جست‌وجوی استاد و پژوهشگر

Cindy L Yuمشاهده پروفایل

استاد

• Financial Statistics
• Missing Data Analysis
• Survey Statistics
• +۳ مورد دیگر

Cindy L. Yu is a Professor in the Department of Statistics at Iowa State University (ISU), affiliated with the Center for Survey Statistics and Methodology (CSSM). She holds a Ph.D. in Statistics from Cornell University (2005). Her research focuses on financial statistics, missing data analysis, survey statistics, and causal inference. Dr. Yu collaborates on national survey projects related to natural resources, developing statistical methodologies for environmental and agricultural applications. Her work integrates advanced statistical techniques with real-world challenges in finance, agriculture, and public policy. Notable contributions include applications of machine learning in financial modeling, quantile regression for crop insurance, and survey design optimization. She has advised numerous projects in statistical methodology and policy analysis, though no formal advisee names are listed. Publications reflect interdisciplinary expertise, spanning econometrics, environmental statistics, and survey methodology. Her research emphasizes practical solutions for data-driven decision-making in complex systems.

Andres Algabaمشاهده پروفایل

استاد

• Fairness in Machine Learning
• Large Language Models
• Science of Science
• +۷ مورد دیگر

Andres Algaba is a Part-time Professor in econometrics and mathematics for business and economics at Vrije Universiteit Brussel (VUB), where he also serves as an FWO Postdoctoral Researcher in Artificial Intelligence and Coordinator in Generative AI. He holds a joint PhD in Business Economics from VUB and Ghent University (2020), along with MSc (2015) and BSc (2014) degrees in Business Sciences from KU Leuven. His research focuses on: Fairness in machine learning and algorithmic decision-making Large language models and their scientific applications Generative AI and interpretability methods Science of science and bibliometric analysis Econometric modeling and sentiment analysis His publications demonstrate a strong evolution from econometric modeling (2020) toward cutting-edge AI fairness research and LLM analysis (2023-2025), with increasing focus on generative models and scientific disruption patterns. Awards: FWO Postdoctoral Fellowship Election to Young Academy Belgium (2024) Research Leadership: Principal investigator for Francqui Collen Start-Up Grants (2025-2028) focusing on generative AI and interpretability methods. Supervises multiple master's students in AI fairness and algorithmic bias. Affiliations: Member of Data Analytics Lab and Integrated Intelligence Lab at VUB, with frequent collaborations in national and international research networks focused on algorithmic transparency and AI ethics.

• Econometrics
• High Dimensional Econometrics
• Bayesian Inference
• +۳ مورد دیگر

Anna Simoni is a Senior Researcher at CNRS/CREST and Professor of Econometrics and Statistics at ENSAE and École Polytechnique. She holds a PhD (2009) and Habilitation (2017) from Toulouse School of Economics. Her research focuses on econometric methods for big data analysis, Bayesian inference, and policy evaluation. Key roles include Associate Editor of the Journal of Econometrics and Fellowships at Hi! Paris and Institut Louis Bachelier. In 2019, she received the CNRS Bronze Medal for her contributions. Education: PhD in Economics, Toulouse School of Economics (2009) Habilitation à Diriger de Recherche (HDR), Toulouse School of Economics (2017) Research Interests: Anna specializes in advanced econometric techniques such as Bayesian methods, high-dimensional modeling, and nowcasting using digital data. Her work integrates machine learning and statistical theory to address economic questions like GDP prediction and causal inference. Awards & Grants: CNRS Bronze Medal (2019) Fellow, Hi! Paris (2021) Fellow, Institut Louis Bachelier (2021) ANR Grant for 'Moment Conditions Models and Bayesian Inference for Policy Evaluation' (2021–2025) Labs & Affiliations: Active at CREST (Center for Research in Economics and Statistics) and affiliated with the Institut Polytechnique de Paris. Her work intersects economic theory, statistical methodology, and applied policy analysis.

Smriti Nandan Paulمشاهده پروفایل

استادیار

• Space Domain Awareness
• Space Traffic Management
• Space Weather
• +۶ مورد دیگر

Dr. Smriti Nandan Paul is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the Missouri University of Science and Technology. She directs the Dynamics, Control, and Computer Vision for Space Sustainability and Missions (DCV-Space) group, focusing on astrodynamics, guidance, navigation, and control (GNC) for space autonomy, space weather, and machine learning applications. Her work combines computational and experimental approaches for near-Earth and cislunar space environments. Education : Ph.D. in Aeronautics and Astronautics, Purdue University (2020) Dual Degree (B.S./M.S.) in Aerospace Engineering, IIT Bombay (2015) Research Focus : Space domain awareness (SDA), space traffic management (STM), stochastic machine learning, computer vision for space missions, and GNC systems for sustainable space operations. Her group emphasizes applications in orbital mechanics, sensor tasking, and autonomous satellite systems. Awards : Purdue AAE Teaching Scholarship (2019) AAE Teaching Fellowship (2020) AAE Teaching Assistant Award (2019) Advising & Labs : Leads the DCV-Space group, which develops methodologies for space object tracking, drag coefficient modeling, and machine learning-driven solutions for space sustainability. Past roles include Visiting Assistant Professor at Purdue University and postdoctoral research at West Virginia University.

Viviana Maggioniمشاهده پروفایل

دانشیار

• Hydrology
• Remote Sensing
• Water Resources Engineering
• +۵ مورد دیگر

Viviana Maggioni is an Associate Professor and Director of Undergraduate Affairs in the Sid and Reva Dewberry Department of Civil, Environmental, and Infrastructure Engineering at George Mason University's College of Engineering and Computing. She leads an active interdisciplinary research group focusing on the intersection of hydrology and remote sensing, with applications in water resources management, weather and climate prediction, and agricultural practices. Her research interests include water resources engineering, hydrology, remote sensing, land surface modeling, uncertainty analysis, and land data assimilation systems. Dr. Maggioni applies satellite remote sensing and modeling techniques to estimate and monitor hydrological variables at local to global scales, with particular emphasis on flash flood forecasting, precipitation estimation, and water cycle monitoring in data-scarce regions. Her recent publications demonstrate a strong focus on West Africa flash flood forecasting, High Mountain Asia hydroclimatic risk assessment, coastal storm surge modeling, and satellite precipitation product evaluation. She has developed expertise in integrating multiple satellite observations into hydrological models and advancing methods for uncertainty quantification in remote sensing products. Editor in Chief of the Journal of Hydrometeorology (American Meteorological Society Publications) Associate Editor of Frontiers in Climate - Climate Services Chair of the American Geophysical Union (AGU) Fall Meeting Program Committee for the Hydrology Section Mason Representative for CUAHSI 2018-2022 Co-Chair of the International Precipitation Working Group (IPWG) Dr. Maggioni currently mentors four PhD students and has successfully guided numerous doctoral candidates to completion. Her research group conducts field work from the local scale (monitoring stormwater at Mason's campus) to the global scale (using satellite data to evaluate remote regions). She leads multiple NASA-funded projects including Machine Learning-based Flash Flood Forecasting in West Africa and Mapping Changes in Hydroclimatic Risk in High Mountain Asia.

Pedro Afonso Fernandesمشاهده پروفایل

پژوهشگر ارشد

• Economics
• Data Analysis
• Computational Methods
• +۹ مورد دیگر

Pedro Afonso Fernandes is a Research Fellow at the CUBE – Católica Lisbon Research Unit in Business & Economics and a Senior Economist at the NECEP – Católica Lisbon Forecasting Lab since 2013. His interdisciplinary work bridges Economics , Data Analysis , and Urban Planning , with expertise in computational methods, econometrics, and forecasting. PhD in Urban Planning (Lusófona University) MSc in Economics (NOVA School of Business and Economics) MSc in Urban and Regional Planning (Technical University of Lisbon) BSc in Economics (NOVA University Lisbon) His research focuses on Business Cycles , Forecasting (including GDP nowcasting), Logic Programming (Prolog), and Computable General Equilibrium (CGE) Models , with applications to Water Sustainability , Regional Development , and Urban Economics . Recent work integrates Space Syntax with Machine Learning for predictive urban analysis. Key trends in his publications include water resource management (e.g., São Francisco River Basin), economic forecasting (GDP, business cycles), and computational urbanism (Space Syntax + Prolog/ML). He also explores lifelong skill certification and its labor market impacts. Best GDP Forecaster - Portugal (2018, 2020) At NECEP, he develops R-based forecasting models for Portugal’s economy and contributes to quarterly newsletters. He has designed tools for traffic forecasting (BRISA), financial plan modeling (tourist resorts), and socioeconomic assessments (e.g., EuroPACE project). His work aligns with UN Sustainable Development Goals , particularly in Education and Water Sustainability .

• adaptive systems
• altruism
• applied empirical methods
• +۱۲ مورد دیگر

Nikos Askitas serves as Coordinator of Data and Technology at the Institute of Labor Economics (IZA) in Bonn, Germany, where he leads both the Research Data Center (IDSC) and ICT unit since joining in 2000. His dual role bridges technical infrastructure management and cutting-edge research in labor economics and social science. His research spans causal machine learning , epidemic disease modeling , big data analytics , and behavioral macroeconomics , with notable contributions to forecasting methodologies and web-based data applications. He develops novel approaches in opinion dynamics, game theory, and adaptive systems while addressing real-world challenges like pandemic response and technology-labor interactions. Recent publications reveal a strong trend toward AI-driven economic analysis , particularly examining generative AI's impact on scientific communication and labor markets. His work consistently integrates web data and real-time indicators with traditional economic modeling, emphasizing practical policy applications in consumption tracking, referendum forecasting, and mobility pattern analysis. His scientific recognition includes: CESifo Research Fellow in Economics of Digitisation Academic Editor for PLoS ONE (Economics section) As infrastructure leader, he oversees IZA's research data ecosystem and technological operations, enabling large-scale empirical studies while advancing methodological frontiers through hands-on machine learning pedagogy for social scientists. His current projects focus on GenAI implications, nowcasting techniques, and causal inference frameworks.

Renato Procopioمشاهده پروفایل

استاد

• Electrical Engineering
• Power Systems
• Renewable Energy Integration
• +۳ مورد دیگر

Renato Procopio is a full-time Professor at the Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN), University of Genoa , Italy. His work focuses on power systems, lightning protection, renewable energy integration, and machine learning applications in energy systems. Current research includes lightning-induced overvoltage analysis , optimal inertia allocation for transmission networks, and vehicle-to-home energy systems Recent publications highlight applications of machine learning in lightning location, photovoltaic microgrid optimization , and energy storage virtual partitioning . His work addresses grid stability in renewable-heavy networks and soil conductivity impacts on distribution line performance. He is organizing the 2025 AEIT HVDC International Conference in Genoa, Italy.

Robert Trappمشاهده پروفایل

استاد

• Mesoscale Dynamics
• Severe Thunderstorms
• Tornadoes
• +۵ مورد دیگر

Robert Trapp is a Professor of Atmospheric Sciences at the University of Illinois at Urbana-Champaign (UIUC) and Director of the School of Earth, Society & Environment. He holds additional affiliations as a Professor at the National Center for Supercomputing Applications (NCSA) and leads the Trapp Severe Weather Research Group. His career includes roles at Purdue University (2003–2014) and the National Severe Storms Laboratory (1996–2003), with visiting appointments at NCAR. Education: PhD in Meteorology (University of Oklahoma, 1994), MS in Meteorology (Texas A&M, 1989), BS in Agriculture/Atmospheric Science (University of Missouri, 1985). Research focuses on mesoscale convective systems, severe thunderstorms, tornado dynamics, and climate change impacts. Key areas include tornado climatology, QLCS (quasi-linear convective systems) behavior, and the effects of global warming on convective weather. His work employs observational data, numerical modeling, and satellite observations. Recent projects include the PERiLS study of southeastern U.S. tornadoes and evaluations of CMIP6 climate models for severe storm representation. Publications emphasize tornado prediction, climate model validation, and extreme weather attribution. He collaborates with NSF, NASA, and industry partners, advancing tools like radar-based tornado forecasting and machine learning for wind load design. Labs/Teams: Trapp Severe Weather Research Group, PERiLS Project leadership, and involvement with the Flexible Array of Radars and Mesonets (FARM).

Robert Sicaمشاهده پروفایل

استاد

• Atmospheric Physics
• Lidar Technology
• Climate Science
• +۳ مورد دیگر

Robert Sica is a Professor in the Department of Physics and Astronomy at Western University. His research focuses on atmospheric physics, lidar technology, and environmental monitoring. He leads the MPLCAN/MPLNET micro-pulse lidar networks and the Purple Crow Lidar initiative, specializing in aerosol, cloud, and temperature measurements. Key areas include wildfire smoke tracking, instrument calibration, and climate change impacts. Education details are not explicitly listed, but his long-term involvement with lidar systems indicates advanced training in atmospheric sciences and remote sensing. His work integrates optical engineering, machine learning, and data assimilation techniques. Research interests emphasize atmospheric composition, remote sensing instrumentation, and their applications to climate studies. Recent work explores lidar-based humidity retrieval, wildfire smoke nowcasting, and aerosol layer identification algorithms. Labs/Teams: Purple Crow Lidar Team, MPLCAN Network, CANDAC (Canadian Network for the Detection of Atmospheric Change). Collaborates with GRUAN and NDACC networks for standardized atmospheric measurements.

Tim Poateمشاهده پروفایل

پژوهشگر

• Coastal Science
• Coastal Engineering
• Environmental Science
• +۳ مورد دیگر

Tim Poate is a Senior Research Consultant at the University of Plymouth's School of Biological and Marine Sciences, part of the Faculty of Science and Engineering. He specializes in coastal processes, focusing on wave transformation, storm impacts, and coastal morphodynamics. His work involves collaborations with international partners such as Deltares and Bangor University, and he has contributed to major projects like the SWEEP initiative and the Coastal Marine Applied Research (CMAR) consultancy group. With 15 years of coastal research experience, Tim has led fieldwork using advanced techniques including in-situ instruments (pressure sensors, GPS surveys), terrestrial laser scanning, and UAV aerial surveys. His research addresses coastal resilience to climate change, including sea-level rise and extreme storms. He has developed methodologies for coastal flood warnings and regional shoreline change projections, aiding planners and policymakers. Key Projects: SWEEP (Coastal Change Management Areas), Operational Wave Forecast (OWWL), and the Waves Across Shore Platforms (WASP) dataset. Technical Expertise: Remote sensing, numerical modeling (XBeach-G), and real-time monitoring systems. Tim's publications span coastal hazard prediction, dune dynamics, and the integration of machine learning in environmental modeling. His work emphasizes practical applications, bridging academic research with industry needs through CMAR. He has also contributed to the REF Impact Case Study on enhancing coastal resilience, demonstrating his commitment to applied science with real-world impact.

• Water resources
• Remote sensing
• Geographic Information Systems
• +۷ مورد دیگر

Marouane Temimi is an Associate Professor at Stevens Institute of Technology's Department of Civil, Environmental and Ocean Engineering. He leads the Coastal and Environmental Sensing And Modeling Lab (previously at Masdar Institute/ Khalifa University, UAE). His research focuses on water resources, remote sensing, hydrometeorology, and natural hazards. He holds a PhD from the University of Quebec in Civil Engineering with a specialization in remote sensing and water resources. Education: PhD (2006) University of Quebec; MS (2002) University of Quebec; BE (2000) National Engineering School of Tunis. Research interests include: River ice dynamics and climate impacts Remote sensing of hydrological processes Numerical modeling of atmospheric processes Land-atmosphere interactions Flood monitoring using satellite data Atmospheric dispersion modeling His work spans 15+ years in academia and industry, with notable contributions to NOAA, NASA, and UAE government-funded projects. He has received awards including US National Academy of Sciences Fellowship and multiple Chancellor’s Scholarly Awards. Over 100 peer-reviewed articles demonstrate expertise in hydrometeorology, remote sensing applications, and environmental modeling. Grants include major funding from NASA, NOAA, and UAE Ministry of Climate Change, totaling ~$10M. Current projects focus on river ice monitoring, flood resilience, and air quality in arid regions. Labs/Teams: Leads Stevens' environmental sensing lab and collaborates internationally on initiatives like the ML4LM (Machine Learning for Land Modeling) group.

Stavros Koliosمشاهده پروفایل

استادیار

• Meteorology
• Remote Sensing
• Satellite Data Analysis
• +۵ مورد دیگر

Stavros Kolios is an Assistant Professor in Aerospace Science and Technology at the National and Kapodistrian University of Athens. His research focuses on advanced meteorological systems, remote sensing technologies, and environmental monitoring using satellite data. He specializes in developing automated algorithms for weather forecasting, rainfall estimation, and atmospheric analysis. Key research interests include machine learning applications for meteorology (e.g., hail detection, aerosol monitoring), satellite-based environmental systems (GIS platforms for flood monitoring, land-use change analysis), and climatic studies in the Mediterranean region. He has pioneered projects integrating neural networks with geospatial data to enhance weather prediction accuracy and ecological assessment. His work spans 20+ years, with publications addressing topics like TRMM rainfall accuracy, dust outbreak detection, and cloud system oscillations. Collaborative projects include the Ionian-Puglia meteorological network and environmental monitoring systems for Greek ports. He actively contributes to open-source tools like the GEOMAGNET WebGIS application.

Vipin Kumarمشاهده پروفایل

استاد

• Data Mining
• High-Performance Computing
• Climate Science
• +۴ مورد دیگر

Vipin Kumar is a Regents Professor and William Norris Endowed Chair in the Department of Computer Science and Engineering at the University of Minnesota . He leads the CSE Data Science Initiative and has held roles such as Department Head (2005–2015) and Director of the Army High Performance Computing Research Center (1998–2005). His research focuses on data mining, high-performance computing, climate science, and environmental applications . Education: B.E., Electronics & Communication Engineering, Indian Institute of Technology Roorkee (1977) M.E., Electronics Engineering, Philips International Institute, Netherlands (1979) Ph.D., Computer Science, University of Maryland, College Park (1982) Research Interests: Kumar pioneers Knowledge-Guided Machine Learning (KGML) for environmental science, climate change analysis, and global ecosystem monitoring. His work integrates computational methods with domain knowledge to address challenges in agriculture, hydrology, and climate modeling. Key contributions include scalable algorithms (e.g., METIS, PSPASES) and frameworks for big data-driven discovery. Publications: Over 300 articles and 10 books, including seminal works like Introduction to Parallel Computing and Introduction to Data Mining . Recent efforts focus on applying machine learning to monitor global change and predict environmental phenomena like floods, carbon cycles, and lake temperatures. Scientific Awards: ACM SIGKDD Innovation Award (2012) IEEE Sidney Fernbach Award (2016) NSF Expeditions in Computing Grant (Climate Change Project) Fellowships: ACM, IEEE, SIAM, AAAS Advising & Grants: Advisor to over 50 graduate students (listed in full description). Principal Investigator of major grants including NSF BIGDATA, NSF INFEWS, and DARPA World Modeler initiatives. Leads interdisciplinary teams on projects like the Global Observatory for Planetary Health and Ecosystem Resources (GOPHER) . Labs/Teams: Director of GOPHER, a multi-institutional effort to monitor Earth’s ecosystems using AI and satellite data. Active in initiatives like the Climate Change Data Expedition and the Data Science Leadership Summit .

• Statistical Machine Learning
• Bayesian Statistics
• Infectious Disease Modelling
• +۹ مورد دیگر

Dr. Oliver Ratmann is an Associate Professor in Statistics and Machine Learning at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His work focuses on developing statistical and machine learning methods to address global health challenges, particularly in infectious diseases, child health, and public health policy. He co-founded the Global Reference Group for Children in Crisis and the Machine Learning and Global Health Network, and co-leads the PANGEA HIV consortium. Education: PhD in Bayesian Statistics and Network Sciences, Imperial College London (2006–2009) MSc in Bioinformatics, Imperial College London (2005) Diplom in Mathematics and Computer Science, TU Darmstadt, Germany (2000–2005) Research Interests: Statistical machine learning, Bayesian methods, HIV/AIDS epidemiology, computational biology, pandemic preparedness, and child health equity. His lab develops interdisciplinary approaches to model disease spread, optimize health interventions, and improve data-driven policy design. Key Projects: Leading studies on HIV transmission networks in Africa, global estimates of child orphanhood due to pandemics, and AI-driven disease forecasting. His work integrates genomic data, demographic surveys, and machine learning to inform public health strategies. Advising & Grants: Directs the MSc in Statistics at Imperial and mentors a research group with 3 PhD students, post-docs, and international collaborators. His grants include funding from global health initiatives and collaborative programs with institutions like the Rakai Health Sciences Program. Labs & Teams: Heads the Machine Learning and Global Health Network and contributes to the PANGEA HIV consortium, focusing on eliminating HIV transmission through genomic and epidemiological surveillance.