جست‌وجوی استاد و پژوهشگر

Yongsheng Chenمشاهده پروفایل

دانشیار

• Data Assimilation
• Numerical Modeling
• Mesoscale Dynamics
• +۴ مورد دیگر

Dr. Yongsheng Chen is an Associate Professor at York University's Department of Earth and Space Science and Engineering (ESSE). He holds a PhD from McGill University and completed postdoctoral research at the US National Center for Atmospheric Research (NCAR). His work focuses on advanced numerical modeling and ensemble-based data assimilation systems for weather and climate applications. Education: PhD in Atmospheric Science (McGill University), Postdoctoral Fellowship (NCAR) Dr. Chen specializes in atmospheric research with key areas including Data Assimilation , Numerical Modeling , and Severe Weather Prediction . His research spans regional climate modeling, air quality forecasting, and upper atmosphere studies, as demonstrated by his publications in journals like Journal of Atmospheric and Solar-Terrestrial Physics and Journal of Geophysical Research: Space Physics . His notable work includes applications of the WRF-Chem model for dust studies, tropical cyclone simulations, and wind power forecasting using neural networks. Dr. Chen collaborates on developing observing system simulation experiments and improving weather prediction systems. As part of the Severe Weather Research Lab (Petrie 249), he contributes to advancing numerical weather prediction techniques and atmospheric modeling frameworks.

Giri Narasimhanمشاهده پروفایل

استاد

• Algorithms
• Bioinformatics
• Data Science

Dr. Giri Narasimhan is a Distinguished University Professor at Florida International University (FIU) in the Knight Foundation School of Computing & Information Sciences. He previously served as the Associate Dean for Research and Graduate Studies in the College of Engineering and Computing. With a B.Tech. from the Indian Institute of Technology Bombay and a Ph.D. from the University of Wisconsin-Madison, his expertise spans Algorithms, Bioinformatics, and Data Science. He leads the Bioinformatics Research Group (BioRG), focusing on interdisciplinary research with federal and state grants from NSF, NIH, and industry. His awards include Excellence in Research Awards from SCIS and FIU, and the Best Poster Award at ISMB-SCS. He holds editorial roles, contributes to international conferences, and addresses keynotes globally. Research interests emphasize algorithms for biological data analysis, environmental modeling, and education innovation. Recent work includes flood prediction systems (FIDLAR), microbiome analysis pipelines (PluMA), and machine learning for climate science. He advocates for student industry engagement through micro-internships and promotes diversity in computing via initiatives like the Software Guild workshop. Grants and collaborations include multi-omic studies on aging, HIV, and environmental health. His labs integrate computational methods with clinical and ecological data to address complex challenges in bioinformatics and data-driven decision-making.

Byongjun Hwangمشاهده پروفایل

دانشیار

• Remote Sensing
• Geophysics
• Climate Change
• +۵ مورد دیگر

Byongjun Hwang is a Reader in Geography at the University of Huddersfield, where he is affiliated with the Department of Physical and Life Sciences within the School of Applied Sciences. He serves as Director of the Centre for Climate Resilient Societies and is a member of the Centre for Human and Physical Geography. Dr. Hwang is a geophysicist and remote sensing specialist with extensive research experience in Earth Observation to study environmental changes. His research spans from Arctic sea ice to wetlands and forests in sub-Saharan Africa. He completed his PhD from the University of Manitoba in Canada and has secured research grants from NERC, Royal Society, EU, and the U.S. Office of Naval Research. His research interests include: Arctic sea ice dynamics and fragmentation Remote sensing of environmental changes Climate change impacts on polar regions Environmental monitoring of agricultural wetlands in Malawi Coffee forest conservation in Ethiopia Land use mapping in the UK using UAV and citizen science Dr. Hwang's recent publications demonstrate a strong focus on Arctic sea ice research, particularly examining floe size distribution, fragmentation processes, and the use of satellite and aerial survey data. His work often involves collaboration with institutions such as the University of Reading, Met Office, ECMWF, NOC, British Antarctic Survey, Lilongwe University of Agriculture and Natural Resources, and Jimma University. Scientific recognition includes: h-index of 18 from 1,369 citations (Google Scholar) 1001 Citations, 18 h-index (Scopus) Dr. Hwang teaches remote sensing, GIS, climate, statistics, and mountain environments. He actively supervises postgraduate students and welcomes research ideas involving image analysis/machine learning, advanced satellite algorithms, and UAV technology for environmental monitoring. He is currently leading projects including 'Forest conservation through traceable supply chain development' (2023-2026) and the 'Resilient Societies Research Centre' (2021-2024). His research contributes to UN Sustainable Development Goals related to climate action, life on land, and responsible consumption and production. Dr. Hwang has presented his work at numerous international conferences and has been featured in media coverage of Arctic expeditions.

Mitchel Colebankمشاهده پروفایل

استادیار

• Cardiovascular Modeling
• Uncertainty Quantification
• Scientific Machine Learning
• +۲ مورد دیگر

Dr. Mitchel Colebank is Assistant Professor in Mathematics at the University of South Carolina with affiliate appointment in Biomedical Engineering. He holds a PhD in Biomathematics from North Carolina State University and completed postdoctoral training at UC Irvine. His research develops computational models of cardiovascular physiology, focusing on uncertainty quantification, parameter estimation, and scientific machine learning. Current projects include pulmonary hemodynamics modeling, digital twin development, and machine learning for healthcare applications. Colebank received the NIAID R25 Big Data Health Science Fellowship and Best Paper Prize at ICSTA'21. Research areas: Cardiovascular and pulmonary hemodynamics Bayesian inference methods Multiscale physiological modeling Data assimilation techniques

Feng Guمشاهده پروفایل

استاد

• Modeling and Simulation
• Complex Systems
• High Performance Computing
• +۵ مورد دیگر

Dr. Feng Gu is a Professor of Computer Science at the College of Staten Island, The City University of New York (CUNY). He holds degrees from Georgia State University (Ph.D., M.S.), Beijing Institute of Machinery (M.S.), and China University of Mining and Technology (B.S.). His research focuses on modeling and simulation, complex systems, and high-performance computing, supported by NSF and NIJ grants. Key achievements include pioneering work in malware detection, adaptive particle filtering, and genomic data analysis. He previously served as Department Chair at Voorhees College (2012–2013). **Education:** Ph.D. and M.S., Computer Science, Georgia State University M.S., Information Systems, Beijing Institute of Machinery B.S., Mechanical Engineering, China University of Mining and Technology **Research Interests:** High-performance computing for large-scale systems Data assimilation in wildfire simulation and genomic analysis Machine learning applications in cybersecurity and healthcare Parallel/distributed algorithms for particle filters **Awards:** Best Paper Award (TMS/DEVS 2018) Outstanding Paper in 2018 Emerald Literati Awards Runner-up for ANSS 2015 Best Paper His work bridges computational methods with real-world challenges in environmental modeling, healthcare, and security. Active in interdisciplinary collaborations, he has published extensively in top conferences like IEEE, ACM, and Springer journals.

• Porous Media Flow
• Geological Carbon Sequestration
• Subsurface Energy Systems
• +۲ مورد دیگر

Dr. Cheng Chen is Associate Professor and Nariman Farvardin Professor in the Department of Civil, Environmental and Ocean Engineering at Stevens Institute of Technology. He directs the Subsurface Energy, Water and Environmental Systems (SEWES) Lab, focusing on porous media transport phenomena through combined computational and experimental approaches. His NSF and DOE-funded research addresses multiphase flow in geological formations for carbon sequestration, shale energy recovery, and nuclear waste disposal. Recent work integrates machine learning for scale-bridging data assimilation and distributional reinforcement learning for autonomous marine systems. Chen's methodological innovations include lattice Boltzmann simulations for pore-scale analysis and Gaussian process models for uncertainty quantification. Scholarly outputs demonstrate consistent focus on energy-geoscience interfaces, with recent expansion into privacy-preserving federated learning applications for IoT systems. His 2023-2024 publications increasingly address security challenges in distributed computing and adversarial machine learning. Major Recognition: Stanford Top 2% Most Cited Scientist (2023) Nariman Farvardin Endowed Professorship Chen teaches numerical methods in environmental engineering and subsurface flow modeling, maintaining industrial partnerships with Schlumberger, SeaRobotics, and Consolidated Edison. He holds editorial roles for SPE Journal and has authored over 60 peer-reviewed publications.

Hyongki Leeمشاهده پروفایل

استاد

• Remote Sensing
• Hydrology
• Geodesy
• +۳ مورد دیگر

Hyongki Lee is the Kaspar J. Willam Professor in the Department of Civil and Environmental Engineering at the University of Houston , where he has served as Professor since 2023. He previously held Associate Professor (2017-2023) and Assistant Professor (2011-2017) positions at the same institution. Dr. Lee directs the Geosensing Systems Engineering Graduate Program and specializes in spaceborne geodetic instruments for terrestrial water dynamics research. Ph.D. in Geodetic Science, Ohio State University, 2008 M.Sc. in Civil and Environmental Engineering (Geomatics & Remote Sensing), Yonsei University, 2002 B.Sc. in Civil and Environmental Engineering, Yonsei University, 2000 His research integrates satellite altimetry , SAR/InSAR , and GRACE data to quantify terrestrial water storage changes for water resource management , flood forecasting , and climate studies . Key projects include operational flood prediction systems for the Mekong Basin and glacier dynamics monitoring in Alaska. Recent work focuses on machine learning-enhanced flood modeling (2025) and multi-sensor water extent mapping (2024). His FIER framework enables rapid inundation forecasting via rotated EOF analysis. Kaspar J. Willam Professorship (2025) Cullen College of Engineering Professorship (2020-2024) ASCE EWRI Best Case Study Award (2021) NASA New Investigator Award (2014) Dr. Lee has supervised 15+ graduate students and postdoctoral researchers, including Tien Du and Chi-Hung Chang . His lab (NCALM) secures grants from NASA , NOAA , and NSF for remote sensing applications in vulnerable regions like the Mekong and Congo basins.

Youmin Tangمشاهده پروفایل

استاد

• Climate Prediction
• Numerical Modeling
• ENSO Dynamics
• +۲ مورد دیگر

Dr. Youmin Tang is a Professor in the Department of Geography, Earth, and Environmental Sciences at the University of Northern British Columbia. His research utilizes sophisticated numerical models and mathematical tools to predict seasonal climate variability, with particular focus on ENSO (El Niño Southern Oscillation) dynamics. His work aims to improve climate predictions critical for sectors like agriculture, forestry, fisheries, and energy production. Research interests center on climate modeling, prediction systems, and understanding ocean-atmosphere interactions. His work combines traditional numerical methods with emerging machine learning techniques to enhance forecast accuracy and understand multidecadal climate patterns. Publication analysis reveals strong focus on tropical climate phenomena (ENSO, IOD, monsoons), prediction methodology development (ensemble systems, machine learning approaches), and ocean-atmosphere interactions. Recent works increasingly incorporate AI techniques to address prediction challenges. Active in graduate education, Dr. Tang supervises students in Natural Resources and Environmental Studies (NRES) programs with focus areas in Environmental Science and Geography.

Di Tianمشاهده پروفایل

دانشیار

• Agroclimatology
• Climate Science
• Environmental Data Science
• +۴ مورد دیگر

Dr. Di Tian is an Associate Professor of Agroclimatology in the Department of Crop, Soil, and Environmental Sciences at Auburn University's College of Agriculture. His research focuses on integrating climate science, remote sensing, and machine learning to address environmental challenges in agriculture and water resources. Key areas include climate extremes modeling, drought prediction, and sustainable irrigation strategies. Education details are not explicitly stated in the provided text, but his work emphasizes advanced computational methods applied to agricultural systems. Research interests span agroclimatology, hydrological processes, and the application of deep learning to environmental data analysis. Recent publications emphasize subseasonal forecasting of soil moisture and flash droughts, climate model bias correction, and the use of remote sensing (e.g., ECOSTRESS) for evapotranspiration monitoring. His work bridges climate science with actionable insights for agriculture and water management. Dr. Tian leads the Hydroclimate Research Group, offering research opportunities for undergraduate and graduate students, postdocs, and visiting scholars. He provides research assistantships and nominates outstanding candidates for fellowships like the Presidential Graduate Research Award. Postdoctoral opportunities prioritize candidates with backgrounds in hydrology, climatology, or data science. Labs and teams include the Hydroclimate Research Group, which develops frameworks for climate impact assessment and integrates numerical weather predictions with satellite data. Current projects focus on compound climate extremes, irrigation strategies under climate change, and improving crop resilience through data-driven approaches.

Andrea N. Arnoldمشاهده پروفایل

دانشیار

• Inverse Problems
• Uncertainty Quantification
• Bayesian Inference
• +۷ مورد دیگر

Andrea N. Arnold serves as an Associate Professor in the Department of Mathematical Sciences at Worcester Polytechnic Institute, with cross-disciplinary affiliations in Bioinformatics & Computational Biology, Data Science, and Neuroscience. Her office is located in Stratton Hall 427, and she maintains an active research profile with recent contributions through August 2025. Education: BS in Mathematics, Duquesne University (2009) PhD in Applied Mathematics, Case Western Reserve University (2014) Her research centers on inverse problems and uncertainty quantification, developing Bayesian inference frameworks for parameter estimation in biological systems. She integrates computational mathematics, statistics, and scientific computing to address healthcare challenges, particularly in stroke modeling and medical robotics. Current projects emphasize real-world algorithmic applications with societal impact in medicine. Recent publications demonstrate consistent focus on computational inverse problems, with emerging trends in scientific machine learning for biological data and medical device innovation. Her work bridges mathematical theory with clinical applications, notably in neurological modeling and surgical robotics. Scientific Awards: None mentioned in available sources. Professor Arnold actively mentors undergraduate and graduate researchers, including Maddie (glial cell dynamics), Nick (virtual palpation robotics), and recent graduates Elliot, Natalie, Caitlin, and Derek. She leads Team MADDI on scientific machine learning projects and supervises summer research initiatives through programs like the Emory REU. Her lab engages in interdisciplinary collaboration with neuroscience and medical engineering groups, focusing on data-driven modeling for stroke recovery and surgical innovation. Outreach efforts include organizing WPI's annual Sonia Kovalevsky Day for middle school girls, promoting STEM participation through hands-on mathematics activities.

Stephan Sturmمشاهده پروفایل

دانشیار

• Financial Mathematics
• Stochastic Analysis
• Stochastic Volatility Modeling
• +۵ مورد دیگر

Stephan Sturm is an Associate Professor in the Department of Mathematical Sciences at Worcester Polytechnic Institute (WPI). He holds a PhD from the Technical University of Berlin (2010) and a BS/MS from the University of Vienna (2004). His research focuses on financial mathematics, stochastic analysis, and stochastic volatility modeling, with particular emphasis on portfolio optimization under incentive schemes and arbitrage-free pricing frameworks. He contributes to WPI's professional master's program in financial mathematics and advises students on theses and MQPs. His academic journey includes a postdoctoral position at Princeton University's Department of Operations Research and Financial Engineering. He has taught extensively at WPI and other institutions, covering topics like stochastic processes, financial risk management, and actuarial mathematics. His work bridges theoretical advancements in stochastic analysis with practical applications in financial engineering. Key research themes include XVA valuation (CVA/DVA/FVA), systemic risk analysis in financial networks, and optimal consumption-investment problems under market frictions. His publications address both foundational mathematical challenges and real-world market dynamics, often employing sophisticated stochastic calculus and numerical methods. Teaching highlights include courses on measure-theoretic probability, computational finance, and derivatives pricing. His contributions extend to curriculum development at WPI's IQP project centers in Asia, integrating financial mathematics with global industry challenges.

Tirupati Bolisettiمشاهده پروفایل

استاد

• Hydrology
• Climate Change Impact Assessment
• Geothermal Energy
• +۴ مورد دیگر

Dr. Tirupati Bolisetti is a Professor in the Department of Civil and Environmental Engineering at the University of Windsor's Faculty of Engineering. He holds a PhD from the University of Windsor, an MTech from the Indian Institute of Technology (IIT) Kanpur, and a BE from Andhra University, India. His research focuses on hydrology and climate change impact assessment, geothermal energy systems, evaporation from porous surfaces, and climate resiliency of Arctic water infrastructure. Key areas include hydrological modeling, geothermal energy applications, and sustainable urban drainage systems. Selected publications highlight contributions to borehole heat exchanger analysis, low-impact development practices, and climate change impacts on hydropower and hydrology. His work integrates advanced methodologies like Polynomial Chaos Expansion for uncertainty quantification and data assimilation techniques in hydrologic models. Professionally, he is affiliated with the Professional Engineers Ontario (PEO). His research trends emphasize adapting water infrastructure to climate change, advancing geothermal energy technologies, and optimizing urban flood management systems through interdisciplinary modeling approaches.

Weimin Huangمشاهده پروفایل

استاد

• Remote Sensing
• Radar Technology
• Oceanography
• +۳ مورد دیگر

Weimin Huang is a Full Professor in the Department of Electrical and Computer Engineering at Memorial University's Faculty of Engineering and Applied Science. His research focuses on ocean remote sensing technologies including HF radar, GNSS, and marine radar systems for measuring oceanic surface parameters. He holds a PhD in Radio Physics from Wuhan University and an M.Eng from Memorial University. Research interests span electromagnetic wave propagation, digital signal processing for marine applications, and machine learning applications in remote sensing. Key areas include: High-frequency radar ocean surface mapping Satellite-based navigation systems for earth observation Microwave radar signal analysis AI-driven environmental monitoring Recent publications demonstrate strong focus on neural network applications for sea state parameter retrieval and multi-sensor data fusion techniques. Research consistently addresses challenges in harsh marine environments and climate impact assessment. Honors include: IEEE GRSS Letters Prize Paper Award (2019) PEGNL Teaching Award (2018) NSERC Discovery Accelerator Supplement (2017) Leads the Radar Remote Sensing Lab (RRSL) and mentors graduate researchers in electromagnetic theory and remote sensing methodologies.

Bledar Alex Konomiمشاهده پروفایل

دانشیار

• Bayesian statistics
• Spatial statistics
• Uncertainty quantification
• +۲ مورد دیگر

Bledar Alex Konomi is Associate Professor of Statistics at University of Cincinnati's College of Arts and Sciences. He holds a PhD from Texas A&M University (2011) and BS from Athens University of Economics and Business. Research develops Bayesian methods for large spatial/simulation datasets, uncertainty quantification, and multifidelity computer model calibration. Applications include remote sensing, storm surge prediction, and paleoclimate reconstruction. Recent articles feature: Spatial modeling innovations for big data Satellite calibration techniques Bayesian decision tree algorithms Ice model parameter estimation Advises doctoral students in statistical computing and spatial modeling. Teaches graduate courses: Bayesian Analysis, Statistical Machine Learning, and Mathematical Statistics. Secured NSF/NASA JPL grants for remote sensing uncertainty quantification. Previously held positions at Pacific Northwest National Laboratory and Simon Fraser University.

Xuemin Tuمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Applied Mathematics
• +۳ مورد دیگر

Xuemin Tu is a Professor in the Department of Mathematics at the University of Kansas, serving as the Scheduling Officer. His research focuses on scientific computing, numerical analysis, and applied mathematics, emphasizing domain decomposition methods, BDDC algorithms, and data assimilation. He is affiliated with the College of Liberal Arts & Sciences and actively contributes to interdisciplinary computational science. Research interests include iterative methods for partial differential equations, numerical solutions of fluid dynamics problems, and the development of efficient algorithms for large-scale simulations. His work bridges theoretical analysis and practical applications in engineering and geophysics. Key trends in his recent articles (2018–2025) highlight advancements in BDDC algorithms for fluid and porous media systems, implicit sampling techniques for data assimilation, and machine learning applications in image recognition. He also explores stochastic models for parameter estimation and uncertainty quantification. No scientific awards or grants are explicitly listed in the provided text. Advising roles or student mentorship details are not included, though his research areas suggest contributions to graduate training in computational mathematics. Labs or collaborative teams are not detailed here, but his affiliations imply involvement with computational science initiatives at the University of Kansas.