects often involve TÜBİTAK/TÜBA funding , with recent work on cold plasma applications in wound healing and gastric ulcer treatment.