Find Academic Supervisors

Ramy ElmoazenView profile

Researcher

• Learning Analytics
• Educational Technology
• Collaborative Learning
• +3 more

Ramy Elmoazen is a Postdoctoral Researcher at the School of Computing, University of Eastern Finland (UEF), within the Faculty of Science, Forestry and Technology. His research focuses on learning analytics, educational technology, and collaborative learning, with a particular emphasis on temporal and social dimensions in collaborative environments. He leads and contributes to projects like BalticSeaBioMed, IGLUE (game-based learning using escape rooms), and Maker & STEM Education initiatives. His work bridges theoretical frameworks like epistemic network analysis (ENA) with practical applications in online education and biomedical learning. Key projects include evaluating technology-enhanced learning in laboratories, exploring barriers and opportunities in STEM education, and advancing methods for capturing student interactions through learning analytics. His doctoral dissertation (2024) emphasized using learning analytics to study social and temporal aspects of collaborative learning. Elmoazen’s interdisciplinary approach integrates quantitative methods, systematic reviews, and educational design, contributing to both academic and applied contexts. He has collaborated on studies ranging from clinical trials in dentistry to large-scale reviews of ENA applications, showcasing his versatility across health education and technology-driven pedagogy. His research highlights the importance of scalable analytics tools and evidence-based practices in modern educational settings.

• Tuberculosis
• Diagnostics
• Epidemiology
• +3 more

Madhukar Pai, MD, PhD is a Senior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) , based at the Montreal General Hospital site, and a Professor in the Department of Epidemiology, Biostatistics and Occupational Health at McGill University’s Faculty of Medicine and Health Sciences . He also holds an appointment in the Division of Respiratory Medicine, Department of Medicine, MUHC . He leads research within the Infectious Diseases and Immunity in Global Health Program and the Centre for Outcomes Research and Evaluation (CORE) . Education: MD – Medical degree (institution and year not specified in text) PhD – Doctoral degree (field and institution not specified in text) Research Interests: Professor Pai’s research revolves around tuberculosis (TB) , a disease causing 10 million new cases and 1.4 million deaths annually. His work integrates epidemiology , diagnostics evaluation , implementation science , and global health policy to accelerate TB control. A major strand involves rigorous evaluation of novel rapid molecular diagnostics compared to conventional tests, assessing accuracy, cost-effectiveness, and readiness for national policy adoption. He employs standardised (mystery) patients to measure quality of TB care in private and public sectors across India , South Africa , Indonesia , and Nigeria . The COVID-19 pandemic has added a new dimension, examining disruptions to TB services and leveraging lessons for resilient health systems. His overarching goal is translational epidemiology —ensuring that scientific advances rapidly translate into policies and practices that save lives. Publication Trends: Between 2022 and 2025, Professor Pai has published prolifically on TB diagnostics , care cascades , COVID-19 impacts , private sector engagement , antimicrobial resistance , and decolonizing global health . His articles frequently appear in The Lancet , BMJ Global Health , PLoS Medicine , and Nature Reviews Disease Primers , reflecting a commitment to high-impact, policy-relevant science. Methodological contributions include systematic reviews, meta-analyses, standardised patient studies, and qualitative policy analyses. Scientific Awards & Recognition: (No specific named awards detailed in the provided text) Funding & Grants: Professor Pai’s research is supported by competitive international grants focused on TB diagnostics implementation, health systems strengthening, and pandemic preparedness. While exact grant titles and amounts are not listed, his extensive publication record attests to sustained funding from agencies invested in global health research. Labs & Teams: He leads a multidisciplinary team within the Centre for Outcomes Research and Evaluation (CORE) and the Infectious Diseases and Immunity in Global Health Program at RI-MUHC. Collaborations span McGill University, the McGill International TB Centre, and numerous partners in high-burden countries.

Paul CrossView profile

Senior Lecturer

• Insect Pollinators
• Bee Tracking Technology
• UAV-based Environmental Monitoring
• +12 more

Dr. Paul Cross is a Senior Lecturer in the Environment and Senior Tutor at the School of Environmental and Natural Sciences, Bangor University, UK. He is actively involved in research, teaching, and public engagement, with a strong focus on environmental science, pollinator conservation, and sustainable agriculture. His work bridges ecology, engineering, and socio-economic modeling, contributing significantly to understanding pollinator health and agricultural sustainability. His research interests include UAV-based tracking of insect pollinators, beekeeping as a tool for poverty alleviation, zoonotic diseases (particularly E. coli O157), bee morphometrics, and socio-economic methodologies for estimating disease and illegal behaviors. He employs advanced technologies such as radar, machine learning, and UAVs to study pollinator behavior and environmental impacts. His recent publications highlight a strong trend in integrating engineering and ecological sciences, particularly through the development of energy-harvesting bee tracking devices and radar-based monitoring systems. His work spans disciplines from entomology and soil science to public health and climate change, reflecting a highly interdisciplinary approach to environmental challenges. Senior Lecturer in the Environment, Bangor University Senior Tutor Course Director for MSc Conservation and Land Management Member of SENRGy Teaching and Ethics Committees Dr. Cross has supervised multiple PhD students on topics ranging from beekeeping in Tanzania to UAV-based bee tracking and neonicotinoid impacts. He has led or contributed to numerous research projects funded by the Welsh Government, RCUK, and the British Academy, focusing on pesticide hazards, greenhouse gas emissions, and food safety. He is actively involved in public outreach, with media appearances on BBC Countryfile and contributions to The Conversation. His work contributes to UN Sustainable Development Goals related to poverty reduction, climate action, and life on land.

• Computer Science
• Machine Learning
• Image Processing
• +4 more

Yang Cao is a Professor at the University of Science and Technology of China , Department of Automation, Hefei, China. He holds a PhD from Northeastern University (2004, Shenyang, China) and has active affiliations with institutions like Virginia Tech and Huazhong University of Science and Technology. Research Focus: Spatiotemporal modeling, event-based vision, 3D human-object interaction, and industrial defect detection. Publications: 15 recent articles highlight his work in diffusion models, transformers, and state-space networks for tasks like traffic emission imputation, eye tracking, and PCB defect detection. Collaborative Work: Co-authored with Zheng-Jun Zha, Wei Zhai, Yu Kang, and others in journals like IEEE Transactions on Neural Networks and CVPR Workshops. Scientific Contributions: His research bridges computer vision, machine learning, and industrial applications, emphasizing real-world challenges such as low-light enhancement and sensor fusion.

• Computer Vision
• Machine Learning
• Biometrics
• +3 more

David Menotti is a prominent researcher in computer vision and machine learning, with a focus on biometrics, license plate recognition, and video analysis. He has collaborated extensively with institutions and researchers globally, contributing to over 171 publications between 2003-2025. Key research areas include face recognition, synthetic data generation, and zero-shot learning Major contributions in license plate super-resolution, sign language translation, and ocular biometrics Active in organizing competitions like FRCSyn and OCFR to advance synthetic data applications His work often combines diffusion models, CNN architectures, and multimodal approaches to solve real-world problems in unconstrained environments. Notable recent projects involve enhancing face recognition with synthetic data, vehicle color recognition under adverse conditions, and Libras-to-Portuguese translation. Menotti's publications appear in journals like Information Fusion , IEEE Access , and conferences including CVPR, SIBGRAPI, and IJCNN. He frequently collaborates with researchers such as Rayson Laroca, William Robson Schwartz, and Pedro Vidal.

• Computer Vision
• Pattern Recognition
• Machine Learning
• +7 more

Prof. Dr.-Ing. Joachim Denzler is a Professor leading the Chair for Digital Image Processing (Lehrstuhl für Digitale Bildverarbeitung) at Friedrich-Schiller-Universität Jena. His research spans computer vision, machine learning, medical imaging, and remote sensing applications across diverse domains including biomedical analysis, environmental monitoring, and facial recognition systems. His research interests focus on explainable AI, causal discovery, physics-informed neural networks, and bias mitigation in deep learning models. He has made significant contributions to fine-grained classification, concept activation vectors, and privacy-preserving techniques in facial recognition. His work often bridges theoretical computer vision with practical applications in medical diagnostics, environmental science, and infrastructure monitoring. His recent publications demonstrate a strong trend toward causal discovery methods, physics-informed neural networks, and addressing bias in machine learning systems. His work spans both theoretical advancements in model interpretability and practical applications in medical imaging, environmental monitoring, and infrastructure safety. The interdisciplinary nature of his research is evident from collaborations with ecologists, neuroscientists, and civil engineers. Best Paper Award at International Conference on Pattern Recognition (ICPR) 2024 Prof. Denzler actively mentors numerous PhD students and postdoctoral researchers, with frequent co-authors including Maha Shadaydeh, Niklas Penzel, Gideon Stein, and Tim Büchner appearing as first authors on significant publications. His laboratory has secured research funding across multiple domains including medical imaging, environmental monitoring, and AI safety. His work on CausalRivers represents a major contribution to benchmarking causal discovery methods with real-world time series data. The Chair for Digital Image Processing maintains strong industry and clinical partnerships, particularly in medical imaging applications for facial palsy analysis and neonatal care. Prof. Denzler's team has developed novel techniques for dam deformation monitoring using remote sensing data and created advanced methods for analyzing plant diversity effects on ecosystem functioning.

Xiaoyuan XieView profile

Professor

• software testing
• fault localization
• AI for software engineering
• +6 more

Xiaoyuan Xie is a Professor at the School of Computer Science, Wuhan University, where he leads a research group focused on software engineering, particularly in software testing, fault localization, and the intersection of AI and software engineering. His work bridges theoretical foundations with practical applications in software quality assurance and development processes. His research interests span software testing, fault localization, program slicing and analysis, debugging techniques, search-based software engineering, evolutionary computing, and machine learning applications in software engineering. He has pioneered work in metamorphic testing, particularly for AI systems, and developed innovative approaches for fault localization that consider exception trigger streams and parallel debugging contexts. Xie's publication trend shows a strong focus on applying AI techniques to software engineering problems, with recent work emphasizing metamorphic testing for AI systems, code search, debugging assistance, and fault localization enhanced by deep learning. His research bridges traditional software engineering with modern AI techniques, creating novel solutions for testing and debugging complex software systems. ICST 2025 Most Influential 10-years Journal First Paper Award Multiple ACM SIGSOFT Distinguished Paper Awards (2024, 2022, 2021) CCF Open Source Innovation Competition First Prize 2024 CCF NASAC Software Innovation Award for Young Researchers 2021 ACM SIGEVO HUMIES 2017 Silver Medal Luojia Young Scholar, Wuhan University 2016 Professor Xie has supervised numerous PhD and Master's students who have gone on to prestigious positions at companies like Alibaba, Tencent, and ByteDance, as well as academic institutions including Yale University and the Hong Kong University of Science and Technology. His research is supported by multiple national grants, including NSFC重点项目 (key projects), 国家重点研发计划 (key R&D programs), and the Research Fund for International Excellent Young Scientists from NSFC.

• Computer Vision
• Metric Learning
• Image Generation
• +3 more

Dr. Xinyu Zhang is a Research Fellow at the Australian Institute for Machine Learning (AIML), University of Adelaide's Faculty of Sciences, Engineering and Technology. Her research bridges computer vision and machine learning, focusing on image/video generation, self-supervised learning, and multimodal retrieval for human-centric AI applications. Zhang's current investigations include: Causal representation learning and multimodal integration Bayesian deep learning frameworks Video generation with temporal consistency Lightweight detection transformers Unsupervised person re-identification Analysis of recent publications reveals strong emphases on generative modeling innovations (especially video synthesis), efficient transformer architectures for real-time applications, and self-supervised representation learning. Her work frequently addresses the alignment between latent representations and human perception across vision-language tasks. Dr. Zhang co-supervises graduate students on projects involving multi-agent 3D scene generation and knowledge transfer in low-supervision learning. She serves as conference reviewer for premier venues including CVPR, ICCV, and NeurIPS, contributing to the advancement of computer vision research.

Jacob MotaView profile

Assistant Professor

• Muscle Function Assessment
• Neuromuscular Adaptations
• Occupational Health (Firefighters/Law Enforcement)
• +4 more

Jacob Mota, Ph.D. , is an Assistant Professor in the Department of Kinesiology & Sport Management at Texas Tech University. His research focuses on neuromuscular adaptations and muscle function assessment across diverse populations including firefighters, law enforcement officers, and athletes. Ph.D. in Human Movement Science (2020), University of North Carolina Chapel Hill School of Medicine M.S. in Kinesiology (2016), Texas Tech University B.S. in Exercise and Sport Sciences (2014), Texas Tech University Dr. Mota's work examines the relationship between chronic health conditions and musculoskeletal injury risk in occupational athletes, with emphasis on resistance training adaptations and muscle quality analysis. His Neuromuscular and Occupational Performance Laboratory develops evidence-based strategies for injury prevention and performance optimization. Recent publications analyze firefighter health standards, muscle asymmetry impacts on occupational performance, and technological advancements in muscle assessment. His research integrates exercise physiology, biomechanics, and public safety health metrics. Outstanding Mentorship of Undergraduate Students in Research – University of Alabama (2022) Apple Polishing Award – TTU Mortar Board (2023) Dr. Mota teaches courses in applied exercise physiology and advanced strength conditioning, contributing to Texas Tech's Kinesiology undergraduate and graduate programs.

• Digital Health
• Wearables
• Connected Health
• +4 more

Dr. Rob Argent is a Lecturer in Digital & Connected Health at the Royal College of Surgeons in Ireland (RCSI), within the School of Pharmacy and Biomolecular Sciences. With a background as a Physiotherapist in the NHS, he transitioned to academic research through a PhD in Connected Health at University College Dublin (UCD). His work focuses on patient-facing digital health technologies, particularly wearables for musculoskeletal rehabilitation and chronic disease management. He is an editorial board member of npj Digital Medicine and holds certifications in health professions education. Education: BSc (Hons) Physiotherapy, Coventry University (2007–2010) PhD in Connected Health, University College Dublin (2016–2019) PGDip in Health Professions Education, RCSI (2021) Professional Certificate in University Teaching & Learning, UCD Research Interests: Rob's research bridges clinical practice and technology, emphasizing wearable sensor validation, digital biomarkers, and usability of health technologies. Key areas include gait analysis, fall detection algorithms, and the evaluation of consumer wearables. He advocates for stakeholder-driven digital health solutions aligned with UN Sustainable Development Goal 3 (Good Health). Grants & Projects: "Evaluation of novel digital biomarkers in Multiple Sclerosis" (Enterprise Ireland, 2023–2026) "CERBERUS: Certainty of Evidence Reports for Wearable Devices" (SFI, 2023–2024) "EPIVIEWS: Epilepsy Wearables Monitoring Study" (Irish Research Council, 2022–2025) Teaching Roles: Module Lead for Connected Healthcare and Internet of Medical Things in RCSI's MSc Technologies & Analytics in Precision Medicine program. Also contributes to leadership development, lifestyle medicine, and project management modules across multiple programs. Labs/Teams: Part of RCSI's Population Health research cluster and collaborates with UCD's Insight Centre for Data Analytics. His work aligns with interdisciplinary efforts to advance digital health innovation and clinical translation.

Cory HirschView profile

Associate Professor

• Plant Stress Resistance
• Abiotic Stress Biology
• Biotic Stress Responses
• +3 more

Cory Hirsch is an Associate Professor & Interim Department Head in the Department of Plant Pathology at the University of Minnesota , located in the College of Food, Agriculture and Natural Resource Sciences (CFANS) . His lab focuses on Plant Stress Resistance Biology , leveraging genomic, transcriptomic, and phenomic approaches to understand plant responses to abiotic (e.g., extreme temperatures, salinity) and biotic (e.g., pathogens) stresses. Key research areas include microbiome interactions, pathogen resistance mechanisms, and precision phenotyping. He leads or collaborates on grants from USDA, Minnesota Soybean Council, and the University of Minnesota. Education: PhD and BS in Plant Breeding/Genetics and Biochemistry from the University of Wisconsin-Madison. The Hirsch Lab is based in Stakman Hall , with a focus on translational research bridging basic science and agricultural applications. Research Themes: The lab uses cutting-edge technologies to dissect genomic variation and gene expression dynamics in crops like maize, wheat, and sugar beet. Recent work includes transposable element roles in stress responses, hyperspectral imaging for disease detection, and machine learning for phenotyping. Projects emphasize understanding stress biology to enhance crop resilience. Grants & Collaborations: Notable projects include USDA-funded studies on transposable elements in maize abiotic stress, Minnesota Soybean Council support for soybean disease phenotyping, and collaboration with the Microbial and Plant Genomics Institute. These efforts aim to improve crop productivity and sustainability. Labs & Infrastructure: The lab is equipped with advanced phenotyping tools, including RGB/hyperspectral imaging systems and drone-based platforms. It collaborates with multiple departments and external institutions to advance integrative plant science.

• Image and Video Processing
• Multimedia Analysis
• Computer Vision
• +5 more

Prof. Tan Yap Peng is a Professor and Chair of the School of Electrical & Electronic Engineering at Nanyang Technological University (NTU), Singapore. He holds the President's Chair in Electrical and Electronic Engineering and serves as Associate Vice President (Lifelong Learning – Postgraduate Programmes by Coursework). His research focuses on multimedia analysis, computer vision, machine learning, and data analytics. He earned his B.S. from National Taiwan University and M.A./Ph.D. from Princeton University. He has led major initiatives including the INFINITUS Infocomm Research Centre and contributed to IEEE technical committees. His over 200 publications span image/video processing, neural network robustness, and cross-modal systems. Awards include IEEE Fellow status. Education: B.S. Electrical Engineering (NTU), M.A./Ph.D. (Princeton) Research interests emphasize interactive digital media, content-based analysis, and AI-driven solutions for visual and signal processing. His work addresses challenges in adversarial attacks, video generation, and low-light image enhancement. He has held editorial roles at IEEE Transactions and EURASIP journals. Conference leadership includes chairs for ICME and ICIP. His contributions bridge academia and industry through collaborative research networks.

• Artificial Intelligence
• Machine Vision
• Distributed Computing
• +11 more

Dr. Dan Ionescu is a Professor at the University of Ottawa's School of Electrical Engineering and Computer Science, Department of Electrical and Computer Engineering. He has been affiliated with the university since 1985, contributing extensively to teaching and research. His career includes visiting professorships at École Nationale Supérieure de Télécommunications (Paris) and Universitat Politècnica de Catalunya (Barcelona). He founded the Machine Intelligence Research Laboratory (1988) and the Network Computing and Control Research Laboratory (NCCT, 1999), which he currently directs. His research spans Artificial Intelligence, Machine Vision, Distributed Computing, Network Control, and Formal Methods. Notable contributions include methodologies in Expert Systems, Image Processing, Temporal Logic, and Network Management. His work has received industrial and governmental grants from CITO, Nortel, NSERC, and others. Recent research focuses on Web-based collaborative platforms (e.g., UC-IC, Watch-Together), Autonomic Computing, and AI applications in gesture control and 3D IR camera systems. Dr. Ionescu’s technical leadership includes pioneering the first distributed network management platform with industry partners and designing the NCIT*net 2 network architecture. His current interests include AI-driven solutions for cybersecurity, medical imaging, and IoT-enabled disaster response systems. He has advised numerous projects in AI ethics, quantum computing, and real-time control systems. His research trends emphasize interdisciplinary innovation, combining AI with healthcare (e.g., MRI analysis, deepfake detection), cybersecurity (GAN-based intrusion detection), and IoT (Salv AIoT platform). Collaborations with IBM CAS and Diatem Networks highlight his industry-academia integration. His legacy includes over 40 years of impactful contributions to computing and engineering education.

Gaurav SharmaView profile

Professor

• Data Analytics
• Cyber Physical Systems
• Signal and Image Processing
• +3 more

Gaurav Sharma is a Professor at the University of Rochester, holding joint appointments in the Department of Electrical and Computer Engineering, Computer Science, and Biostatistics and Computational Biology. He is also a Distinguished Researcher at the Goergen Institute for Data Science and Artificial Intelligence. His research focuses on Data Analytics, Cyber Physical Systems, Signal and Image Processing, Computer Vision, Media Security, and Communications. Previously, he served as Director of the Center for Emerging and Innovative Sciences (2008-2010) and held roles at Xerox Research (1996-2003). He earned a PhD in Electrical and Computer Engineering from North Carolina State University (1996), alongside degrees from Indian institutions including the Indian Institute of Science and IIT Roorkee. Education: PhD (NCSU), M.S. Applied Mathematics (NCSU), M.S. Electrical Communication Engineering (IISc), B.Eng (IIT Roorkee) Awards: Fellow of IEEE, SPIE, and IS&T; Elected Sigma Xi member Editorial Roles: Editor-in-Chief of IEEE Transactions on Image Processing (2018-2020) and Journal of Electronic Imaging (2011-2015) Leadership: Chair of IEEE Signal Processing Society committees and SPIE/EI conferences His research spans interdisciplinary areas like medical imaging analytics, cybersecurity in IoT systems, and computational methods for biomedical applications. Recent work includes studies on cardiac mortality patterns, viral protein inhibition, and federated learning for privacy-sensitive healthcare tasks. Dr. Sharma’s contributions to academic governance include roles on IEEE publication committees and strategic planning boards, reflecting his commitment to advancing technical standards and open science practices.

Katja Dörschner BoyaciView profile

Professor

• Perception
• Computational Neuroscience
• Material Perception
• +5 more

Prof. Dr. Katja Dörschner Boyaci is a Professor at Justus-Liebig-Universität Gießen , Faculty of Psychology and Sports Science, leading the Perception & Active Exploration Group . Her research focuses on understanding how the human brain constructs rich perceptual experiences from sensory input, particularly in the perception of material properties like softness, glossiness, and roughness. Research Interests: Computational and neural mechanisms of material perception Integration of visual and haptic information Expectation-driven modulation of perception Neuroimaging (fMRI, EEG) and psychophysical approaches Virtual reality and computational modeling Her work combines psychophysics , neuroimaging , and computational modeling to explore how humans judge material properties from static and dynamic images, and how prior experiences shape these perceptions. Scientific Contributions: Published extensively on material perception, with recent papers in Nature Human Behaviour , Journal of Neuroscience , and Vision Research . Leads the collaborative B8 project on integrating experience and sensory information in material perception. Collaborations & Funding: Co-leads the B8 project with Prof. Hüseyin Boyaci, funded to investigate neural mechanisms of expectation-based perception. Employs interdisciplinary methods including EEG, fMRI, VR, and behavioral experiments. Laboratory & Team: The Perception & Active Exploration Group at Giessen University studies how humans perceive intrinsic object qualities through active exploration and sensory integration, with implications for product design, computer graphics, and robotics.