جست‌وجوی استاد و پژوهشگر

Yu-Wing Taiمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Yu-Wing Tai is an Associate Professor of Computer Science at Dartmouth College, one of the Ivy League institutions. His research focuses on Computer Vision, Deep Learning, and Multimodalities Generative AI. He holds a PhD from the National University of Singapore and has served as an Associate Editor for IEEE TPAMI and as an Area Chair for major conferences like CVPR, ICCV, and NeurIPS. He is a Senior Member of the IEEE. Previously, he held roles as a Senior Research Director at Kuaishou Technology (2020-2023), Adjunct Professor at HKUST (2017-2023), and Research Director at Tencent (2017-2020). His work spans low-level vision, image/video editing, 3D reconstruction, and applications of large generative models. His research highlights include Motion-Agent for human motion generation, ChatCam for camera control, and Gear-NeRF for 3D reconstruction. He advises a team of PhD students and has mentored alumni now in academia and industry worldwide. His teaching includes courses on Multi-modalities Generative AI and Computer Graphics.

James McGreeمشاهده پروفایل

استاد

• Statistical Methodology
• Bayesian Design
• Clinical Trial Design
• +۱ مورد دیگر

Professor James McGree is Professor of Statistics in the School of Mathematical Sciences at Queensland University of Technology, holding a PhD from the University of Queensland. His research focuses on optimal experimental design, Bayesian adaptive methodology, and innovative clinical trial development. Research interests span: Bayesian adaptive designs for clinical and ecological applications Statistical methodology for complex spatial and temporal data Optimization of sampling strategies for environmental monitoring His publication portfolio demonstrates extensive methodological development in Bayesian statistics applied to environmental science, healthcare, and industrial optimization. Recent work includes flood mitigation strategies, coral reef monitoring, and clinical trial designs for rare diseases. Professor McGree actively supervises doctoral candidates working on Bayesian system identification, subsampling methods for big data, and robust clinical trial designs.

• Artificial Intelligence in Healthcare
• Medical Imaging Analysis
• Brain Connectomics
• +۳ مورد دیگر

Liang Zhan is an Associate Professor at the Swanson School of Engineering, University of Pittsburgh. He holds a Ph.D. from the University of California, Los Angeles (2011). His research focuses on applying artificial intelligence and machine learning to neuroimaging analysis, particularly in Alzheimer’s disease and brain connectomics. Key areas include deep learning frameworks for structural and functional brain imaging, graph neural networks for multimodal data fusion, and explainable AI for disease diagnosis. His work integrates advanced computational methods with clinical neuroscience, addressing challenges in biomarker discovery, dynamic brain network analysis, and uncertainty quantification. Recent studies explore excitation-inhibition balance alterations linked to neurodegenerative risks and APOE-ε4 genotype effects. He has pioneered methods like Brain Posterior Evidential Networks (BPEN) and SIN-Seg for robust medical image analysis. Zhan’s publications emphasize cross-modal data integration, with contributions to frameworks like Zeus (zero-shot LLM instruction for segmentation) and NeuroCave (web-based visualization platform). His research bridges theoretical advances in graph representation learning and practical applications in neuroimaging diagnostics.

• Life and Health Sciences
• Infectious Disease Modelling
• Non-equilibrium Statistical Mechanics
• +۴ مورد دیگر

Dr. Massimo Cavallaro is a Research Fellow at the University of Leicester's School of Computing and Mathematical Sciences, specializing in quantitative approaches to life and health sciences. His work integrates mathematical modeling, machine learning, and computational biology to address challenges in public health, infectious disease dynamics, and molecular biology. Before joining Leicester, he was a Research Fellow at the University of Warwick, focusing on mRNA transcription mechanisms and systems biology. He holds a PhD in Applied Mathematics from Queen Mary University of London and an MSc in Theoretical Physics from the University of Catania, Italy. His research interests span infectious disease modeling, non-equilibrium statistical mechanics, gene expression regulation, and the application of explainable AI to healthcare. He has contributed to studies on cocaine use patterns, SARS-CoV-2 variant surveillance, and antimicrobial stewardship strategies. Key publications include analyses of adolescent substance use trajectories, spatio-temporal surveillance of viral pathogens, and computational methods for understanding transcriptional bursting. His work bridges theoretical frameworks with real-world public health applications.

• Urban Intelligence
• Smart Cities
• Climate Mitigation
• +۷ مورد دیگر

Dirk Ahlers is a Senior Researcher and Research Manager at the Department of Architecture and Planning, Norwegian University of Science and Technology (NTNU), within the Faculty of Architecture and Design. He leads projects such as ClimaGen, +CityxChange, NEB-STAR, and Re-Value, focusing on urban regeneration, climate action, and smart city initiatives. His work bridges disciplines like computer science, geospatial analysis, and urban planning to address climate challenges. He coordinates EU-funded projects and contributes to frameworks like the New European Bauhaus and the EU Cities Mission. Education: PhD in computer science and geospatial analysis. Background includes postdoctoral research at the Data and Information Management group and roles in the Department of Computer Science, focusing on Urban Intelligence, mobility, and emission monitoring. Research Interests: Urban Intelligence, Smart Cities, Climate Mitigation, Sustainability, Geospatial Analysis, and Information Retrieval. Projects emphasize data-driven approaches for urban transitions, carbon neutrality, and stakeholder engagement. Grants & Projects: Key roles in ClimaGen, +CityxChange (completed), NEB-STAR, Re-Value, CrAFt, and others. Contributions to EU Smart City Marketplace and SCC1 Lighthouse Group strategies. Labs & Teams: Involved with NTNU Smart Sustainable Cities group and initiatives like UBIMOB (data-driven mobility), SCIL (Smart City Infrastructure Lab), and FME-ZEN (Zero Emission Neighborhoods).

• Spatio-temporal statistics
• Computational statistics
• Geophysics
• +۴ مورد دیگر

Jo Eidsvik is a Professor at the Department of Mathematical Sciences, NTNU. His research focuses on spatio-temporal statistics, computational statistics, and their applications in Earth sciences, geophysics, and data assimilation. He leads projects like the Center for Geophysical Forecasting (SFI-CGF), GEOPARD, and MASCOT. He has authored a seminal book on 'Value of Information in the Earth Sciences' (2017), recognized with a runner-up award by INFORMS. Eidsvik has supervised numerous PhD and MSc students, including current advisees André Olaisen and Oscar Ovanger. He holds editorial roles in 'Mathematical Geosciences' and 'Statistics and Computing'. Education: MSc in Applied Mathematics (University of Oslo, 1997), PhD (NTNU, 2003). Industry experience includes roles at the Norwegian Defense Research Establishment and Statoil. He has been a visiting scholar at Stanford University and Duke University. Research highlights include Bayesian inversion methods, adaptive sampling with autonomous vehicles, and value of information analysis in decision-making. Recent work explores machine learning for irregular time series (ML4ITS) and oceanographic data assimilation. Over 60 MSc theses supervised, emphasizing interdisciplinary approaches. Labs/Teams: Center for Geophysical Forecasting (SFI-CGF), GEOPARD, MASCOT, Games. Grants/Awards: INFORMS Decision Analysis Society award (2017), extensive project funding through SFI and industry. Key Projects: Havvarsel (ocean data), CO₂ storage monitoring, and robotic sampling systems.

Carlos Duque Calvacheمشاهده پروفایل

دانشیار

• Hydrogeology
• Groundwater numerical modelling
• Groundwater-surface water interaction
• +۳ مورد دیگر

Carlos Duque Calvache is an Associate Professor II (20% part-time) in the Department of Geosciences at the University of Oslo. His academic career includes roles as an Assistant Professor at Aarhus University (Denmark), Postdoctoral Research Fellow positions at the University of Oslo and University of Copenhagen, and a Marie Curie IOF fellowship at the University of Delaware (USA). His primary research focuses on hydrogeology, groundwater numerical modeling, and coastal aquifer dynamics. Dr. Calvache’s research explores groundwater-surface water interactions, saltwater intrusion, and the application of heat as a tracer in hydrological systems. He has contributed to studies on the Motril-Salobreña aquifer in Spain, tidal effects on coastal aquifers, and geophysical methods for aquifer characterization in mining sites. He actively supervises MSc students and collaborates on interdisciplinary research projects. His publications emphasize methodological advancements in groundwater modeling, including type curves for flow estimation and dilution test applicability in vertical flow systems. Though no scientific awards are explicitly noted, his work demonstrates sustained contributions to environmental geosciences and hydrological engineering.

• Artificial Intelligence
• Machine Learning
• Autonomous Systems
• +۴ مورد دیگر

Loo Junn Yong is a Lecturer at the Malaysia School of Information Technology, Monash University, specializing in artificial intelligence, machine learning, and autonomous systems. His research focuses on applying advanced algorithms to real-world challenges in robotics, brain network analysis, and traffic safety. He leads projects such as 'Efficient and Interpretable Multi-view Deformable Transformer with 3D Position Embeddings and Intelligent Queries for Autonomous Driving' and collaborates on ASEAN NCAP assessment protocols for vehicle safety. His work spans generative models, trajectory prediction, and ethical decision-making in autonomous systems. Notable contributions include frameworks for brain disorder identification using dynamic graph representation and energy-efficient trajectory planning for electric vehicles. He actively publishes in top conferences like IJCAI and IEEE SMC. His research aligns with UN SDGs through innovations in sustainable transport and health diagnostics.

Ante Seletkovićمشاهده پروفایل

دانشیار

• Remote Sensing
• GIS Applications
• LiDAR in Forestry
• +۳ مورد دیگر

Assoc. Prof. Ante Seletković holds a PhD and serves as an Associate Professor at the Faculty of Forestry and Wood Technology, University of Zagreb , specifically in the Department of Wood Technology. His roles include teaching courses like Spatio-temporal analysis in GIS and Collection and processing of 3D data . Located in the Green Building (Room 317), he advises via email and Tuesdays 08:00-10:00. Professional Background: Associate Professor (2013–present) Assistant Professor (2009–2013) Senior Assistant (2008–2009) Junior Researcher (2000–2008) Research Focus: Specializing in remote sensing , LIDAR applications , and GIS integration , his work emphasizes spatial data analysis, aerial imagery interpretation, and forest inventory methodologies. He has pioneered studies on urban green spaces, forest health monitoring using hyperspectral imaging, and drone-based photogrammetry. Key Projects: 3D-FORINVENT photogrammetry project Urban heat island analysis in Zagreb Mistletoe infestation detection in fir forests LIDAR accuracy assessments in oak forests Awards: None explicitly listed, but his extensive publication record (over 50 articles since 2000) and sustained academic contributions indicate significant recognition in forestry technology. Teaching & Labs: Oversees labs focused on spatial technologies and leads graduate programs in Techniques, Technologies and Management in Forestry . Collaborates with institutions like the Croatian Forest Research Institute and the University's Wood Technology department.

• Wireless Networks
• Edge Computing
• Reinforcement Learning
• +۳ مورد دیگر

Prof. Hassine Moungla is a Lecturer at Telecom SudParis, associated with the SAMOVAR research institute and the NeSS group. His research focuses on wireless networks, edge computing, reinforcement learning, IoT systems, vehicular networks, and machine learning applications in network optimization. He has contributed to over 50 publications since 2004, addressing topics such as UAV-assisted networks, intelligent reflecting surfaces (IRS), and blockchain-based network slicing. Education: PhD in Computer Science (focus on network management and policy-based systems). Key Research Themes: Edge computing architectures, reinforcement learning for network optimization, IoT mobility management, and vehicular networks. His work often intersects with industry challenges in 5G/6G, smart cities, and autonomous systems. Notable projects include optimizing UAV deployment using deep learning and developing anomaly detection mechanisms for mobile networks. Grants & Collaborations: Active in EU-funded projects on edge computing and has collaborated with institutions like INRIA and the University of Paris-Saclay.

Anna Petrasovaمشاهده پروفایل

پژوهشگر

• Geospatial Analytics
• Environmental Modeling
• Open Source Software Development
• +۳ مورد دیگر

Anna Petrasova is a Geospatial Software Engineer at North Carolina State University (NCSU), specializing in dynamic spatio-temporal modeling of urbanization, pest/pathogen spread, and geospatial simulation interfaces. She holds a Ph.D. in Geospatial Analytics and is a core contributor to the open-source GRASS GIS project. Her work focuses on participatory modeling, cloud-based geospatial platforms, and intuitive GUI design for collaborative decision-making in environmental and urban planning contexts. As a member of the OSGeo Foundation and GRASS GIS Steering Committee, she advocates for open-source software in research and education. Research interests span geospatial analytics, environmental modeling, and participatory approaches to addressing climate change and invasive species threats. She has developed tools like Rapid-DEM for topographic updates and GRASS_FUTURES for urban growth simulation. Her 15 most recent articles (2021–2024) emphasize open-source platforms, stakeholder engagement in modeling, and applications of GIS in flood risk reduction, invasive species forecasting, and landscape dynamics. Contributions include integrating FUTURES and PoPS models into GRASS, as well as Jupyter Notebook workflows for geospatial education. Anna collaborates with institutions like the Tangible Landscape Lab and has led over 900 code contributions across repositories, including core GRASS GIS development and educational tutorials.

• Statistics
• Spatio-Temporal Data Analysis
• Stochastic Modeling
• +۴ مورد دیگر

Adina Alexandra Iftimi is an Associate Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. She is a member of the PROMEDyA research group, focused on Prediction and Optimization under Uncertainty through dynamic stochastic models and their applications. Research Interests: Her work centers on statistical methods for spatio-temporal data, stochastic processes, and modeling under uncertainty. These interests align with broader fields in statistics, operations research, and applied probability, with potential applications in environmental science, public health, and risk assessment. Education: Ph.D. in Statistics and Operations Research, University of Valencia (2017) Scientific Affiliation: Research Group: PROMEDyA – Prediction and Optimization under uncertainty: dynamic stochastic models and applications She has not listed any publications or scientific awards in the provided text. There is no mention of students she may have supervised or grants she has led. Her active research profile suggests ongoing contributions to statistical methodology and stochastic modeling.

• Smart Factory Optimization
• Wearable Technology in Healthcare
• Sustainable Business Models
• +۳ مورد دیگر

Dr. Rachel Sales is a Research Fellow in Management Science at the University of Strathclyde’s Strathclyde Business School. She specializes in optimizing industrial processes and healthcare technology applications. Her work intersects smart factory operations, wearable health monitoring, and sustainable business models. Affiliations: Digital Health & Wellness Research Group (DHaWG), EPSRC-funded Doctoral Training Partnership (2018-2025) Her research focuses on spatio-temporal worker movement analysis for factory safety, machine learning-driven scheduling frameworks, and garment reprocessing for circular economies. She contributed to the FAIM 2025 Sullivan Best Paper Award-winning study on sustainable business models. Awards: FAIM 2025 Sullivan Best Paper Award (2025) Her projects include a £13,234 EPSRC-funded initiative exploring digital health technologies. She collaborates with interdisciplinary teams on topics like Postural Tachycardia Syndrome monitoring using wearables and ultra-wideband tracking systems.

Cindy Chenمشاهده پروفایل

دانشیار

• Spatio-temporal Databases
• Data Mining
• Cloud Computing
• +۴ مورد دیگر

Cindy Chen serves as Department Chair and Associate Professor at the Miner School of Computer and Information Sciences within the Kennedy College of Sciences at the University of Massachusetts Lowell. Her academic journey includes a Ph.D. in Computer Science with specialization in Database and Knowledge Base Management from UCLA (2001), an MS in Computer Science from UCLA, and a BS in Space Physics from Peking University. Ph.D.: Computer Science (Database and Knowledge Base Management), University of California at Los Angeles (2001) MS: Computer Science, University of California at Los Angeles BS: Space Physics, Peking University - Beijing, China Her research spans spatio-temporal databases, data mining, cloud computing, and social network analysis. Early work focused on spatio-temporal data models and query languages , evolving into cloud data warehouse optimization and social network mining (particularly online dating behavior), then advancing to high-dimensional indexing , knowledge graph querying , and recent innovations in temporal database granulation using Allan Variance techniques for CAV friction modeling. Current work integrates database systems with autonomous vehicle infrastructure and real-time urban analytics. Her publication timeline reveals a transition from foundational spatio-temporal database systems (1998-2008) to cloud/social applications (2008-2015), then to granulation techniques and CAV systems (2015-present). Key domains include XML databases, skyline queries, social trend discovery, and friction data modeling for autonomous vehicles. Dean's Fellowship (1998) Dean's Fellowship (1999) Dr. Chen has secured significant research funding including the UMASS Transportation Center grant for an Interactive TMC Decision Support Tool (2008) and a UML Healey Grant Award for Spatio-Temporal Knowledge Discovery in Nuclear Physics (2008). Her collaborative projects span transportation systems, nuclear physics applications, and autonomous vehicle infrastructure. She actively advises on database curriculum development and serves as department chair overseeing academic operations.

Rene Marcel Plonusمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Marine Biology
• Fisheries Management
• +۵ مورد دیگر

Dr. Rene Marcel Plonus serves as a Postdoctoral Researcher in Marine Ecosystem Dynamics and Management at the University of Hamburg's Faculty of Mathematics, Computer Science and Natural Sciences. He is affiliated with the Department of Biology and works within the Institute of Marine Ecosystem and Fisheries Sciences, focusing on computational approaches to marine conservation. His educational background includes a 2023 PhD in Fisheries Science with thesis 'Multi-dimensional characterization of pelagic habitats at different spatio-temporal scales', a 2018 Master's in Ecosystem and Fisheries Sciences, and a 2015 Biology Bachelor's degree. He holds European Scientific Diver certification from 2017. Plonus specializes in developing machine learning solutions for marine science applications, particularly automatic fish species classification using underwater video footage to support non-invasive fisheries management. His research integrates computer vision with ecological monitoring, targeting biodiversity assessment in the North Sea and Baltic regions. His publication record demonstrates consistent output in high-impact marine science journals since 2017, with recent 2024 publications showing continued focus on plankton habitat identification and microbial transport pathways. Current projects include the Nikofin initiative using BRUVs (Baited Remote Underwater Videos) and eDNA for biodiversity monitoring at the Sylt Outer Reef. As a researcher, he actively contributes to advancing computational methods in marine ecology through algorithm development for plankton image classification and pelagic habitat segregation, addressing critical challenges in ecosystem-based fisheries management.