جست‌وجوی استاد و پژوهشگر

Ali Nazemiمشاهده پروفایل

دانشیار

• Hydrology
• Water Security
• Climate Change
• +۳ مورد دیگر

Dr. Ali Nazemi is an Associate Professor in the Department of Building, Civil, and Environmental Engineering at Concordia University, where he joined in 2015 as a Strategic Hire in Water Resources. He holds adjunct appointments at the University of Saskatchewan's School of Environment and Sustainability and is an Associate Member of the Global Institute for Water Security. His foundational education includes a PhD from the University of Birmingham (UK), an MSc from Ferdowsi University of Mashhad (Iran), and a BSc from KNT University of Technology (Iran). Dr. Nazemi's research advances methodologies for water security challenges under climate change, focusing on: Hydrological modeling and algorithm development Climate change vulnerability assessments Coupled human-water systems Hydroclimatic data diagnostics His recent publications predominantly explore climate model integration, uncertainty quantification in water systems, and hybrid modeling approaches, with consistent themes of climate adaptation and risk management across Canadian and international contexts. He has been recognized with several awards, including: Dorothy Hodgkin Postgraduate Award (UK government, 2005–2009) Best Presentation Award at IEEE World Congress on Computational Intelligence (2006) Post Graduate Teaching Award (University of Birmingham, 2004–2007) Dr. Nazemi actively supervises graduate students through his Water Security and Climate Change (WSCC) lab, with projects on climate downscaling, vulnerability assessment tools, and coupled human-water systems. He leads research collaborations with the Saskatchewan Water Security Agency, NSERC's Changing Cold Region Network, and the Global Water and Energy Cycle Experiment.

Douglas Nychkaمشاهده پروفایل

استاد

• Spatial Statistics
• Nonparametric Regression
• Computational Statistics
• +۵ مورد دیگر

Douglas Nychka is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines since 2018. He holds an emeritus position at the National Center for Atmospheric Research (NCAR), where he previously directed the Institute for Mathematics Applied to Geosciences (IMAGe) from 2004 to 2017. Nychka earned his PhD in Statistics from the University of Wisconsin-Madison and a BA in Mathematics (with Physics emphasis) from Duke University. His research focuses on spatial statistics, nonparametric regression, and computational methods for large datasets, particularly applied to environmental and geophysical problems. He has developed influential R packages like fields and LatticeKrig , which are widely used for spatial data analysis. Nychka received prestigious awards including the Jerry Sacks Award for Multidisciplinary Research (2004) and recognition as a Fellow of both the American Statistical Association and the Institute of Mathematical Statistics. His work bridges statistical theory, computational innovation, and real-world applications in climate science and renewable energy. His academic career includes 14 years as a faculty member at North Carolina State University and roles at the National Institute of Statistical Sciences. Nychka's research emphasizes spatial statistics for climate data, statistical downscaling, and uncertainty quantification, with contributions to solar radiation modeling and extreme event analysis. He actively engages in interdisciplinary projects, collaborating with experts in climatology, environmental science, and data science. Professional service includes roles on committees for the National Research Council and leadership in statistical societies. His teaching focuses on modernizing curricula to integrate data science with applied statistics. Nychka’s work is characterized by a commitment to open-source software and reproducible research, exemplified by his R package contributions.

• Hydrology
• Hydrogeology
• Climate Change Impact
• +۳ مورد دیگر

Nathan Young is an Assistant Professor in the Department of Sustainable Resources Management at SUNY College of Environmental Science and Forestry (ESF). His research focuses on physical hydrology and environmental science, with a particular emphasis on understanding how heat, water, and solutes move through the environment in response to climate change and landscape alterations such as permafrost thaw, sea-level rise, and environmental hazards like wildfires and landslides. He integrates fieldwork, numerical modeling, and quantitative analysis to improve predictive models for sustainable water management. Education includes a Ph.D. in Geology and Environmental Science (2019, Iowa State University), an M.S. in Earth and Environmental Science (2014, Wright State University), and a B.A. in Geology and Sociology/Anthropology (2012, Earlham College). Research interests span hydrogeology, climate change impacts, permafrost dynamics, and groundwater flow modeling. His work often involves advanced tools like MATLAB, R, Python, and numerical models such as MODFLOW and HydroGeoSphere. He emphasizes fieldwork in student mentorship, valuing hands-on experience and quantitative skills. Teaching includes courses like Watershed Hydrology (FOR 340) and Watershed Ecology and Management (FOR 442). Current advisee Ford Ford is pursuing an MS in Environmental Science. Awarded funded graduate assistantships and undergraduate research positions are available to qualified candidates with academic excellence, research experience, and enthusiasm for hydrological studies. His lab focuses on cryohydrogeological systems, particularly in Arctic regions like Nunavik, Québec, and investigates the interplay between thermal regimes, permafrost degradation, and subsurface hydrology.

Miguel F. Anjosمشاهده پروفایل

استاد

• Mathematical Optimization
• Smart Grids
• Operations Research
• +۳ مورد دیگر

Miguel F. Anjos is Professor and Chair of Operational Research at the School of Mathematics, University of Edinburgh , and holds the NSERC-Hydro-Québec-Schneider Electric Industrial Research Chair on Optimization for Smart Grids at Polytechnique Montréal. He received his B.Sc. (1992), M.S. (1994), and Ph.D. (2001) from McGill, Stanford, and Waterloo respectively. Research Theme Head of Data and Decisions at Edinburgh Founding Director of Trottier Institute for Energy Editor-in-Chief of Optimization and Engineering Research Interests: His work bridges mathematical optimization with smart grid applications , focusing on conic optimization, optimal power flow, demand response, and facility layout. He applies these techniques to energy storage, electric transportation, and industrial systems. Scientific Awards: Méritas Teaching Award (2012) Humboldt Research Fellowship (2009) Queen Elizabeth II Diamond Jubilee Medal (2013) Elected Fellow of EUROPT and Canadian Academy of Engineering Academic Service: Served on Mathematical Optimization Society Council, SIAM Activity Group on Optimization, INFORMS Optimization Society Vice-Chair, and Mitacs Research Review Committee. Hosts benchmark datasets: QAPLIB, FLPLIB, Jones Benchmark.

Virginia Youngمشاهده پروفایل

استاد

• Actuarial Mathematics
• Financial Mathematics
• Insurance Decision Making
• +۳ مورد دیگر

Virginia Young is the Cecil J. and Ethel M. Nesbitt Professor of Actuarial Mathematics at the University of Michigan's Department of Mathematics, within the College of Literature, Science, and the Arts. She holds a Ph.D. from the University of Virginia (1984). Her research focuses on actuarial and financial mathematics, particularly decision-making processes for individuals and insurance companies in financial and insurance contexts. This includes topics like optimal reporting strategies, reinsurance mechanisms, and risk management under uncertainty. Her work addresses modern challenges such as defined contribution pension plans and strategic insurance product design. Key research areas include stochastic control theory, game-theoretic models in insurance markets, and optimization under model ambiguity. She explores how insurers and individuals make decisions under risk, with applications to annuities, reinsurance chains, and lifetime financial planning. Recent studies investigate Stackelberg games in reinsurance, optimal deductible insurance, and minimizing lifetime ruin probabilities through strategic annuitization. Virginia Young has no listed scientific awards in the provided texts. She advises no formally documented students, though her role likely involves mentoring within the Mathematics Department. Her work contributes to both theoretical advancements and practical applications in actuarial science and financial risk management.

Peter Molnarمشاهده پروفایل

استاد

• Hydrology
• Geomorphology
• Sediment Dynamics
• +۷ مورد دیگر

Peter Molnar is a Professor at ETH Zürich's Department of Civil, Environmental and Geomatic Engineering, where he leads research in hydrology, geomorphology, and environmental engineering. His work spans river basin dynamics, sediment transport, precipitation modeling, and climate-water interactions, with field sites focused on Alpine systems. His research interests center on river basin water and sediment dynamics , precipitation analysis and stochastic modeling , fluvial systems including river networks and riparian zones , and geomorphic processes in mountain streams . Key projects include the Maggia River Experimental Site, basin sediment cascade modeling, and hydroclimatic analysis of rainfall intensity-temperature relationships. His publication record shows consistent high-impact work in journals including Nature , Nature Climate Change , and Water Resources Research , with recent focus on climate change impacts, sensor development, and sustainable agriculture. Golden Owl Award for best teaching (2011) Organizer of international workshops on geomorphology complexity and urban precipitation Patron of CometX initiative Molnar actively mentors PhD students including current candidates working on droughts in Ethiopia, Alpine Rhine sediment budgets, and mountain ecohydrology. His research is supported by Swiss National Science Foundation grants, ETH funding, and international collaborations. He leads experimental work at the Maggia River site and develops warning systems for rainfall-triggered landslides.

Jonathan Roddenمشاهده پروفایل

استاد

• Comparative Politics
• Political Economy
• Federalism
• +۶ مورد دیگر

Jonathan Rodden is a Professor of Political Science at Stanford University and a Senior Fellow at the Hoover Institution and Stanford Institute for Economic Policy Research. His research focuses on the comparative political economy of institutions, particularly federalism and fiscal decentralization, with significant contributions to understanding the urban-rural political divide and its implications for representation and policy. PhD from Yale University BA from the University of Michigan Fulbright student at University of Leipzig His research spans several domains: Comparative Federalism: Analyzed fiscal decentralization mechanisms, budgetary transfers, and institutional design in federations globally. Political Geography: Developed computational methods for redistricting analysis and demonstrated how geographic voter distribution creates inherent electoral biases. Policy Uncertainty: Co-authored foundational work linking economic uncertainty to political polarization and fiscal cycles. Public Health: Conducted epidemiological studies on firearm-related mortality and suicide patterns in California. Scientific awards include: Gregory Luebbert Prize for best book in comparative politics (2007) Michael Wallerstein Award for best paper in political economy (2015) He has advised PhD students like Jowei Chen and directed policy projects with international institutions including the World Bank, IMF, and OECD. His work on redistricting simulations has been cited in U.S. Supreme Court amicus briefs and congressional testimony.

Taylor Johnsonمشاهده پروفایل

دانشیار

• Formal Verification
• Cyber-Physical Systems (CPS)
• Artificial Intelligence
• +۷ مورد دیگر

Taylor Johnson is an Associate Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He directs the Verification and Validation for Intelligent and Trustworthy Autonomy Laboratory (VeriVITAL) and serves as a Senior Research Scientist in the Institute for Software Integrated Systems. Previously, he was an Assistant Professor at the University of Texas at Arlington from 2013 to 2016. His research focuses on formal verification techniques for cyber-physical systems (CPS), emphasizing safety, reliability, and security through hybrid systems, formal methods, and control theory. He has published extensively on neural network verification, earning best paper awards and recognition from IEEE, IFIP, and ACM. Education: Ph.D., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2013) M.Sc., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2010) B.S.E.E., Electrical and Computer Engineering (Rice University, 2008) Research Interests: Formal verification of neural networks and CPS, safety-critical systems, autonomous systems, and AI/ML security. His work bridges theoretical foundations (e.g., hybrid systems) with practical applications in aerospace, energy systems, and robotics. Key Contributions: Developed the NNV tool for neural network verification, led the Verification of Neural Networks Competition (VNN-COMP), and pioneered techniques for robust federated learning and malware detection. Awards: AFOSR YIP Award (2016), NSF CRII Award (2015), and multiple best paper honors. His research is funded by AFRL, NSF, Intel, NVIDIA, and industry partners. Labs & Collaborations: VeriVITAL Lab (Vanderbilt), collaborations with United Technologies Research Center, Boeing, and Toyota.

Elliot Huiمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Microfluidics
• Spatial Cell Biology
• +۳ مورد دیگر

Elliot Hui, Ph.D., is an Associate Professor in the Department of Biomedical Engineering at the University of California, Irvine (UCI), within the Samueli School of Engineering. His research focuses on biological microtechnology, including spatial cell biology, microscale tissue engineering, global health diagnostics, and microfluidic computing. He leads the Hui Lab, which develops tools for automating biochemical reactions, controlling cellular organization, and understanding tissue development dynamics. Key achievements include pioneering microfluidic logic systems for autonomous laboratory automation and creating novel cell culture platforms to study intercellular communication in tissues. His work bridges engineering and biology, addressing challenges in diagnostics and regenerative medicine. Notable contributions include the development of a programmable finite state machine for microfluidic control and a SLAS Fellowship awarded to his student Erik. Research Interests: Microfluidic devices, cell-cell interaction modeling, tissue engineering, and lab-on-a-chip systems. Labs/Teams: Hui Lab at UCI, specializing in microscale biological systems and automation. Publications span topics such as microfluidic computing architectures, tissue dissociation devices, and Bayesian experimental design. His work emphasizes applications in global health diagnostics and mechanistic studies of cellular processes.

Naratip Santitissadeekornمشاهده پروفایل

مدرس ارشد

• Data Assimilation
• Inverse Problems
• Geophysical Fluid Dynamics
• +۵ مورد دیگر

Naratip Santitissadeekorn is a Senior Lecturer in Data Assimilation at the School of Mathematics and Physics, University of Surrey, where he is affiliated with the Mathematics at the Interface Group. His work bridges mathematics, data science, and real-world applications in urban planning, crime analysis, and geophysical fluid dynamics. Dr. Santitissadeekorn received his PhD from Clarkson University in 2008, with a dissertation titled "Transport Analysis and Motion Estimation of Dynamical Systems of Time-Series data." His doctoral research was supervised by Professor Erik Bollt. Following his PhD, he completed two significant postdoctoral positions: from 2008-2011 at the University of New South Wales, Sydney, Australia, working with Professor Gary Froyland on numerical techniques for finite-time Lagrangian coherent set identification, with applications to delimiting the polar vortex and Agulhas rings; and from 2011-2014 at the University of North Carolina-Chapel Hill, working with Professor Chris Jones on data assimilation projects. Dr. Santitissadeekorn's research focuses on inverse problems and data assimilation in geophysical fluid dynamics, the applications of Lagrangian Coherent Structures (LCS), and computational ergodic theory. His work combines theoretical mathematics with practical applications, particularly in urban growth modeling and crime analysis. He has developed innovative methods for identifying coherent structures in fluid flows, estimating transition probabilities from spatiotemporal data, and creating data-driven frameworks for urban expansion scenarios. His research demonstrates how mathematical techniques can be applied to solve real-world problems in environmental science, urban planning, and public safety. An analysis of Dr. Santitissadeekorn's recent publications (2020-2023) reveals a strong focus on urban expansion modeling and network analysis. His work on urban growth has evolved from basic cellular automata models to sophisticated frameworks that manage uncertainty through parameter clustering and growth mode identification. His research on Hawkes processes has advanced ensemble-based filtering techniques for analyzing count data in large networks. These publications demonstrate a consistent pattern of applying mathematical rigor to complex spatiotemporal phenomena, with increasing emphasis on data-driven approaches and practical applications. Dr. Santitissadeekorn has made significant contributions to data assimilation methods, particularly through the development of the extended Poisson-Kalman filter (ExPKF) for urban crime modeling. His teaching includes courses in Algebra and Bayesian Statistics, reflecting his expertise in both theoretical and applied mathematics. While specific awards are not mentioned in the available information, his extensive publication record in high-impact journals demonstrates recognition within his field. Dr. Santitissadeekorn's research has practical implications for urban planning and law enforcement. His work on urban expansion models helps planners understand different growth trajectories, while his crime modeling research contributes to improved police patrolling strategies. His interdisciplinary approach, combining mathematics, computer science, and domain-specific knowledge, positions him at the forefront of applying data science to societal challenges.

Werner Bauerمشاهده پروفایل

مدرس

• Numerical Analysis
• Scientific Computing
• Fluid Dynamics
• +۵ مورد دیگر

Dr. Werner Bauer is a Lecturer in Mathematics at the University of Surrey, affiliated with the Mathematics at the Interface Group within the School of Mathematics and Physics. His research focuses on numerical analysis and scientific computing, particularly in the Mathematics of Planet Earth. Key areas include parallel-in-time methods for oscillatory PDEs, structure-preserving discretizations for fluid dynamics, stochastic flow models for ensemble prediction, and geometric formulations of fluid and magnetohydrodynamic systems. He also explores finite difference and finite element methods, with prior work on grid adaptation in weather and climate models. His research interests span numerical methods for geophysical flows, stochastic modeling of oceanic and atmospheric dynamics, and energy-conserving computational frameworks. Bauer’s recent work emphasizes uncertainty quantification, ensemble forecasting, and the development of compatible finite element schemes to ensure physical conservation laws in simulations. Bauer’s publications highlight advancements in structure-preserving discretizations, stochastic parameterization of mesoscale eddies, and variational integrators for geophysical equations. His work bridges applied mathematics and computational science with applications in climate modeling and environmental fluid dynamics.

Daniela Calvettiمشاهده پروفایل

استاد

• Large Scale Scientific Computing
• Computational Inverse Problems
• Uncertainty Quantification
• +۴ مورد دیگر

Daniela Calvetti is the James Wood Williamson Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. Her research focuses on large-scale scientific computing, computational inverse problems, uncertainty quantification, and predictive modeling in neuroscience, metabolism, and cellular physiology. She holds a PhD from the University of North Carolina-Chapel Hill. Her work integrates advanced mathematical techniques with biomedical applications, including brain energy metabolism modeling, MEG/EEG source reconstruction, and computational methods for medical imaging. Notable contributions include Bayesian hierarchical algorithms for inverse problems and interdisciplinary collaborations bridging mathematics with neuroscience and physiology. Recent research highlights include developing sparsity-promoting Bayesian models for tomography, computational frameworks for neuromuscular control variability, and predictive models of disease dynamics like post-pandemic COVID-19 recurrence. Her methodologies emphasize statistically inspired preconditioning and adaptive meshing techniques to enhance computational efficiency in solving complex inverse problems. Dr. Calvetti has published extensively across computational science, inverse problems, and biomedical applications. She leads a research group advancing interdisciplinary computational methods with applications in neuroscience, virology, and metabolic systems.

Xuhui Fanمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Bayesian Methods
• +۵ مورد دیگر

Dr. Xuhui Fan is a Lecturer in Artificial Intelligence at the School of Computing, Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (Australia) and a bachelor's degree in Mathematical Statistics from China. Prior to his current role, he worked as a project engineer at Data61 (formerly NICTA), a postdoc fellow at the University of New South Wales, and a lecturer at the University of Newcastle. His research focuses on Bayesian methods, federated learning, temporal point processes, and neural network architectures. He is affiliated with the Data Horizons Research Centre and the Frontier AI Research Centre at Macquarie University. Key research interests include developing interpretable AI models, advancing federated learning for privacy-sensitive applications, and applying Bayesian techniques to complex data analysis. His work bridges theoretical advancements in machine learning with practical applications in areas such as anomaly detection, generative models, and spatio-temporal data analysis. Dr. Fan’s publications span top-tier conferences like NeurIPS, ICML, and IJCAI, covering topics such as diffusion models, nonstationary processes, and scalable relational models. He has contributed to surveys on Bayesian federated learning and developed novel frameworks for dynamic customer segmentation and network sustainability. His research collaborations span institutions in Australia and internationally, reflecting his expertise in interdisciplinary AI applications. Current projects emphasize ethical AI practices, efficient uncertainty quantification, and scalable inference techniques for large-scale datasets.

Farhad Malekiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Medical Imaging
• +۵ مورد دیگر

Dr. Farhad Maleki is an Assistant Professor in the Department of Computer Science at the University of Calgary, Faculty of Science. He holds a PhD in Computer Science from the University of Saskatchewan (2019). His postdoctoral research at McGill University’s Augmented Intelligence & Precision Health Laboratory focused on machine learning for medical image analysis. He has held leadership roles, including President of the Association of Postdoctoral Fellows at McGill and President of the Computer Science Graduate Council at the University of Saskatchewan. Currently, he serves on the Machine Learning Education Sub-Committee of the Society for Imaging Informatics in Medicine and as a guest editor for journals in medical data analysis. Dr. Maleki’s research spans Artificial Intelligence , Machine Learning , Biomedical Data Analysis , and Computer Vision . His work emphasizes medical applications, including tumor segmentation, clinical outcome prediction, and AI-driven diagnostics in oncology and cardiology. He also explores agricultural challenges, such as wheat head segmentation using generative models and domain adaptation. Key contributions include developing robust medical imaging tools (e.g., Rel-UNet for tumor segmentation) and frameworks for evaluating AI model reliability ( RIDGE ). His work bridges clinical needs with computational innovation, addressing issues like reproducibility, generalizability, and low-annotation learning across healthcare and agriculture domains. Dr. Maleki’s articles focus on advancing AI methods for precision health and agriculture. His recent work highlights interdisciplinary applications, such as integrating clinical and pathology data for cancer survival prediction, optimizing radiation therapy using Bayesian methods, and leveraging synthetic data for crop phenotyping. These studies emphasize practical deployment and ethical considerations in AI adoption.

Edriss S. Titiمشاهده پروفایل

استاد

• Nonlinear Partial Differential Equations
• Applied Mathematics
• Numerical Analysis
• +۵ مورد دیگر

Edriss S. Titi is a University Distinguished Professor and Arthur Owen Professor of Mathematics at Texas A&M University within the College of Arts & Sciences. His research focuses on nonlinear partial differential equations, applied mathematics, and geophysical fluid dynamics. He leads studies on fluid mechanics, atmospheric and oceanic dynamics, data assimilation, and control theory. His work often addresses mathematical rigor in modeling complex systems like climate dynamics and turbulent flows. Research Interests: Nonlinear PDEs and their applications Fluid dynamics and turbulence Data assimilation algorithms Climate and ocean modeling Infinite-dimensional dynamical systems Recent publications emphasize Navier-Stokes equations , primitive equations , and data assimilation in chaotic systems . His methodologies bridge theoretical analysis and computational modeling, with applications to weather prediction and geophysical flows. Collaborations include the Institute for Applied Mathematics and Computational Science (IAMCS) at Texas A&M. Notable contributions include rigorous analysis of global well-posedness for oceanic models and development of CDAnet, a physics-informed deep learning framework for fluid flow downscaling.