جستجوی استاد راهنمای دانشگاهی

Mustafa Aliمشاهده پروفایل

پژوهشگر

• Environmental economics
• Sustainable agriculture
• Sustainable water management
• +۶ مورد دیگر

Dr. Mustafa Ali is a Research Associate at Lancaster University's Management School, affiliated with the Pentland Centre for Sustainability in Business. He has held academic positions at Shanghai Jiao Tong University, University of Chichester Business School, and currently Lancaster University since 2019. His interdisciplinary research spans environmental economics, sustainable agriculture, water management, public health, and societal-environmental interactions. His research interests focus on sustainability assessment, waste management systems, carbon footprint analysis, and circular economy approaches. He applies life cycle assessment, emergy analysis, and spatio-temporal modeling to examine environmental impacts across food systems, healthcare waste, urban infrastructure, and agricultural practices. His work often compares developed and developing country contexts, particularly focusing on Pakistan, China, India, and the UK. His publication record shows consistent output in high-impact environmental journals, with research evolving from healthcare waste management in Pakistan toward broader sustainability challenges including carbon footprinting of food systems and circular economy applications. His work demonstrates methodological diversity, employing GIS analysis, life cycle assessment, emergy accounting, and stakeholder coordination frameworks. Dr. Ali has received research funding from prestigious sources including the NSFC (China) and has worked on international projects across Europe and Asia. His research contributes to understanding sustainability challenges in both developing and developed contexts, with particular attention to policy-relevant findings for urban planning, waste management, and sustainable consumption. His academic journey began with an engineering background and MBA, followed by PhD studies at Southeast University in China. This multidisciplinary foundation supports his current work bridging technical, economic, and social dimensions of sustainability challenges.

Nicole Luisiمشاهده پروفایل

مدرس

• HIV/AIDS Prevention
• Infectious Disease Epidemiology
• Health Disparities
• +۴ مورد دیگر

Nicole Luisi is an Instructor in the Rollins School of Public Health, Department of Epidemiology at Emory University, and an Adjunct Instructor in the Executive MPH (EMPH) Program. She also serves as Director of Data Analytics and Informatics Projects, supporting research grants, developing web-based tools, and consulting for the CfAR Prevention Science Core. Her work focuses on epidemiologic methods, web-based surveys, and health data technologies. Education includes a BA from Moravian College, MPH and MS degrees from East Stroudsburg University of Pennsylvania and the University of Massachusetts Amherst, and a PhD from the University of Georgia. Research interests span HIV/AIDS prevention, infectious disease epidemiology, health disparities, and data science. Recent work examines crisis pregnancy centers, opioid misuse, and disparities in HIV outcomes among marginalized populations. She has taught courses such as SAS programming and statistical analysis. Key contributions include analyzing spatial distribution of healthcare facilities, evaluating pandemic mitigation behaviors, and advancing PrEP awareness among vulnerable groups. Her work emphasizes health equity and innovative data solutions for public health challenges.

Menno-Jan Kraakمشاهده پروفایل

استاد

• Cartography
• GIS
• Geovisualization
• +۴ مورد دیگر

Menno-Jan Kraak is a Full Professor at the University of Twente's Faculty of Geo-Information Science and Earth Observation, leading the Department of Geo-information Processing. He holds a PhD in Cartography from Delft University of Technology and a cum laude Master's from Utrecht University's Faculty of Geographical Sciences. His expertise spans cartography, GIS, geovisualization, and spatio-temporal analytics. Kraak served as President of the International Cartographic Association (2015-2019) and chairs the GIMA program, a collaborative MSc initiative across Dutch universities. His research focuses on innovative cartographic techniques, SDG-related geovisualization, and urban morphology analysis. Notable works include books such as Cartography, Visualization of Geospatial Data and Mapping Time , and editorial roles in journals like International Journal of Cartography . He leads the Spatio-Temporal Analytics, Maps, and Processing (STAMP) research program and contributes to global geospatial initiatives like UN-GGIM. Kraak has been awarded the Kartografieprijs 2020 and is actively involved in advancing cartographic education and ethics through international collaborations. Awards: Kartografieprijs 2020 Leadership Roles: ICA President (2015-2019), Chair of GIMA, Board Member of Netherlands Cartographic Society Key Projects: STAMP Program, Scientific Atlas of the Netherlands, ITC Atlas Development SDG Contributions: Mapping for Sustainable Development Goals, Urban Resilience Visualization

Alex Gregoryمشاهده پروفایل

استاد مدعو

• Spatio-temporal Modeling
• Quantitative Analysis
• Data Science

Alex Gregory is an Adjunct Professor in the Department of Anthropology at New York University (NYU), affiliated with the College of Arts and Science. His research focuses on spatio-temporal modeling, quantitative analysis, and data science applied to archaeological contexts. Education: BA in Archaeological Studies from SUNY Potsdam BA in Mathematics from SUNY Potsdam MS in Applied Anthropology from Oregon State University Current Research: Investigating the spatial distribution of artifacts at the Cooper's Ferry site in Idaho (16-13,000 years ago). This project employs advanced quantitative methods to understand human settlement patterns and material culture dynamics. No scientific awards or publications are listed in the provided materials. His CV is available for further details.

Colin Robertsonمشاهده پروفایل

دانشیار

• Spatial-Temporal Analysis
• Environmental Health
• Geographic Information Systems (GIS)
• +۳ مورد دیگر

Colin Robertson is an Associate Professor in the Department of Geography and Environmental Studies at Wilfrid Laurier University, where he previously served as Director of the Cold Regions Research Centre. He also leads data science initiatives at Boeing Vancouver, focusing on aerospace analytics. His research spans spatial-temporal analysis for ecosystem and human health, citizen science, and environmental monitoring. Robertson holds a Ph.D. in Geography from the University of Victoria, with earlier degrees from Simon Fraser University and BCIT. Education: B.A. (Honours) in Geography, Simon Fraser University, 2002 Advanced Diploma (Honours) in GIS, BC Institute of Technology, 2004 M.Sc. in Geography, University of Victoria, 2007 Ph.D. in Geography, University of Victoria, 2011 Research Interests: Spatial analysis of environmental and health systems, disease surveillance, climate change impacts, and geospatial data science. His work bridges traditional spatial analysis with machine learning, emphasizing practical applications in conservation, public health, and aerospace. Key Contributions: Founded the Spatial Lab to advance geospatial research, developed frameworks for emerging disease surveillance (e.g., RinkWatch citizen science project), and contributed to maritime intelligence systems at GSTS. His research integrates interdisciplinary methods to address real-world challenges in environmental systems. Labs & Collaborations: Director of Cold Regions Research Centre, leading projects on Arctic ecosystems and climate change. Collaborates with governments, NGOs, and industry on spatial data solutions, including Boeing’s aerospace maintenance analytics. Awards & Recognition: No specific awards listed, but recognized for innovative applications in spatial data science across academia and industry.

• Human-Robot Interaction
• Cognitive Robotics
• Machine Learning
• +۲ مورد دیگر

Professor Yiannis Demiris holds the position of Professor of Human-Centered Robotics and Head of ISN at Imperial College London's Department of Electrical and Electronic Engineering. He is a Royal Academy of Engineering Chair in Emerging Technologies (Personal Assistive Robotics) and Fellow of both IET and BCS. His research integrates artificial intelligence, machine learning, and robotics to develop interactive systems that improve human physical, cognitive and social well-being. Major projects include multimodal modeling of human states for robotic assistance, trustworthy autonomous systems, edge-case handling in autonomous driving, and augmented reality interfaces for robot control. Professor Demiris has received the President's Award for Excellence in Research Supervision and teaches courses on human-centered robotics and mobile healthcare. His publications demonstrate increasing focus on trust modeling in human-robot systems, socially aware navigation, and assistive robotic applications.

• Remote Sensing
• Geographic Information Systems (GIS)
• Environmental Data Science
• +۳ مورد دیگر

Bo Zhou is a Lecturer in the Department of Geography at the University of California, Los Angeles (UCLA). His current work involves a NASA-funded project collaborating with the U.S. Geological Survey (USGS) and the Bureau of Land Management (BLM) to develop a web-based tool that integrates NASA satellite data with field assessments for spatial and temporal analysis of environmental variables. This research focuses on enhancing ecological monitoring and resource management through advanced data fusion techniques. Dr. Zhou holds a Ph.D. in Forestry (2013) and an M.A. in Geography (2007), both from the University of Missouri Columbia. His research interests span remote sensing, geographic information systems (GIS), environmental data science, and the application of AI-driven methods to environmental challenges. His recent projects emphasize the development of generative models, autonomous systems, and urban simulation tools to address complex spatial and temporal data problems. His publications from 2022–2025 highlight contributions in AI, robotics, and computer vision, particularly in generative models, autonomous navigation, and data-efficient learning. These works reflect a focus on advancing technologies for urban mobility, environmental monitoring, and simulator-based research. Despite prolific output, no scientific awards are explicitly mentioned in the provided texts. Zhou’s advising and grant activities are not detailed here, but his collaborations with NASA and federal agencies underscore a strong commitment to applied environmental research. His work bridges academic and practical domains, aiming to improve decision-making in ecological and urban systems through innovative data integration and simulation.

Alex Reinhartمشاهده پروفایل

دانشگاهی

• Statistical Pedagogy
• Educational Research
• Natural Language Processing
• +۵ مورد دیگر

Alex Reinhart is an Associate Teaching Professor in the Department of Statistics & Data Science at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. He holds a Ph.D. from CMU and a BS in Physics from the University of Texas at Austin. His research focuses on statistical pedagogy, natural language processing, and applications of spatio-temporal data analysis to crime prediction and radiation detection. Reinhart’s work emphasizes improving statistical education through innovative methods like writing in the age of AI and psychology case studies. He also explores the intersection of large language models and human-like text generation. His contributions include the book Statistics Done Wrong , which critiques common statistical errors in scientific research. His recent publications address pandemic-related challenges, such as vaccine hesitancy and real-time data analysis through surveys like the US COVID-19 Trends and Impact Survey. His research spans statistical software development (e.g., the pseudobibeR package) and interdisciplinary applications in public health, biomechanics, and education.

Matthias Katzfussمشاهده پروفایل

استاد

• Computational Spatial Statistics
• Spatio-Temporal Statistics
• Gaussian Processes
• +۵ مورد دیگر

Matthias Katzfuss is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications in environmental science and satellite remote sensing. He has received funding from NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. His scientific achievements include awards such as the NSF Career Award, a Fulbright Scholarship, and the Early Investigator Award from the American Statistical Association’s Section on Statistics and the Environment. His work bridges theoretical advancements and real-world applications, particularly in environmental monitoring and large-scale data fusion. Recent research trends include scalable methods for high-dimensional spatial data, nonstationary covariance modeling, and Bayesian transport maps. His publications address challenges in remote sensing, climate science, and machine learning, emphasizing computational efficiency and methodological innovation. Matthias collaborates extensively with institutions like JPL and NASA, contributing to global-scale environmental studies. His lab focuses on developing open-source software tools, such as the GPgp package, to enable reproducible research in spatial statistics.

Ronald Gangnonمشاهده پروفایل

استاد

• Spatial and Spatio-Temporal Modeling
• Age-Period-Cohort Models
• Small Area Estimation
• +۳ مورد دیگر

Ronald Gangnon is a Professor in the Department of Biostatistics and Medical Informatics and the Department of Population Health Sciences at the University of Wisconsin-Madison, within the School of Medicine and Public Health. He also holds an affiliate appointment in the Department of Statistics. His academic career includes a PhD in Statistics (1998) and MS in Statistics (1994) from the University of Wisconsin-Madison, and a BA in Mathematics and Economics from the University of Minnesota-Duluth (1992). Before joining the faculty in 2005, he worked as a research scientist at the Statistical Data Analysis Center (SDAC). His research focuses on applied biostatistics in clinical and epidemiologic studies, particularly multi-state models for disease progression, spatial/spatio-temporal modeling, small area estimation, and age-period-cohort analysis. He has contributed to studies on health disparities, birth outcomes, colorectal cancer screening, and transportation patterns among older adults. Gangnon collaborates widely, appearing as an author in over 100 peer-reviewed articles. His work bridges statistical methodology with public health challenges, emphasizing spatial analysis and health equity. Outside academia, he is an avid cyclist and film enthusiast, frequently attending the Wisconsin Film Festival.

Ron Gangnonمشاهده پروفایل

استاد

• Spatial Epidemiology
• Spatio-Temporal Analysis
• Biostatistics
• +۸ مورد دیگر

Ronald Gangnon is a Professor in the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison, affiliated with the School of Medicine and Public Health. He holds additional affiliations with the Department of Statistics, Computer, Data & Information Sciences, and the College of Letters and Science. His research focuses on spatial and spatio-temporal epidemiology, high-dimensional variable selection, small area estimation, and age-period-cohort modeling, with applications to cancer, infectious diseases, environmental health, asthma, and geriatrics. Dr. Gangnon collaborates extensively with interdisciplinary teams on projects involving maternal health, pediatric outcomes, cancer survivorship, and healthcare disparities. His work integrates advanced statistical methods with public health challenges, emphasizing geospatial analysis, environmental exposures, and translational research. Recent studies address transportation accessibility for aging populations, socioeconomic determinants of healthcare utilization, and the impact of prenatal nutrition on neurodevelopmental disorders. His articles explore topics like motor milestone delays in autistic children, disparities in colorectal cancer screening, and the relationship between neighborhood socioeconomic status and asthma exacerbations. These studies highlight his commitment to addressing health inequities through rigorous biostatistical analysis. Dr. Gangnon’s expertise includes collaborative modeling for breast cancer screening strategies and simulation studies on mammography protocols. He has contributed to national initiatives like the US Preventive Services Task Force and the Childhood Allergy and NeOnatal Environment (CANOE) study. His work often emphasizes practical interventions to improve patient outcomes, such as telehealth-delivered exercise programs for cancer survivors and community-based asthma management strategies.

• Statistical Genetics
• Biomedical Statistics
• Bayesian Inference
• +۴ مورد دیگر

Dr. George Nicholson is a Postdoctoral Researcher in the Department of Statistics at the University of Oxford , specializing in statistical genetics , biomedical data analysis , and methodological innovation . His work bridges probabilistic modeling with real-world health applications. Research Focus: Development of advanced statistical methods for analyzing complex biomedical datasets, including longitudinal clinical trials, UK Biobank, and SARS-CoV-2 surveillance systems. Key Contributions: Creation of composable statistical inference frameworks, generalized Bayesian approaches for model misspecification, and hierarchical models for population differentiation studies. Methodological Expertise includes Bayesian networks , Markov chain Monte Carlo techniques , spatio-temporal modeling , and multivariate factor analysis . His recent publications address critical challenges in: Public Health: Designing robust methodologies for SARS-CoV-2 prevalence estimation through wastewater surveillance and clinical data integration Genomics: Characterizing genetic determinants of adiposity progression and metabolic disease trajectories Clinical Trials: Developing novel latent factor models for treatment response evaluation in autoimmune disorders

Kulsawasd Jitkajornwanichمشاهده پروفایل

استادیار

• Computational Communication
• Social Media Analytics
• Natural Language Processing
• +۴ مورد دیگر

Kulsawasd Jitkajornwanich is an Assistant Professor in the Department of Professional Communication at Texas Tech University's College of Media and Communication. He holds a Ph.D. in Computer Science from the University of Texas at Arlington, with research focused on computational and data science applied to social media, environmental monitoring, and disaster management. His work integrates GIS, NOSQL databases, and AI techniques to address challenges in crisis communication, precision agriculture, and health misinformation detection. Education: Ph.D. in Computer Science, University of Texas at Arlington (2014) M.S. in Computer Science, University of Texas at Arlington (2009) B.S. in Computer Science, Chulalongkorn University (2004) Research Interests: Computational communication, social media analytics, NLP, big data visualization, and AI-driven solutions for environmental and health crises. His interdisciplinary approach bridges computer science with communication studies to foster impactful research in crisis management and science communication. Awards: 2021 Community Outreach Award for 'Tawai For Health' 2019 National Dissertation Award (Thailand) 2018 Faculty Research Visibility Award (KMITL) Teaching & Grants: Teaches courses like Big Data Analytics and Media Insights. Co-PI on a €93K EU grant (Erasmus+) for educational automation in Thailand. Advises graduate and undergraduate students across 2 PhD, 14 master’s, and 15 undergraduate projects, including a 'Best Paper Award' winner. Labs/Teams: Founded Texas Tech's Media Insights Lab (2023), focusing on computational communication and data-driven media strategies. Collaborates with Thai government agencies on disaster management and health initiatives.

• Spatial Databases
• Geographic Information Systems (GIS)
• Big Data Analytics
• +۶ مورد دیگر

Alberto Belussi is an Associate Professor at the Department of Computer Science within the School of Science and Engineering at the University of Verona. He serves as a member of the PhD Councils, Teaching Boards, and various committees related to academic governance, including roles as Presidente of the University Language Centre's programming and administrative boards. Research Interests: Spatial query processing and optimization in geographical applications Conceptual modeling of spatial databases with emphasis on integrity constraints Machine learning techniques for spatial data Big data analytics with spatio-temporal datasets Development of tools for GeoUML and INSPIRE data specification interoperability Projects: He has led or participated in projects such as INDICE (Digital Innovations for Cultural Industries), Advanced digitization techniques and blockchain for cultural heritage , and Semantic mapping of Italian National Core to EU INSPIRE , among others spanning 2001–2025. Teaching: Belussi teaches courses like Advanced Database Systems, Databases, and Data Management and Machine Intelligence across multiple academic years, including 2025/2026.

• Spatial data management
• Spatio-temporal statistics
• Machine learning in GIS
• +۳ مورد دیگر

Felix Kunde is a Researcher at the Beuth University of Applied Sciences Berlin, contributing to the ExCELL and MAGDa projects. He holds a Bachelor's degree in Geography and a Master's degree in Geoinformation Science. His professional experience includes roles as a database developer for 3D city models and teaching geodatabase systems at universities. He actively participates in conferences such as FOSSGIS, FOSS4G, and WhereCamp, presenting work on topics like traffic forecasting, geospatial data management, and PostGIS extensions. Research interests focus on spatial data management (RDBMS/NoSQL/HDFS), spatio-temporal statistics, linear referencing, machine learning in GIS, and 3D city modeling. Current projects address short-term traffic forecasting with STARIMA/SVM/ANN methods, handling missing data in time series, and scalable geostatistical algorithms on Hadoop. He also works on open data models for traffic in OpenStreetMap and mobile databases using GeoPackage. Education: Bachelor of Geography Master of Geoinformation Science Key Contributions: Co-developer of pgMemento for spatial database auditing Contributor to the 3DCityDB project Lead researcher on ExCELL mobility platform Teaching: Lectures on geodatabases at multiple universities PostGIS workshops at conferences His publications span topics from geodatabase systems to traffic prediction algorithms, with notable work in spatial-temporal data analysis and geoinformatics applications. He advises multiple students on projects related to mobility analytics, data mining, and geospatial technology integration.