جست‌وجوی استاد و پژوهشگر

Douglas Galarusمشاهده پروفایل

دانشیار

• Intelligent Transportation Systems
• Data Science
• Spatio-Temporal Data
• +۳ مورد دیگر

Dr. Douglas Galarus serves as an Associate Professor in the Department of Computer Science and Software Engineering at Montana Technological University, leveraging 30+ years of expertise in systems engineering, data science, and intelligent transportation systems development. His career spans faculty roles at Utah State University and research leadership at Montana State University's Western Transportation Institute. His academic credentials include: Ph.D. in Computer Science, Montana State University (2017) M.S. in Computer Science, University of Montana (1995) M.A.T. in Mathematics Education, University of Montana (1993) B.A. in Mathematics, University of Montana (1989) Dr. Galarus specializes in applying data science to spatio-temporal data quality challenges within intelligent transportation systems. His research bridges theoretical methodology development with mission-critical production systems for road weather management, traveler information, and incident response. Key focus areas include Bluetooth/WiFi data fusion for winter travel delay estimation and quality assessment frameworks for environmental sensor networks. Publication analysis (2010-2021) reveals consistent innovation in spatio-temporal data quality methodologies, particularly for transportation and weather sensor applications. His work demonstrates progressive integration of multi-source data streams (ITS, Bluetooth, weather sensors) to solve practical winter mobility challenges while advancing theoretical quality assessment metrics. His research impact is recognized through: GEOProcessing Best Paper Award (2019) Computer Science Faculty Researcher of the Year at Utah State University (2018-19) Three national ITS America awards (2010, 2012, 2014) Dr. Galarus currently directs $226,000+ in active research funding including a $125,000 CALTRANS/WSDOT project on chain-up delay estimation and an NSF S-STEM grant. He has mentored numerous students through capstone projects and research labs, particularly during his leadership of the Western Transportation Institute's award-winning program. His current grants focus on real-time data fusion for rural transportation safety and STEM education initiatives. His labs have developed nationally recognized tools including the One-Stop-Shop traveler information system and Automated Safety Warning System Controller, establishing partnerships with 11 western state DOTs through the Western States Rural Transportation Consortium. These initiatives emphasize practical implementation of data science solutions for rural transportation challenges.

• Bayesian Statistics
• Spatio-temporal Data Analysis
• Environmental Statistics
• +۳ مورد دیگر

Sujit Sahu is a Professor of Statistics at the University of Southampton with a focus on Bayesian modeling and spatio-temporal data analysis. He co-leads the Statistics group within the Statistical Sciences Research Institute (S3RI) , contributing to environmental and health-related statistical research. Books : Author of Bayesian modeling of spatio-temporal data with R (Chapman & Hall/CRC Press) and Introduction to Probability, Statistics & R: Foundations for Data-Based Sciences (Springer Nature, 2024). Research Themes : Applied Bayesian modeling, environmental/climate statistics, spatio-temporal uncertainty quantification, and statistical software development (R package bmstdr ). Grants : Sole investigator for EPSRC grant EP/J017485/1 (2013-2016, £365k) and key contributor to multi-institution projects like NERC grant NE/J012238/1 (2012-2015, £475k). Publication Trends : 15 most recent works show a consistent focus on Bayesian spatio-temporal methods applied to epidemiology, environmental monitoring, and public health policy. Key subfields include disease mapping, air pollution exposure assessment, oceanographic data analysis, and statistical learning algorithm development. Scientific Recognition : His 2022 Bayesian spatio-temporal textbook was honored as one of the Book Authority Best Bayesian Statistics Books of All Time .

Harry Kai-Ho Chanمشاهده پروفایل

مدرس

• Data Mining
• Data Science
• Big Data
• +۷ مورد دیگر

Dr. Harry Kai-Ho Chan is a Lecturer in Data Science at the University of Sheffield's School of Information, Journalism and Communication. He holds a BEng, MPhil, and PhD from The Hong Kong University of Science and Technology (HKUST). His research focuses on data mining, big data analytics, and spatial data management, with particular emphasis on spatio-textual data, indoor location-based services, and applying machine learning in databases. Before joining Sheffield in 2022, he was a Postdoc researcher on the "Data Management for Indoor Location-based Services" project at Roskilde University (2020-2022). He currently serves as the Interim Deputy Programme Coordinator for the BSc Data Science program and the Department Employability Lead for Data Science. His work addresses efficient query processing for spatial data, co-location pattern mining, and uncertainty-aware systems in indoor environments. He has contributed to over 15 peer-reviewed publications in top venues like IEEE TKDE and VLDB. Teaching responsibilities include courses like INF114 (Communicating Data), INF217 (Databases and beyond), and INF4000 (Data Visualization). He actively reviews for leading conferences (e.g., SIGKDD, ICDE) and journals (e.g., IEEE TKDE, GeoInformatica).

Yidan Zhangمشاهده پروفایل

پژوهشگر ارشد

• Spatial-Temporal Data Sensemaking
• Human-Computer Interaction
• Immersive Analytics
• +۵ مورد دیگر

Yidan Zhang is a Research Fellow at Monash University's Department of Human Centred Computing, specializing in spatial-temporal data analysis within immersive environments. She holds a PhD from Monash (2024), MSc in Information Technology (2019), and a BA in Arts from Jinan University (2016). Her work focuses on embodied interaction, immersive analytics, and energy visualisation, contributing to UN SDGs in education and sustainable energy. Research Interests : Spatio-Temporal Data Sensemaking Immersive Analytics Embodied Provenance Eye Tracking in Immersive Systems Energy Efficiency Visualisation Recent Research Trends : Her publications emphasize embodied interaction design, immersive data exploration tools, and energy-aware visualisation systems. Notable contributions include frameworks for tracking user interactions in immersive environments and improving user engagement in energy management. Lab/Teams : Member of the Embodied Visualisation research group, collaborating on multidisciplinary projects involving immersive technology and human-computer interaction.

James Martinمشاهده پروفایل

استاد

• Wildlife Ecology
• Wildlife Management
• Gamebird Conservation
• +۵ مورد دیگر

Dr. James Martin is a Professor of Wildlife Education at the University of Georgia's School of Forestry and Natural Resources. He holds a PhD in Forest Resources from UGA (2010) and a B.S. in Environmental Studies from the University of North Carolina Asheville (2003). His research focuses on three core themes: landscape ecology and management, gamebird (particularly quail) ecology, and sustainable agriculture/forestry. He integrates quantitative methods and collaborates with agencies to enhance wildlife conservation in managed ecosystems. His teaching includes courses on conservation decision-making, wildlife habitat principles, and internships. His research emphasizes northern bobwhite populations, predator management impacts, and the ecological effects of forestry practices. Recent studies address translocation success, pathogen surveillance, and the efficacy of monitoring technologies. Dr. Martin leads the SREL Lab and maintains an affiliation with Google Scholar. His work bridges ecological theory and applied land management, with a focus on wildlife-friendly agricultural practices and biodiversity conservation in working forests. Notable contributions include studies on bobwhite density in private forests, gopher tortoise burrow ecology, and the effects of thinning on wildlife habitat. His articles often analyze spatio-temporal dynamics, predation risk, and the economic feasibility of conservation practices. While no formal awards are listed, his prolific publication record reflects his leadership in North American gamebird conservation science.

• Precision/Digital Agriculture
• Remote Sensing
• Internet of Things (IoT)
• +۵ مورد دیگر

Dr. Dharmendra Saraswat is a Professor in the Department of Agricultural & Biological Engineering at Purdue University, College of Engineering. His research focuses on precision agriculture, remote sensing, and IoT applications for crop and natural resource management. He holds a Ph.D. from The Ohio State University and has held previous faculty positions at the University of Arkansas and Chandra Shekhar Azad University of Agriculture & Technology. His work bridges engineering and agriculture, emphasizing data science tools, decision support systems, and unmanned aerial systems (UAS). Education: Bachelor’s in Agricultural Engineering, University of Allahabad Master’s in Agricultural Engineering, Indian Agricultural Research Institute (IARI), New Delhi Ph.D. in Food, Agricultural & Biological Engineering, The Ohio State University Research Interests: Dr. Saraswat’s work integrates information technologies with agricultural challenges, including spatio-temporal modeling, machine learning for precision farming, and watershed management. He develops cloud-based tools and mobile applications to enhance agricultural decision-making. His expertise spans remote sensing, GIS, and UAS sensor data analysis. Awards: Fellow of Indian Society of Agricultural Engineers (2014) John W. White Outstanding Extension Award (2014) ASABE Educational Aids Blue Ribbon Award (2015/2013) Excellence in Remote Sensing (2013) Early Career & Innovation Awards (2011) Lab & Collaborations: His research leverages facilities like Purdue’s ADM Agricultural Innovation Center and Discovery Park. He collaborates on projects involving digital agriculture, environmental modeling, and agricultural robotics. Key contributions include disease detection algorithms, UAV-based systems, and climate impact assessments.

Djamila Aouadaمشاهده پروفایل

استادیار

• Computer Vision
• Signal Processing
• Image Processing
• +۳ مورد دیگر

Djamila Aouada is an Assistant Professor and Senior Research Scientist at the University of Luxembourg's Interdisciplinary Centre for Security, Reliability and Trust (SnT), where she heads the Computer Vision, Imaging, and Machine Intelligence (CVI2) research group. She earned her State Engineering degree from École Nationale Polytechnique, Algeria, and PhD from North Carolina State University. Her research spans computer vision, signal processing, pattern recognition, and data modeling. Dr. Aouada leads several national and European projects including FNR FAVE, 3D-Act, and H2020 STARR, focusing on AI-driven solutions for industrial and security applications. She has received four IEEE Best Paper Awards for her contributions. Dr. Aouada has supervised 5 completed PhD theses and currently mentors 5 PhD candidates, while maintaining collaborations with Los Alamos National Laboratory and Mitsubishi Electric Research Labs. She serves as Senior IEEE Member and previously chaired IEEE Benelux Women in Engineering.

Jonathan Leachمشاهده پروفایل

استاد

• Quantum Optics
• Quantum Information
• Quantum Imaging
• +۲ مورد دیگر

Professor Jonathan Leach leads the Quantum Optics and Computational Imaging group at Heriot-Watt University's School of Engineering & Physical Sciences. His experimental quantum optics research develops novel methods for generating, manipulating, and detecting quantum states of light for secure communication and ultra-sensitive sensing. Work bridges quantum information science and computational imaging, with applications including high-dimensional entanglement systems, quantum state measurement techniques, and fundamental limits of super-resolution imaging. Publications demonstrate advanced quantum imaging techniques, with recent focus on single-photon lidar enhancement, quantum ghost imaging, and structured light manipulation. Research consistently integrates quantum phenomena with computational methods. Switched On Award for Most Engaging Lecturer (2015) Group investigates quantum technologies for achieving performance advantages in computation, communication, and imaging beyond classical limits.

Hamdy Mahmoudمشاهده پروفایل

استادیار

• Semi/nonparametric regression models
• Change point detection
• Environmental statistics
• +۳ مورد دیگر

Hamdy Mahmoud is a Collegiate Assistant Professor at Virginia Tech's Department of Statistics within the College of Science. He also serves as an Assistant Professor at Assiut University in Egypt. His academic journey includes a Ph.D. in Statistics from Virginia Tech (2014) under Inyoung Kim, an M.S. in Statistics from both Virginia Tech (2010) and Cairo University (2005), and a B.S. in Statistics from Cairo University (1996). His research focuses on semi/nonparametric regression models, change point detection, environmental statistics, and spatial/spatio-temporal analysis. Notable contributions include applications in environmental health and cardiovascular mortality studies. He has published extensively in journals like Computational Statistics & Data Analysis and Environmental Metrics . Teaching highlights include courses such as Applied Bayesian Statistics and Statistical Methods I/II at Virginia Tech. He has received awards including the Jesse C. Arnold Teaching Excellence Award (2012-2013) and induction into the Mu Sigma Rho Honor Society (2011). His work bridges statistical theory and practical applications in health sciences and environmental studies. Professional Memberships: American Statistical Association (ASA), International Biometric Society (ENAR) Recent Awards: Travel Fund Award (2014), Teaching Excellence nominations (2014) Dr. Mahmoud’s research trends emphasize robust statistical methods and their environmental and medical applications. His publications often address complex data structures through semi/nonparametric frameworks, with a focus on change point analysis and spatial-temporal modeling.

• Computer Vision
• Machine Learning
• Action Recognition
• +۶ مورد دیگر

Prof. Juergen Gall is a Professor at the University of Bonn, affiliated with the TRA Mathematics, Modelling and Simulation of Complex Systems and the Department of Information Systems and Artificial Intelligence. He leads the Computer Vision Group at the Lamarr Institute for Machine Learning and Artificial Intelligence. His research focuses on action recognition, video understanding, anticipation, human pose estimation, and applications in plant science, earth science, and neuroscience. He has contributed to numerous projects and conferences, including organizing workshops on machine learning for Earth systems and holistic video understanding. His work spans over 200 publications in top venues like CVPR, ICCV, and NeurIPS. He oversees software tools like MANTA and STING-BEE, and has developed datasets such as Humans in Kitchens and PoseTrack21. Research interests emphasize advancing computer vision for real-world challenges, with a focus on multi-modal learning and deep learning applications. Recent articles explore parameter-efficient models, diffusion-based anticipation, and multi-view matching for plant analysis. His contributions bridge academia and industry, addressing agricultural and environmental monitoring needs.

Xingchen Zhangمشاهده پروفایل

مدرس

• Human Intention Prediction
• Human Pose Estimation
• Image Fusion
• +۳ مورد دیگر

Dr. Xingchen Zhang is a Marie Skłodowska-Curie Individual Fellow at the Personal Robotics Laboratory (PRL), Imperial College London, and holds a Lecturer position in the Department of Electrical and Electronic Engineering. Previously, he served as a Teaching Fellow and Research Associate at Imperial College. He teaches Deep Learning courses and is actively involved in academic reviews for prestigious programs like UKRI Future Leaders Fellowships and top conferences/journals such as CVPR, ECCV, and IEEE TNNLS. Xingchen earned his BSc from Huazhong University of Science and Technology (2012) and PhD from Queen Mary University of London (2018). His research focuses on human motion prediction, pose estimation, image fusion techniques (visible-infrared, multi-focus), visual object tracking, and deep learning applications in computer vision. His work has led to a notable book on image fusion, awarded the National Science and Technology Academic Publications Fund in China (2019). His recent publications emphasize real-world applications such as traffic flow prediction, pedestrian privacy protection, and fusion-based object tracking. He contributes to advancing graph neural networks for mobility analysis and spatio-temporal modeling, while maintaining a strong focus on interdisciplinary robotics and AI solutions. Awards: Marie Curie Fellowship, National Science & Technology Academic Publications Fund (2019) Key Roles: Reviewer for UKRI FLF, CVPR, ECCV, IJCV Labs: Personal Robotics Lab (PRL), Imperial College London

• Optical sensors
• Fiber Bragg grating sensor
• Measurements
• +۱۰ مورد دیگر

Alberto Vallan is an Associate Professor in the Department of Electronics and Telecommunications at Politecnico di Torino, Italy. He is actively engaged in research, teaching, and academic leadership, particularly in the fields of measurement science, optical sensors, and biomedical instrumentation. Research Interests: His work focuses on Fiber Bragg Grating sensors , optical and plastic fiber sensors , measurement uncertainty , and biomedical device development . He also contributes to low-cost space systems and lyophilization process monitoring . His research integrates metrology, photonics, and real-world applications in healthcare and industry. The recent publications highlight a strong trend in fiber-optic sensing for medical diagnostics , smart wearable systems , and real-time monitoring technologies . These works span applications from cancer therapy and mental stress detection to cultural heritage preservation and space-based power delivery, reflecting a highly interdisciplinary approach. Scientific Honors: Elevation to IEEE Senior Member (2012) IEEE Fellow (2013) IEEE IMS Outstanding Reviewer (2014) Guest Editor, Sensors (2023–2024) Advising and Grants: He supervises PhD students such as Chiara Bellezza Prinsi, Matteo Raggi, and Aurora Bellone, whose projects include smart textiles and tumor therapy monitoring. He has led multiple funded research projects, including PRIN-funded work on intrinsic optical fiber sensors and commercial projects on energy harvesting and lyophilization monitoring. His work is co-financed by entities like the European Union (NextGenerationEU). Labs and Research Groups: He is affiliated with the Electrical and Electronics Measurements and Nanosatellites research groups at DET. He also collaborates with Advanced Magnetic Controls A.M.C. srl and industry partners like H-Cube srl.

• Host-pathogen interactions
• Mycobacterium tuberculosis
• Advanced imaging techniques
• +۳ مورد دیگر

George Ashdown is a Research Fellow at the School of Biosciences, part of the College of Health and Life Sciences at Aston University. He leads a research program focusing on host-pathogen interactions, particularly targeting intracellular Mycobacterium tuberculosis using advanced imaging and 'omics techniques. His work integrates super-resolution microscopy with machine learning to study drug delivery systems and disease biomarkers. He holds a PhD from King's College London (2017) on T cell immunological synapse dynamics and an MSc from the University of Edinburgh (2011) in Quantitative Cell and Molecular Imaging. His research has spanned malaria parasite lifecycle analysis using high-throughput imaging and AI-driven drug response studies. Ashdown's expertise spans lattice light-sheet microscopy, AI applications in biological imaging, and the development of novel drug delivery systems. He actively supervises PhD projects in host-pathogen interactions and lipid-based therapies. His contributions include pioneering methods to quantify molecular dynamics in T cells and advancing automated malaria diagnostics using convolutional neural networks. Current projects emphasize leveraging spatiotemporal data from cutting-edge microscopy for clinical translation. Research outputs highlight interdisciplinary approaches combining microscopy innovation with computational biology. He collaborates extensively on malaria and tuberculosis projects, contributing to both fundamental understanding and applied therapeutic development.

• Bayesian Statistics
• Gaussian Processes
• Variational Inference
• +۵ مورد دیگر

Michael Riis Andersen is an Associate Professor at the Department of Applied Mathematics and Computer Science, DTU Compute , Technical University of Denmark. His research focuses on Bayesian statistics, Gaussian processes, variational inference, and probabilistic programming, with applications in computer vision, natural language processing, and spatio-temporal modeling. His recent work includes: (1) Bayesian optimization with uncertainty-aware frameworks, (2) recommender systems balancing accuracy with editorial constraints, (3) geometry-aware transformers for geospatial analysis, and (4) scalable inference methods for neural networks and Gaussian processes. Many articles explore variational inference and probabilistic programming to enhance computational reliability and efficiency. Research keywords : Bayesian Statistics, Variational Inference, Machine Learning, Computer Vision, Probabilistic Programming, Spatio-Temporal Modeling.

Stuart Clarkمشاهده پروفایل

استاد

• Sedimentary basin dynamics
• Machine learning applications in geology
• Numerical simulations of Earth processes
• +۵ مورد دیگر

Professor Stuart Clark is a distinguished academic at the University of New South Wales (UNSW), currently serving as Professor in the Civil and Environmental Engineering department within the Faculty of Engineering. He was appointed Director of Governance for the Faculty of Engineering in 2024 and promoted to Professor in 2025. Previously, he was an Associate Professor (2021-2025) and Senior Lecturer (2017-2021) in the Minerals and Energy Resources School at UNSW. His educational background includes a PhD in Geophysics from the University of Sydney (2007), Master of Arts from the University of Melbourne (2004), and BSc.(Hons)/B. Arts from the University of Sydney (2002). He also earned a Graduate Certificate in University Learning and Teaching from UNSW in 2022. Professor Clark's research focuses on understanding the influence of deep Earth processes on sedimentary basin development and applying machine learning to geological modeling. His work spans quantitative sedimentary basin dynamics, numerical simulations of Earth processes like subduction and sediment transport, and innovative applications at the intersection of geology and machine learning. His research has significant implications for sustainable resource use and exploration. His recent publications reveal a strong emphasis on basin analysis, particularly in the Northern Carnarvon Basin, with applications spanning petroleum geology, resource distribution, and geological modeling using advanced statistical methods like Bayesian inference. His work increasingly bridges traditional geology with computational approaches, including machine learning applications for fracture detection, fault identification, and image analysis of geological structures. Arc Postgraduate Council's Supervisor Award (2021) UNSW Vice Chancellor's Teaching Excellence Award - Rising Star (2019) UNSW Engineering Hero Award (2020) Professor Clark has successfully secured multiple significant research grants, including the Geodynamical Assessments of Subsidence in the North West Shelf in Australia (2024-2025), the ARC Industry Transformation Research Hub for Resilient and Intelligent Infrastructure Systems (2021-2027), and several Australian Research Council Linkage projects. He currently supervises numerous PhD and research master's students working on diverse projects related to basin evolution, dynamic topography, fracture mechanics, and resource exploration. His teaching portfolio includes undergraduate and postgraduate courses in geology, sedimentary and energy resources, and seismic imaging.