جست‌وجوی استاد و پژوهشگر

Monica Wachowiczمشاهده پروفایل

استاد

• Data Science
• Geospatial Science
• Machine Learning
• +۵ مورد دیگر

Professor Monica Wachowicz holds the rank of Professor of Data Science at RMIT University, within the School of Science. Her academic roles include Associate Dean of Geospatial Science, Research Leader of Cyber and Data Analytics at the Space Industry Hub, and Deputy Director of the Sir Lawrence Wackett Defence & Aerospace Centre. She leads interdisciplinary research in streaming analytics, IoT integration, and smart city technologies. Wachowicz has pioneered innovations in edge computing and geospatial intelligence, collaborating with global firms like Cisco, Siemens, and IBM. She is a founding member of the IEEE Technical Sub-Committee on Big Data and serves on nine editorial boards. Her research focuses on leveraging machine learning for IoT data streams to address sustainability challenges in smart cities, including mobility analytics and energy systems. Over 52 students under her supervision have contributed to industries like tech, finance, and academia. Wachowicz is the first woman engineer in Geomatics awarded an NSERC Industrial Research Chair in Canada and holds an Honorary Visiting Scholar title at Flinders University. Her work emphasizes cross-sector collaboration, advancing green economies and data-driven decision-making. Awards include NSERC Industrial Research Chair (2010s) and Honorary Visiting Scholar (Flinders University, ongoing). Key projects include EVStationSIM (EV charging infrastructure analysis), Smart Campus Integration Lab, and IoT-GIS platforms for real-time analytics. She supervises projects on AI for urban sensing, smart mobility, and space situational awareness.

Murali Haranمشاهده پروفایل

استاد

• Statistical Computing
• Spatial Models
• Computer Experiments
• +۵ مورد دیگر

Murali Haran is a Professor of Statistics at Pennsylvania State University's Department of Statistics within the College of Science. He holds a B.S. in Computer Science from Carnegie Mellon University and advanced degrees (M.S. and Ph.D.) in Statistics from the University of Minnesota. His research focuses on computational statistics, spatial models, and applications in climate science and infectious diseases. He has held leadership roles including Chair of the Penn State Statistics Undergraduate Program (2012–2016) and co-editor of Bayesian Analysis (2016–2018). Research interests include statistical computing (MCMC algorithms), spatial/spatio-temporal models, complex computer model calibration, and interdisciplinary applications. He has contributed to climate risk management initiatives like NSF-sponsored SCRiM and directed Penn State's node in the STATMOS network. Awards include ASA Fellowship (2016), Abdel El-Shaarawi Young Researcher Award (2015), and ASA ENVR Young Investigator Award (2014). Key publications include advancements in ice sheet model calibration, Bayesian inference for intractable models, and spatiotemporal disease dynamics. His work bridges statistical theory with real-world challenges in climate and health. Teaching responsibilities span courses like Computational Statistics, Probability Theory, and Data Science. Professional service includes editorial roles for Technometrics, Biometrics , and others. He advises on climate policy through roles like ASA Climate Change Committee member (2009–2014) and ISBA Treasurer (2014–2016). His software contributions include the ngspatial R package and tools for MCMC error estimation.

Philip Dixonمشاهده پروفایل

استاد

• Ecological Statistics
• Environmental Statistics
• Biostatistics
• +۳ مورد دیگر

Philip Dixon is a University Professor of Statistics at Iowa State University's Department of Statistics. His research focuses on developing statistical methods for ecological and environmental challenges, including spatial data analysis, equivalence testing, and computer-intensive techniques like bootstrapping. Collaborative projects include tracking monarch butterflies and analyzing climate impacts on crop yields. He teaches advanced statistical courses and consults on experimental design, environmental data analysis, and statistical computing. Dixon is actively involved in software development for R and SAS, emphasizing reproducible research practices. His work spans ecological, agricultural, and public health applications, with a strong emphasis on methodological innovation and practical solutions.

Sankar Arumugamمشاهده پروفایل

استاد

• Hydroclimatology
• Water Management
• Climate Modeling
• +۷ مورد دیگر

Sankar Arumugam is a Professor and University Faculty Scholar in the Department of Civil, Construction and Environmental Engineering at North Carolina State University (NC State), where he leads the Climate, Hydrology and Water Resources Modeling and Synthesis Group. His research focuses on integrating geospatial data and probabilistic climate information to improve decision-making in water and energy sectors, with expertise in hydroclimatology, spatio-temporal modeling, and large-scale nexus issues like food-water-energy systems. Education: Ph.D. in Water Resources Engineering (Tufts University), M.Sc. in Civil Engineering (IIT Madras), B.E. in Agricultural Engineering (Tamil Nadu Agricultural University). Research interests include: Multi-timescale streamflow forecasting Climate elasticity and hydrological modeling Remote sensing applications for flood prediction Reservoir operations optimization Urban heat island effects Publications highlight advancements in probabilistic hydrological forecasting, climate-water-energy nexus analysis, and reservoir storage optimization. He has pioneered frameworks like GRAPS and COREGS for multi-reservoir systems and seasonal water-power system co-optimization. His work bridges academia and practice through collaborations with institutions like the International Research Institute for Climate and Society (Columbia University) and the World Bank.

Vaclav Petrasمشاهده پروفایل

پژوهشگر

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

Vaclav Petras is a Senior Geospatial Software Engineer and Researcher at North Carolina State University. He holds a Master's in Geoinformatics and a Ph.D. in Geospatial Analytics. His primary role involves advancing spatio-temporal models and tools for geospatial research through open-source development, particularly as a core member of the GRASS GIS project since 2012, where he also serves on the Project Steering Committee. His research focuses on improving geospatial software usability, open science practices, and participatory modeling. Key areas include integrating Jupyter Notebooks with GRASS GIS for education, redesigning user interfaces collaboratively, and developing cloud-based platforms for geospatial analysis. He actively contributes to projects like Tangible Landscape and has published widely on topics ranging from lidar data analysis to invasive species forecasting. Professional contributions span over 100 peer-reviewed articles, with recent work emphasizing cloud-based geospatial tools, risk-based inspection simulations, and ecological modeling. Petras is a strong advocate for open-source software, demonstrated through leadership roles in GRASS GIS and contributions to the Geoforall Lab at NCSU.

Peter Ojiamboمشاهده پروفایل

استاد

• Botanical Epidemiology
• Integrated Disease Management
• Plant Pathogen Ecology
• +۳ مورد دیگر

Peter Ojiambo is a Professor and Director of International Programs in the Department of Entomology and Plant Pathology at North Carolina State University. His research focuses on epidemiology and integrated management of plant diseases, with an emphasis on spatio-temporal modeling for disease prediction and risk assessment. He holds a B.S. and M.S. from the University of Nairobi and a Ph.D. in Plant Pathology from the University of Georgia (2004). Education: B.S. Agriculture, University of Nairobi (1994) M.S. Plant Pathology, University of Nairobi (1997) Ph.D. Plant Pathology, University of Georgia (2004) Research Interests: His work bridges botanical epidemiology and population genetics, with projects on: Spatio-temporal models for disease dynamics Decision support systems for disease management Pathogen dispersal mechanisms and landscape-level risk Integration of ecological and genetic data for improved disease control Key Publications (2024-2021): Focus on disinfestant efficacy meta-analyses, cucurbit downy mildew epidemiology, and R-based statistical tools for plant science research. Themes include fungal/non-fungal pathogen control, spatial risk modeling, and transcriptomic studies of pathogen development. Honors and Awards: American Phytopathological Society Hewitt Award (2012) Advising & Grants: Current advisees include Vinicius Garnica (PhD) and Aleksander Tako (PhD). Active grants total over $4M, addressing: Wheat/barley disease resistance phenotyping Decision support tools for fungicide optimization Genetic bases of quantitative disease resistance in maize Ecological spillovers in pathogen spread Solar cold storage solutions in Kenya Labs & Teams: Leads a research group working with postdoctoral researchers like Dr. Fangfang Guo. Teaches advanced courses in epidemiology and evolutionary ecology.

Krishna Pacificiمشاهده پروفایل

دانشیار

• Quantitative Ecology
• Wildlife Ecology
• Spatio-Temporal Modeling
• +۷ مورد دیگر

Krishna Pacifici is an Associate Professor in the Department of Forestry and Environmental Resources at North Carolina State University (NC State), affiliated with the College of Natural Resources and the Center for Geospatial Analytics. His work focuses on applying quantitative methods to understand ecological responses to environmental stressors and inform conservation decisions. He holds a Ph.D. in Forest Resources (2011) from the University of Georgia, M.S. in Statistics (2012) and Wildlife Ecology (2007), and a B.S. in Fisheries and Wildlife Sciences (2003), all from NC State. Research interests include spatiotemporal modeling, structured decision making, and adaptive management. He leads projects on North American mammal phylogeography, endangered species recovery (e.g., Neuse River Waterdog), and red snapper abundance estimation. His grants total over $6 million from NSF, USGS, NOAA, and state agencies, supporting collaborative efforts like the Southeast Climate Science Center and wild turkey ecology studies. Key grants: NSF-funded continental mammal SDMs ($374K), Neuse River Waterdog studies ($180K), and red snapper abundance modeling ($713K). Teaches FW 453/553: Principles of Wildlife Science. Publications emphasize ecological modeling, conservation applications, and methodological advancements in occupancy analysis and camera trap data.

Nikoleta Anicicمشاهده پروفایل

پژوهشگر

• Entomology
• Vector Ecology
• Invasive Species Monitoring
• +۴ مورد دیگر

Nikoleta Anicic is a Scientific Collaborator in Vector Ecology at the Department of Environment Constructions and Design (DACD) at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), where she focuses on monitoring and researching invasive mosquito species and their ecological impacts. Education: PhD in Evolutionary Ecology, University of Namur (Belgium), 2017 Master of Science in Biology with specialization in environmental microbiology and ecology, University of Zurich, 2017 Bachelor of Science in Biology, University of Neuchâtel, 2014 Anicic's research centers on vector ecology with particular expertise in mosquito monitoring and control. Her work combines molecular biology, entomology (specializing in mosquitoes), data management, ArcGIS, and programming in R to address public health challenges posed by invasive species. She has developed specialized skills in high-resolution optical identification of mosquito eggs and spatio-temporal modeling of invasive species dynamics. Her research has direct applications for arboviral disease surveillance and prevention, particularly for diseases like Dengue, Chikungunya, and Zika transmitted by invasive Aedes species. Analysis of her publication record reveals a strong interdisciplinary approach spanning entomology, environmental microbiology, and computational ecology. Her research trajectory shows progression from fundamental ecological studies on subterranean biodiversity and freshwater zooplankton to increasingly applied work on invasive mosquito surveillance. Recent publications demonstrate sophisticated integration of machine learning techniques with traditional ecological monitoring to predict seasonal mosquito population dynamics across multiple European countries. Nikoleta Anicic leads and participates in numerous research projects focused on invasive mosquito surveillance across Swiss cantons (including Zurich, Uri, Glarus, Schwyz, and the Romandy region) and Liechtenstein. These projects involve close collaboration with cantonal environmental offices and national authorities, with SUPSI serving as the national coordination center for invasive species monitoring designated by the Swiss Federal Office for the Environment. Her work directly informs Swiss public health strategies for controlling invasive mosquito species including Ae. albopictus, Ae. japonicus, and Ae. koreicus. At SUPSI, Anicic is a core member of the Vector Ecology Sector (SECOVETT) within the Institute of Microbiology. She contributes significantly to the Swiss mosquito network, providing scientific expertise for developing monitoring protocols, analyzing field samples, and interpreting spatial patterns of mosquito invasion. Her recent presentation at the 26th International Conference on Subterranean Biology demonstrates her continued engagement with broader ecological questions beyond her primary mosquito research focus.

Anne Perezمشاهده پروفایل

استادیار

• Forensic Entomology
• Insect Succession
• Time Since Death Estimation
• +۴ مورد دیگر

Anne Perez is an Assistant Professor of Instruction in the Department of Biological Sciences at Ohio University, part of the College of Arts and Sciences. She joined the institution in August 2017 and is based at Irvine 175 on the Athens Campus. Her role focuses on teaching and curriculum development in biological sciences. B.S. in Entomology, Ohio State University Ph.D. in Biology, West Virginia University Her research centers on forensic entomology, particularly insect succession on carrion, with an emphasis on developing statistically supported models for estimating time since death. She investigates spatio-temporal variation in insect colonization, the ecology and development of forensically important insects, and the validation of succession-based postmortem interval estimates. Her work bridges ecology, forensic science, and quantitative modeling. The recent publications reflect a consistent focus on forensic entomology and insect behavior, particularly in the context of death investigations and ecological monitoring. Her studies integrate field observation, experimental design, and statistical analysis to improve the reliability of entomological evidence in forensic contexts. Key themes include insect colonization independence, behavioral responses of stored product pests, and model validation for postmortem interval estimation. There are no listed scientific awards or honors in the provided text. Anne Perez is actively involved in teaching and mentoring, though no formal advisees are listed. She teaches core undergraduate courses including BIOS 1030 (Introductory Human Biology I), BIOS 1700 (Biological Sciences I: Molecules and Cells), and leads peer-led team learning for BIOS 1700. Her teaching philosophy emphasizes diverse instructional modalities, student engagement, and applied learning in biology education. There is no mention of a specific research lab, team, or ongoing grants in the provided content. However, her research publications suggest independent scholarly activity and collaboration with experts in forensic entomology.

Chao Huangمشاهده پروفایل

استادیار

• Large Language Models
• Graph Learning
• Recommender Systems
• +۳ مورد دیگر

Chao Huang is an Assistant Professor at the Department of Computer Science and Institute of Data Science at the University of Hong Kong (HKU). As the director of the Data Intelligence Lab@HKU, his research focuses on Large Language Models (LLMs), LLM Agents, Graph Learning, Recommender Systems, and AI for Smart Cities. He holds a PhD from the University of Notre Dame. Education: PhD in Computer Science from University of Notre Dame (USA). Research Interests: His work bridges machine learning with practical applications, including: Developing advanced LLM frameworks like GraphGPT and UrbanGPT Creating automated research tools (AutoAgent, AI-Researcher) Designing recommendation systems with LLM integration (RLMRec, LLMRec) Exploring spatio-temporal AI for urban challenges Notable Achievements: Over 11,000 Google Scholar citations (h-index 55), multiple top conference awards (WWW/SIGIR/KDD), and open-source projects with thousands of GitHub stars. His work has been recognized as most influential/pioneering in major AI conferences. Labs/Teams: Leads the Data Intelligence Lab, collaborating on projects like LightRAG, MiniRAG, and VideoRAG. The lab emphasizes open-source contributions with repositories on GitHub. Grants/Advising: Supervises PhD/MPhil students and offers research internships. Active in recruiting motivated researchers and students through HKU's programs.

• Renewable Energy
• Wind Power Modeling
• Meteorology
• +۶ مورد دیگر

Daniel Drew is a researcher affiliated with the University of Reading, where he completed his PhD in 2011. His work focuses on renewable energy systems, particularly wind power modeling, forecasting, and resource assessment, with applications in the UK, Mexico, and Europe. He has published extensively in high-impact journals related to renewable energy and meteorology. His research interests lie at the intersection of meteorology and energy systems, with a strong emphasis on understanding how atmospheric processes affect wind power generation. Key areas include wind power ramping, urban wind dynamics, offshore wind integration, and the use of reanalysis data for energy modeling. His work often involves collaboration with leading experts in atmospheric science and energy systems. The recent publications (2013–2022) demonstrate a consistent focus on wind energy forecasting, spatial modeling, and meteorological drivers of power system behavior. Trends show increasing attention to system-wide impacts, probabilistic forecasting, and the integration of high-resolution weather data into energy planning. His work bridges fundamental meteorology and practical energy applications. Meteorological drivers of European power system stress Interannual weather variability and electricity market design Urban wind profiling using Doppler lidar Forecasting regional wind power ramping Impact of offshore wind farms on national generation Daniel Drew has not been explicitly listed with any scientific awards or honors in the provided text. However, his extensive publication record in top journals indicates strong recognition in the field. He has collaborated with numerous researchers across institutions, suggesting active participation in research teams and projects. While no formal advising role is listed, his PhD and co-authorship with early-career researchers suggest involvement in mentoring. There is no mention of grants, but his research scope implies participation in funded projects related to renewable energy and meteorology. Daniel Drew’s work contributes to critical challenges in renewable energy integration, particularly in understanding and predicting wind power variability. His research supports the development of resilient and efficient power systems in the context of climate and weather variability.

Wenpin Houمشاهده پروفایل

استادیار

• Statistical Machine Learning
• Single-Cell Genomics
• Epigenomics
• +۳ مورد دیگر

Dr. Wenpin Hou serves as an Assistant Professor (tenure-track) in the Department of Biostatistics at Columbia University's Mailman School of Public Health. She holds affiliations with the Data Science Institute, Foundations of Data Science Center, and Health Analytics Center, driving interdisciplinary work at the statistics-data science interface. Her research pioneers statistical machine learning methods for single-cell genomics, epigenomics, and spatial transcriptomics, with core focus on gene regulatory network modeling and spatio-temporal pattern analysis. She actively develops generative AI applications (including Transformer models) for genomic data, enabling breakthroughs in understanding biological mechanisms across cancer, immunology, infectious diseases, and developmental processes through the ENCODE4 consortium. Dr. Hou's exceptional contributions are recognized by major awards: NIH Maximizing Investigators’ Research Award (MIRA R35) from NIGMS (2023) for gene regulatory network inference using single-cell multiomics NIH Pathway to Independence Award (K99/R00 1K99HG011468-01) from NHGRI (2021) for single-cell DNA methylation spatial landscape analysis Her NIH-funded programs advance computational genomics through collaborations spanning obesity, maternal/child health, and disease mechanisms. As an active Data Science Institute member, she shapes methodological frameworks for high-dimensional biological data while mentoring next-generation researchers in biostatistical innovation. Dr. Hou's work integrates with Columbia's Health Analytics initiatives to translate genomic insights into therapeutic pathways, emphasizing collaborative team science across biomedical domains.

Michael Epprechtمشاهده پروفایل

پژوهشگر

• Socio-economic development
• Poverty analysis
• Science-policy engagement
• +۳ مورد دیگر

Michael Epprecht is a Senior Research Scientist at the University of Bern and Head of the Country Office in the Lao PDR for the Centre for Development and Environment (CDE). His work focuses on socio-economic development, poverty analysis, science-policy engagement, and Information and Communication Technologies for Development (ICT4D). Based in Vientiane, he leads research initiatives on land acquisitions, agricultural commercialization, and their impacts on rural communities. Institution: University of Bern (CDE Lao Country Office) Research Focus: Land-based investments, human well-being, carbon emissions from land use Projects: Lao Knowledge for Development (K4D), Pha Khao Lao, Land for Life in Laos Field Experience: Vietnam, Laos, Cambodia, Tanzania, Kenya, Mauritania His recent publications analyze the spatio-temporal impacts of land deals, proletarianization in agrarian transitions, and carbon emissions from land acquisitions. A 2022 study in World Development examines precarization pathways in Laos, while 2021 work in Global Environmental Change evaluates human well-being trajectories linked to land investments.

• Marine Ecology
• Fisheries Science
• Offshore Wind Farm Impacts
• +۶ مورد دیگر

Dr. Jens Floeter is a Researcher at the University of Hamburg, Faculty of Mathematics, Computer Science and Natural Sciences, Department of Biology, Institute of Marine Ecosystem and Fisheries Sciences (IMF). He has been working as a Scientific Staff Member in the Fisheries Science research group under Prof. Dr. Christian Möllmann since November 2008. His research focuses on marine ecosystem dynamics, particularly examining the interactions between fisheries, offshore wind farms, and marine ecological processes in the North Sea and Baltic Sea regions. Dr. Floeter earned his doctorate (Promotion) in 2005 from the University of Hamburg with a dissertation titled "An investigation of key processes affecting trophic interactions in the North Sea fish assemblage and their significance for multi species fisheries assessment" at the Institut für Hydrobiologie & Fischereiwissenschaft. He completed his Diplom der Biologie in 1998 with a thesis on "Analyse und Modellierung der Nahrungsselektion von Nordseefischen" (Analysis and modeling of food selection by North Sea fish), and began his biology studies at the University of Hamburg in 1991. Dr. Floeter's research spans several interconnected areas within marine ecology and fisheries science. His primary focus is on understanding how offshore wind farms impact marine ecosystems, particularly examining pelagic effects, trophic interactions, and habitat changes. He investigates coupling of spatio-temporal scales in marine ecosystems, higher trophic level food web dynamics, and develops methods for multi-species fisheries assessment. His work often combines field observations with modeling approaches to address complex ecosystem questions. A significant portion of his research examines plankton habitats, predator-prey relationships, and the impacts of anthropogenic structures on marine communities. Analysis of Dr. Floeter's recent publications reveals a strong focus on the ecological impacts of offshore renewable energy infrastructure, particularly wind farms. His research spans physical oceanography (examining wind-wake effects and upwelling/downwelling), biological responses (plankton habitat identification, copepod behavior), and fisheries implications. He has increasingly incorporated advanced analytical methods including machine learning for plankton image classification and tensor decomposition for community ecology analysis. His work demonstrates a consistent trajectory of examining how human activities intersect with marine ecosystem dynamics, with growing emphasis on renewable energy infrastructure impacts. synKoFiMo (2015-2017): "Darstellung von Synergien im Kolk- und Fischmonitoring in Offshore-Windparks" (Demonstrating synergies in scour and fish monitoring in offshore wind farms) UNCOVER (2007): "UNderstanding the Mechanisms of Stock ReCOVERy" BECAUSE (2004-2007): "Critical Interactions BEtween Species and their Implications for a PreCAUtionary FiSheries Management in a variable Environment" LIFECO (2001-2004): "Linking hydrographic Frontal activity and ECOsystem dynamics in the North Sea & Skagerrak" SYCON (1999-2000): "Synthesis of North Sea research and development of a new research strategy" Dr. Floeter has extensive experience with field research methods, including work with high-speed towed multi-sensor vehicles (TRIAXUS, SCANFISH) for ecosystem monitoring. His research group collaborates with multiple institutions including the Helmholtz-Zentrum Geesthacht (HZG), Thünen Institute, and various international partners. His laboratory work focuses on analyzing marine ecosystem dynamics using a combination of field observations, remote sensing data, and computational modeling approaches to understand complex marine systems and inform sustainable management practices.

• Information Visualization
• Digital Humanities
• Urban Complexity
• +۵ مورد دیگر

Prof. Dr. Marian Dörk is a Research Professor for Information Visualization & Management at the University of Applied Sciences Potsdam , where he co-directs the Urban Complexity Lab (UCLAB) . His work bridges Digital Humanities , Human-Computer Interaction , and Critical Design , focusing on innovative visualization techniques for complex data. Education Ph.D. in Computer Science (2012), University of Calgary Dipl.-Ing. in Computational Visualistics (2008), Otto von Guericke University Magdeburg Dörk's research interests encompass: Critical Information Visualization for interrogating data politics Urban Complexity through spatio-temporal analysis Digital Cultural Heritage with situated visualizations Data-Driven Storytelling across journalism and education Exploratory Search with multivariate interfaces His recent publications demonstrate trends in foldable visualizations, edge contextualization in graphs, and ethical approaches to data storytelling. He leads the Urban Complexity Lab , exploring intersections of technology, urbanism, and critical theory. Scientific Awards include best paper and poster recognitions at CHI and VDA conferences. His work has been supported by grants from DFG and BMBF , including the GraDiM project (2023-2025) and SoNAR (IDH) (2019-2021). Dörk teaches in the MSc Urban Futures program and has supervised numerous student research projects like Reading Traces and Reverse Information Architecture . His design collaborations span institutions such as the Jewish Museum Berlin, Ethnologisches Museum Berlin, and Freie Universität Berlin.