جست‌وجوی استاد و پژوهشگر

Alven Yuمشاهده پروفایل

پژوهشگر ارشد

• Environmental Health
• Machine Learning
• Deep Learning
• +۱۲ مورد دیگر

Alven Yu is a Research Fellow in Planetary Health at the School of Public Health and Preventive Medicine, Monash University. He completed his PhD in Epidemiology at Monash University between January 2021 and August 2024 and continues to contribute significantly to environmental health research. He also held an external position as Data Scientist at the University of Melbourne from February to August 2024. Education: PhD in Epidemiology, Monash University (2021–2024) His research focuses on environmental exposure measurement, advanced machine learning applications, and novel methodologies for health risk assessment. He has pioneered a deep ensemble machine learning framework for high-resolution global PM2.5 estimation (2000–2019) and assessed associated short-term mortality burdens. His work spans exposure assessment, biostatistics, environmental health, and climate-related health impacts, with a strong emphasis on big data and predictive modeling. His recent publications (2023–2025) demonstrate a consistent focus on global environmental health challenges, particularly air pollution from wildfires, urban heat, flood impacts, and green space deprivation. These studies leverage machine learning and large-scale datasets to assess health risks across multiple countries, often published in high-impact journals such as Lancet Planetary Health , Nature Sustainability , and Environment International . Scientific Awards and Recognition: Award for Outstanding Self-financed Students Abroad (2024) PhD Top-up funding from NHMRC CRE Monash Postgraduate Publications Award ISEE 2023 Travel Award ICUC 11 Presentation Prize ISEE-AWPC Membership Award 2024 Vice-Chancellor’s Award for Research Team of the Year Dean's Award for Excellence in Research - Team Award (2024) Alven Yu has secured approximately $90,000 in competitive external seed funding as Chief Investigator from NHMRC CRE, Centre for Safe Air, CAR, and MMCR. He has published 39 peer-reviewed articles, with 23 during his PhD (all in JCR Q1 journals), and 14 as first or corresponding author. His research has been covered by over 150 news outlets and mentioned in more than 3,350 social media posts. He is actively involved in two major research projects: Developing a Global Air Quality Alert and Forecast System and Global mortality burden attributable to short-term exposures to wildfire-related PM2.5 .

Olaf Landsiedelمشاهده پروفایل

استاد

• Distributed Systems
• Wireless Sensor Networks
• Peer-to-Peer Systems
• +۴ مورد دیگر

Olaf Landsiedel is a Full Professor and head of the Distributed Systems Group at Kiel University, where he leads research in distributed computing, wireless sensor networks, and peer-to-peer systems. He has been a key contributor to the field since joining COMSYS in 2006 and earning his PhD in 2010. His research interests center on enabling accurate, scalable, and energy-efficient network simulations and protocols. He focuses on bridging the gap between simulation and real-world deployment, improving timing accuracy, modeling power consumption, and designing robust communication for unstable wireless environments. His work emphasizes modularity, flexibility, and reliability in distributed systems. The 15 most recent publications reflect a strong trend toward enhancing simulation fidelity, addressing bursty wireless links, enabling dynamic software updates in sensor networks, and developing tools for realistic experimentation. His work spans both theoretical models and practical implementations, often validated through simulation frameworks and testbeds. Olaf Landsiedel has advised numerous students on topics ranging from routing protocols to modular communication frameworks and ontology design, primarily during his time at RWTH Aachen University. While no specific grants are mentioned, his sustained publication record suggests consistent research funding and collaborative projects. He leads the Distributed Systems Group, which conducts cutting-edge research in network simulation, protocol development, and sensor network reliability. The group's work includes tools like KleeNet for bug detection and Horizon for parallel simulation, indicating a strong focus on practical system building and validation.

Damjan Pantićمشاهده پروفایل

استاد

• Dendrometry
• Forest Inventory
• Forestry Modeling
• +۳ مورد دیگر

Professor Damjan Pantić is a Full Professor at the Department of Forestry, Faculty of Forestry, University of Belgrade. He has held roles including Head of the Department of Forest Management Planning since 2014, Editor-in-Chief of the Bulletin of the Faculty of Forestry , and member of the editorial board of SEEFOR . His career spans over three decades, starting as an assistant in 1989 and advancing through academic ranks to full professor in 2015. He has been a visiting professor at the University of Banja Luka and a key figure in founding the Center for Inventory, Monitoring, and Forest Management Planning. His research focuses on Dendrometry, Forest Inventory, and modeling, with over 170 publications, including a foundational textbook on Dendrometry. Education: BSc (1987), MSc (1993), PhD (2003), all from the University of Belgrade’s Faculty of Forestry. His international roles include representing Serbia in ENFIN (European National Forest Inventory Network), FAO coordination, and H2020 LEAR (Legal Entity Appointed Representative) for the Faculty of Forestry. He is a corresponding member of the Academy of Engineering Sciences of Serbia (AINS) since 2022. Research interests emphasize integrating photogrammetry, remote sensing, and database creation into forestry. He has led or contributed to 21 research projects and over 30 professional studies. Notable achievements include co-authoring the National Forest Inventory of Serbia and contributing to European forest biomass mapping. His teaching spans all academic levels, introducing modern techniques in inventory and modeling. Scientific awards include his AINS membership. He has advised 15 doctoral dissertations and mentored numerous master’s and graduate students. His work in international cooperation includes IUFRO sections and harmonizing national forest inventories across Europe.

Zahra Dabiriمشاهده پروفایل

پژوهشگر

• Remote Sensing
• Digital Image Processing
• Spatio-temporal Data Analysis
• +۷ مورد دیگر

Zahra Dabiri is a Postdoctoral Researcher at the Department of Geoinformatics (Z_GIS), part of the Faculty of Digital and Analytical Sciences at the University of Salzburg , Austria. With a PhD in geospatial technologies, she specializes in Earth Observation (EO) applications for natural hazard analysis. Current research focuses on glacier retreat impacts on alpine infrastructure, rock glacier dynamics using InSAR, and landslide-dammed lake monitoring through satellite time series. Her expertise spans SAR polarimetry , hyperspectral imaging , and machine learning for geospatial analysis. Recent publications highlight her contributions to: Flood susceptibility mapping using AHP and EO data Automated rock glacier delineation with deep learning Landslide volume estimation via multi-temporal DEMs Heatwave impact analysis on water temperatures Scientific recognition includes the 1st runner-up of Copernicus Price Austria (2020) . She actively contributes to the Copernicus Master in Digital Earth program and collaborates with institutions across Europe, Iceland, and New Zealand.

• Urban Planning
• Environmental Health
• Transportation Engineering
• +۵ مورد دیگر

Dr. Esra Suel is an Associate Professor in City Modelling at the University College London's Centre for Advanced Spatial Analysis (CASA) since 2024. She holds a part-time role as Senior Scientist at ETH Zurich's Future Cities Lab. Her expertise spans urban systems, environmental health, and geomatic engineering. She earned a PhD in Urban Systems and Transport Planning from Imperial College London (2016), and holds degrees from Sabanci University and the University of Michigan. Research Interests: Dr. Suel focuses on the intersection of urban environments and health, leveraging street-view imagery and machine learning to assess greenspace impacts on cardiovascular health, mental well-being, and urban inequality. Her work also explores transport equity, air quality modeling, and sustainable city indicators. Recent studies include large-scale slum mapping in Africa and global urban health benchmarking tools. Key Contributions: Her 2025 studies link greenspace exposure to depression risk reduction in US women and reveal cardiovascular health disparities through street-level analysis. Earlier work pioneered air quality estimation via satellite imagery in data-scarce regions. She co-developed KidSat, a poverty-mapping dataset using satellite imagery. Awards: MRC Rutherford Fellowship (2018) Labs: CASA's Urban Analytics Lab, Future Cities Lab (ETH Zurich) Grants: Wellcome Trust-funded Healthy Cities project, MRC funding Her interdisciplinary approach bridges geospatial technologies with public health, contributing to UN Sustainable Development Goals 3 and 11.

Bronson P. Bullockمشاهده پروفایل

استاد

• Forest Management
• Biometrics
• Silviculture
• +۷ مورد دیگر

Dr. Bronson P. Bullock is a Professor at the University of Georgia's Warnell School of Forestry and Natural Resources and Director of the Plantation Management Research Cooperative (PMRC). He previously held positions at North Carolina State University. His research focuses on forest biometrics, silviculture, and quantitative timber management, with expertise in growth and yield modeling, spatial tree relationships, and Bayesian statistical methods. Education: PhD (2002), MS Biometrics (1998), MS Statistics (2000), and BS (1996) from Virginia Tech and Rutgers. His work bridges biometrics and practical forest management, emphasizing precision forestry and carbon dynamics. Research Interests: Volume equations, statistical sampling, silvicultural impacts, spatial matrix correlations, genetic homogeneity effects, diameter distributions, and carbon stock dynamics. He has published extensively in Forest Science , Forest Ecology and Management , and others. Labs/Centers: Harley Langdale Jr. Center for Forest Business and PMRC. Notable collaborations include studies on hurricane impacts and carbon sequestration in southern pine plantations.

Sangmi Lee Pallickaraمشاهده پروفایل

استاد

• Big Data
• Data Science
• Data Engineering
• +۱۳ مورد دیگر

Sangmi Lee Pallickara is a Professor of Computer Science and the Clare Booth Luce Professor at Colorado State University. She is affiliated with the Department of Computer Science in the College of Natural Sciences. Her research is supported by major agencies including the National Science Foundation, Department of Homeland Security, ARPA-E, and NIFA, with ongoing projects in AI Institutes, Cyberinfrastructure, and CyberPhysical Systems. Research Interests: Her work focuses on Big Data systems for scientific applications, including scalable storage, retrieval, metadata management, predictive analytics, and interactive visualization. She applies these to domains such as agriculture, atmospheric science, environmental monitoring, and epidemiology. Her research integrates data science, distributed systems, and deep learning to enable scalable knowledge extraction from high-velocity, voluminous datasets. Publication Trends: Her recent publications emphasize scalable solutions for geospatial and spatiotemporal data, including efficient storage (e.g., ATLAS), visualization (e.g., Glance, Iris), and deep learning (e.g., Argus, CloudNet). There is a strong focus on real-world applications such as wildfire prediction, satellite data imputation, and precision agriculture, leveraging generative models, embeddings, and ensemble methods. Scientific Awards: NSF CAREER Award IEEE TCSC Award for Excellence in Scalable Computing Best Paper Award at IEEE/ACM UCC 2019 Best Paper Award at IEEE CLUSTER 2019 Best Paper Award at IEEE/ACM UCC 2014 Finalist for Best Paper Award at IEEE BDCloud 2018 Advising and Grants: She advises numerous Ph.D. and Master’s students, many of whom have gone on to careers in industry and academia. Her research is funded by the NSF (AI Institutes, CPS), DHS, ARPA-E, NIFA, and the Environmental Defense Fund. She also leads the SWiFT outreach program for K–12 STEM education. Labs and Teams: She leads a vibrant research group focused on Big Data systems, with students working on distributed storage, deep learning for satellite imagery, spatiotemporal analytics, and interactive visualization. The team collaborates with domain scientists in agriculture, climate, and public health.

• Signal and Information Processing
• Energy Disaggregation
• Non-Intrusive Load Monitoring (NILM)
• +۱۵ مورد دیگر

Professor Vladimir Stankovic is a leading academic in the Department of Electronic and Electrical Engineering within the Faculty of Engineering at the University of Strathclyde, UK. His research focuses on signal and information processing to tackle global sustainability challenges such as energy efficiency, net-zero transition, electrification of transport, and landslide monitoring. He is actively involved in multiple high-impact research initiatives and holds leadership roles in major AI and engineering conferences. PhD (Dr-Ing), University of Leipzig, Germany, 2003 MEng (Dipl-Ing), Electrical Engineering, University of Belgrade, 2000 His research interests center on applying signal processing and AI to smart homes, micro-seismic analysis, health monitoring, remote sensing, and computer vision. He has developed innovative methods in energy disaggregation, graph signal processing, and interpretable machine learning. His work bridges engineering with sustainability, contributing to UN Sustainable Development Goals. He teaches core courses including image/video processing, communication systems, signals and systems, and information theory. The recent publications highlight a strong trend in applying AI and machine learning to energy analytics in buildings and agriculture (e.g., dairy farms), landslide monitoring using seismic data, and equitable EV charging planning. These works leverage advanced techniques such as graph neural networks, transformers, and human-in-the-loop deep learning, demonstrating a consistent focus on real-world impact, interpretability, and sustainability. He has received notable scientific awards: Finalist of the World first 10K Best paper Award (Top 3%) IEEE Multimedia Signal Processing Workshop (MMSP) top 10% best paper award 2010 Professor Stankovic has been a Principal or Co-Investigator on numerous research and knowledge exchange projects, including JED-AIs (Justice, Energy, Demand flexibility and AI for Sustainability), REMINDA, and multiple Digital Dairy Chain Innovation Vouchers. These projects are funded by EPSRC and other UKRI councils, focusing on AI-driven energy analytics, community energy sharing, and sustainable farming. He serves in key editorial roles, including Area Editor for IEEE Transactions on Communications and Associate Editor for Nature Scientific Data, and has organized major conferences such as IEEE MMSP 2017 and IJCNN 2025. He is actively involved in research teams and collaborative networks focused on AI for sustainability, energy systems, and environmental monitoring. His lab works on datasets such as the Plegma Dataset and REFIT, supporting open science in energy and seismic analytics.

• Ecosystem Science
• Boreal Forest Ecology
• Arctic Tundra
• +۷ مورد دیگر

Michelle Mack is a Regents' Professor in the Department of Biological Sciences and a key faculty member at the Center for Ecosystem Science and Society at Northern Arizona University. Her research focuses on Arctic and boreal ecosystems, with emphasis on wildfire, permafrost, carbon cycling, and vegetation dynamics under climate change. Department of Biological Sciences, Northern Arizona University Center for Ecosystem Science and Society, Northern Arizona University Her research interests span ecosystem science, boreal forest ecology, Arctic tundra dynamics, permafrost, wildfire ecology, and climate change impacts. She employs field surveys, remote sensing, and modeling to understand how northern ecosystems respond to environmental change, particularly fire and warming. Recent publications highlight her work on Arctic vegetation mapping, overwintering fires, tundra phenology, and post-fire carbon recovery. Her research often involves large interdisciplinary collaborations and contributes to major datasets used in global change science. Michelle Mack has no listed scientific awards in the provided text. She is actively involved in mentoring and collaborative research, as evidenced by her frequent co-authorship with early-career scientists and inclusion in multi-investigator datasets. While no formal students are listed, her role in large research teams suggests significant advisory and leadership responsibilities. She contributes to major environmental research initiatives such as the Arctic-Boreal Vulnerability Experiment (ABoVE). She leads and contributes to research teams generating critical ecological datasets on boreal and Arctic systems. Her work is central to understanding climate feedbacks in high-latitude ecosystems.

Gianluca Aloiمشاهده پروفایل

دانشیار

• Wireless Networks
• Cellular Networks
• Sensor Networks
• +۷ مورد دیگر

Gianluca Aloi serves as an Associate Professor in Telecommunications (IINF-03/A) at the Department of Computer Engineering, Modeling, Electronics and Systems (DIMES) of the University of Calabria, Italy. He holds the position of scientific director for the Telecommunications and Information Theory for Advanced Networking Laboratory (TITAN Lab.). Dr. Aloi earned his PhD in Systems Engineering and Computer Science from the University of Calabria in 2003 and became a University Researcher in Telecommunications (ING-INF/03) in 2004 before advancing to his current academic rank. His research expertise spans wireless networks, cellular networks, sensor networks, Internet of Things systems and their interoperability, management of resources and services in the Cloud/Edge and IoT (CEI) Continuum, and management and orchestration of network resources using Artificial Intelligence. His work particularly focuses on UAV-assisted IoT systems for industrial applications, geological hazard monitoring, and maritime environments. Analysis of Dr. Aloi's recent publications (2023-2025) reveals a strong emphasis on applying reinforcement learning and deep learning techniques to solve complex networking challenges. His research shows a clear trajectory toward developing intelligent network architectures that optimize data collection, improve system resilience, and enhance resource management across the edge-to-cloud continuum. Key application areas include smart factories, disaster monitoring, and urban vehicular systems. Dr. Aloi teaches courses including Fundamentals of Telecommunications Networks and Telecommunications Networks for both Electronic Engineering and Computer Engineering programs. He maintains regular reception hours every Tuesday from 9am to 11am or by appointment via email. As scientific director of TITAN Lab, Dr. Aloi leads research initiatives focused on advanced networking technologies, with particular emphasis on developing solutions for next-generation communication systems that integrate artificial intelligence with traditional networking paradigms to address real-world challenges in telecommunications and IoT applications.

Ihtiyor Bobojonovمشاهده پروفایل

پژوهشگر

• Agricultural Economics
• Climate Change
• Risk Management
• +۴ مورد دیگر

Dr. Ihtiyor Bobojonov is a Researcher at the Leibniz Institute of Agricultural Development in Transition and Emerging Economies (IAMO) , where he has worked since 2012. His research focuses on climate change adaptation , agricultural insurance , and supply chain dynamics in transition economies, particularly Central Asia. He completed his PhD at the University of Bonn , analyzing crop and water allocation under uncertainty in Uzbekistan, and received the ZEF Doctoral Thesis Prize (2007–2009). His work addresses agricultural market potentials in CIS countries, emphasizing supply chain transformation 's impact on producer welfare and insurance market development for climate resilience. He leads IAMO’s Central Asia International Research Group and coordinates the German-Uzbek Chair on Central Asian Agricultural Economics at Tashkent International Agricultural University. His methodological expertise spans bioeconomic modeling , machine learning , and experimental economics , with applications in weather index insurance and remote sensing for yield estimation. Key scientific awards include ZEF’s best thesis prize. His 15 most recent articles (2025–2020) explore topics across agricultural insurance markets , climate risk mitigation , wheat yield modeling , and peer influence on adaptation strategies , with empirical studies in Uzbekistan, Kyrgyzstan, and Mongolia. Research projects like KlimALEZ and DETECCT highlight his commitment to building climate-resilient agrifood systems through innovative financial instruments and digital technologies .

Neil Arnoldمشاهده پروفایل

استاد

• Glaciology
• Physical Geography
• Planetary Geomorphology
• +۷ مورد دیگر

Professor Neil Arnold is a faculty member at the University of Cambridge , affiliated with the Department of Geography and serving as Director of the Scott Polar Research Institute . He holds multiple college roles including Fellow, College Lecturer, and Director of Studies at St John's College. Research Focus: Arnold's work spans Earth and Mars, examining glacier melt dynamics, subglacial hydrology, and planetary geomorphology. He employs field studies, numerical modelling, and remote sensing across diverse locations including the Alps, Himalaya, Svalbard, Iceland, Greenland, Antarctica, and Mars. Key Article Trends: His publications focus on Martian glaciology (subglacial water, debris-covered glaciers), Antarctic ice shelf meltwater (slush contributions, surface hydrology), and Greenland Ice Sheet processes (lake drainage, meltwater impacts). Methodologies emphasize numerical modelling, remote sensing, and comparative planetary analysis. Scientific Awards: Fellow of the Royal Geographical Society (FRGS)

• Software Engineering
• Software Reliability and Quality Engineering
• Data Mining
• +۳ مورد دیگر

Kehan Gao serves as a Professor in the Department of Computer Science at Eastern Connecticut State University, teaching Software Engineering, Databases and Information Management, and Data Structures and Algorithms courses. Her research focuses on: Software Engineering and Reliability Software Quality Engineering Data Mining & Machine Learning Computational Intelligence Software Metrics With over 80 refereed publications, she specializes in software defect prediction using feature selection and data sampling techniques to address class imbalance. Recent work (2014-2025) extends these methodologies to Mars image classification and COVID-19 severity assessment, demonstrating cross-domain applicability of her ensemble learning approaches. No scientific awards were documented in available materials. Information regarding student advising, research grants, and laboratory affiliations was not provided in the source text.

• Metabolomics
• Computer Vision
• Amino Acids
• +۵ مورد دیگر

Ron Wehrens is a researcher at Biometris , part of Wageningen University & Research. His work spans interdisciplinary applications of machine learning, metabolomics, and computer vision in agricultural and biological sciences. Developing statistical tools in R (e.g., aaresponse , Kohonen packages) Applying AI/ML to problems in floriculture, animal behavior, and nutritional science Collaborating on projects involving remote sensing, plant phenotyping, and metabolic health studies Research Interests include metabolomics, deep learning for agricultural robotics, and behavioral analysis in livestock. His recent work focuses on self-organizing maps for data clustering and trait estimation in plants. Scientific Awards : No explicit awards mentioned in the text.

Salim Bawazirمشاهده پروفایل

دانشیار

• Hydrometeorology
• Water Resources Management
• Remote Sensing Applications in Agriculture
• +۳ مورد دیگر

Dr. Salim Bawazir is an Associate Professor in the Department of Civil Engineering at New Mexico State University (NMSU). Currently serving as Associate Department Head since 2023, he has maintained continuous academic roles at NMSU since 1998, including Associate Professor (2008-present), Assistant Professor (2003-2008), and College Assistant Professor (2001-2002). Education Ph.D. in Civil Engineering (Water Resources), NMSU (2000) M.S. in Civil Engineering, NMSU (1993) B.S. in Civil Engineering, NMSU (1988) His research focuses on hydrometeorology and water management in arid environments, particularly: Evaporation/reservoir water loss quantification Crop evapotranspiration modeling Remote sensing applications in water allocation Clinoptilolite zeolite soil engineering Riparian ecosystem rehabilitation Capillary rise dynamics in water-scarce soils Publications demonstrate expertise in: Groundwater recharge optimization through field measurements Phenology-based irrigation planning using Landsat-8 Saltgrass crop coefficient determination for desert riparian zones ASTER satellite-based evapotranspiration mapping Capillary rise modeling in engineered soil systems