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Javid BayandorView profile

Associate Professor

• Space explorations and mission design
• collision dynamics and crashworthiness
• aerospace conceptual design
• +5 more

Javid Bayandor serves as Associate Professor in the Department of Mechanical and Aerospace Engineering at the University at Buffalo's School of Engineering and Applied Sciences. His research bridges theoretical aerospace engineering with practical applications in space exploration and bioinspired systems. Education: Postdoctoral Research in Aerospace Structures, The Royal Melbourne Institute of Technology (2000-2001) PhD in Aerospace Engineering, The Royal Melbourne Institute of Technology (2000) His research portfolio spans eight core areas: space mission design, collision dynamics, aerospace conceptualization, space physics, ballistics, bioinspired robotics, advanced structures, and fluid-structure interactions. This interdisciplinary approach integrates computational modeling with experimental validation to solve complex engineering problems across aerospace and biological domains. Current projects demonstrate particular strength in developing innovative solutions for extreme environments including space and underwater applications. Analysis of his 15 most recent publications (2023-2025) reveals three dominant research trajectories: (1) Space mission architectures for Mercury, Venus, and Mars featuring novel entry/landing systems; (2) Impact dynamics studies covering hypervelocity collisions, bird/drone strikes on urban air mobility vehicles, and spacecraft shielding; (3) Bioinspired flight mechanics focusing on bee and bat aerodynamics under environmental stressors. These threads consistently employ fluid-structure interaction methodologies across scales. Scientific Awards: No awards or honors were documented in the provided source material While specific student advising relationships aren't enumerated, his research scope suggests supervision of graduate students in computational mechanics, space systems, and bioinspired robotics. The publication record indicates sustained grant funding for hypervelocity impact studies, space mission design, and bioflight mechanics, though specific grant details aren't provided. His work likely connects with NASA mission development programs and urban air mobility safety initiatives. The research ecosystem appears centered around computational laboratories specializing in fluid-structure interaction modeling, with potential connections to experimental impact testing facilities and biological flight observation setups for bioinspired studies.

Hussein NassarView profile

Assistant Professor

• Elasticity
• Geometry
• Solid Mechanics
• +3 more

Hussein Nassar is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of Missouri, College of Engineering. He holds a PhD from Université Paris-Est, an MSE from École des Mines – ParisTech, and a BS from Sorbonne Université. His research focuses on connections between elasticity, geometry, and wave mechanics with applications in architected materials and metamaterials. Research interests span solid mechanics, lattice structures, origami-inspired designs, and wave propagation phenomena. His work combines theoretical modeling with computational approaches to develop novel material systems with tailored mechanical properties. Recent publications demonstrate consistent focus on metamaterials and wave mechanics, with emerging themes in origami engineering and computational tutorials. Articles frequently explore deformation mechanisms, transformation elasticity, and wave manipulation through geometric design. Awards: NSF CAREER Award

Aniket BhattacharyaView profile

Professor

• Theoretical Condensed Matter Physics
• Soft Matter Systems
• Polymer Physics
• +5 more

Aniket Bhattacharya is a Professor in the Department of Physics at the University of Central Florida (UCF), specializing in theoretical and computational studies of soft condensed matter physics and biophysics. His work spans DNA translocation through nanopores, intrinsically disordered proteins, and confined biopolymers, with applications in biomedical materials. PhD in Theoretical Condensed Matter Physics (University of Maryland, 1992) BS – Physics Honors (Presidency College, Kolkata) MS in Physics (University College of Science, Kolkata) Research interests: Scaling theory and simulations of soft matter systems Polymer physics in crowded environments Biologically inspired physics, particularly DNA transport through nanopores Physics of intrinsically disordered proteins linked to disease Development of soft materials for biomedical applications Recent publications focus on machine learning strategies for protein characterization, nanopore DNA barcoding, and polymer dynamics in confined spaces. His work has been supported by grants from the NSF, NASA, and NIH. He holds multiple patents related to nanopore-based DNA analysis and mutation prediction in disordered proteins. United States Patent 12,049,663B2 (2024) Non-Provisional Patent Application 19/232,158 (2025) His lab collaborates on interdisciplinary projects involving computational modeling, nonequilibrium transport phenomena, and biomedical applications.

• Geographical Criminology
• Urban Analysis
• Spatial Analysis and Statistics
• +5 more

Naru Shiode is an Associate Professor at the Department of Geography, Geology and the Environment, Kingston University, within the Faculty of Engineering, Computing and the Environment. A quantitative geographer and GIScientist, his work focuses on spatial-temporal modeling of crime patterns, urban risks, and data-driven geo-surveillance. PhD in Spatial Analysis, University College London MEng and BEng in Urban Engineering, University of Tokyo His research spans four domains: Geographical Criminology: Spatial collocation of crimes, hotspot displacement, seasonal crime resonance Urban Analysis: Linking vulnerable populations to risks like epidemics and riots Spatial Analysis and Modeling: Network-based statistics, geo-surveillance systems Data Science and Geo-visualization: 3D GIS models for urban and social data Collaborations include the London Mayor’s Office, Metropolitan Police, and Home Office. Key methodologies involve network-based scan statistics, recursive Voronoi diagrams, and spatial interpolation. Publications since 1999 highlight interdisciplinary work bridging GIS, urban planning, and public health.

Vasyl YurchyshynView profile

Research Professor

• Solar Physics
• Solar Flares
• Solar Magnetic Fields
• +3 more

Vasyl Yurchyshyn is a Research Professor at NJIT's Center for Solar Research, specializing in high-resolution solar observations and magnetic field dynamics. His research investigates solar flare mechanisms, magnetic reconnection, coronal mass ejections, and solar atmospheric phenomena using advanced imaging techniques. Research employs adaptive optics, high-cadence spectroscopy (FISS/GST, IRIS), and space-based observatories (SDO, SOHO) to study fine-scale solar structures. Key focus areas include: magnetic reconnection in chromospheric jets; evolution of flare ribbons; prominence formation; and magnetic field configurations preceding eruptions. Recent work develops machine learning methods for space weather prediction of coronal mass ejections and solar cycles. Publications demonstrate innovations in solar image processing, deep learning for magnetogram enhancement, and transformer models for CME forecasting. Research themes consistently integrate high-resolution observations with magnetohydrodynamic interpretations of solar activity. Long-term investigations track temporal evolution of network jets, active region magnetic fields, and flare-associated plasma sheets using multi-instrument datasets from major solar observatories.

Enrique DunnView profile

Associate Professor

• 3D Computer Vision
• Computer Vision
• Geometric Relationships
• +6 more

Enrique Dunn is an Associate Professor in the Department of Computer Science at Stevens Institute of Technology. He holds an academic position within the Charles V. Schaefer, Jr. School of Engineering and Science. His research focuses on 3D Computer Vision, emphasizing geometric and semantic relationships in imaged environments. Dunn earned a B.S. in Computer Engineering from the Autonomous University of Baja California (1999), an M.S. in Computer Science from the Ensenada Center for Scientific Research (2001), and a Ph.D. in Electronics and Telecommunications (2006). He held postdoctoral roles at UNC Chapel Hill (2008–2012) before joining Stevens in 2016. His research interests include 3D reconstruction, visual odometry, and large-scale visual analytics. Dunn has authored over 40 papers in top conferences/journals such as CVPR and ICCV. He serves as an Associate Editor for Elsevier's Image and Vision Computing journal and has held roles in program committees for ECCV and 3DV. Key contributions include VOLDOR-SLAM (2021), NeuroCS (2023), and methods leveraging dense optical flow residuals for visual odometry. His team comprises Ph.D. students Juan Carlos Dibene and Siyuan Cao, with alumni Zhixiang Min (Apple) and Xiangyu Xu (InnoPeak). Dunn's work addresses challenges in crowd-sourced imagery and dynamic scene modeling.

Mahyar MasoudiView profile

Assistant Professor

• Urban Ecology
• Geographic Information Science
• Environmental Justice
• +3 more

Mahyar Masoudi is Assistant Professor in the Department of Geography at Memorial University of Newfoundland, where he directs the Urban Ecology & Analytics Lab. He holds adjunct appointments at University of Waterloo's School of Planning and is affiliated with Nanyang Technological University's Resilient and Inclusive Cities Lab. His research examines how spatial patterns of urban elements influence environmental performance and human well-being. Education includes: PhD from National University of Singapore (2018), MSc from Universiti Teknologi Malaysia (2012), BSc from University of Guilan (2009), and postdoctoral work at Waterloo and NUS. Research integrates urban ecology , geographic information science , and environmental justice to study urban landscapes as social-ecological-technological systems. Key interests include urban heat mitigation, ecosystem services, and equitable access to environmental benefits using remote sensing, spatial statistics, and machine learning. Publications focus on urban thermal environments, ecosystem service modeling, and landscape pattern analysis. Recent work examines cooling efficiency of green spaces across Asian megacities and develops computational tools like the NCS2020 R package for ecosystem service assessment. No scientific awards documented. Leads the Urban Ecology & Analytics Lab developing geospatial methods to enhance urban sustainability and equity.

Erik Van VleckView profile

Professor

• Applied Mathematics
• Numerical Analysis
• Numerical Methods for PDEs
• +3 more

Erik Van Vleck is a Professor of Mathematics at the University of Kansas, affiliated with the College of Liberal Arts & Sciences. His research focuses on applied mathematics, numerical analysis, and numerical methods for partial differential equations (PDEs), with particular emphasis on data assimilation and reaction-diffusion systems. His work bridges theoretical advancements and practical applications in computational dynamics, environmental modeling, and ecological systems. Van Vleck’s research interests include multi-scale data assimilation techniques, adaptive mesh methods, and the numerical analysis of traveling wave solutions in discrete and continuous systems. He has contributed to understanding pattern formation in ecological models, particularly in savanna ecosystems and fire-driven dynamics. His methodological innovations span numerical methods for PDEs, reduced-order modeling, and stochastic filtering approaches for complex systems. His recent work highlights trends in developing robust adaptive frameworks for data assimilation, integrating advanced numerical techniques with real-world applications in geophysics and environmental science. This includes projects on metric tensor-based mesh adaptation and projected data assimilation using sliding window methods. His contributions to theoretical foundations include spectral stability analyses for discrete dynamical systems and bifurcation studies in reaction-diffusion equations. Van Vleck has collaborated extensively across disciplines, applying mathematical tools to ecological, climatological, and biomedical systems. His research often addresses computational challenges in high-dimensional spaces, emphasizing efficiency and accuracy in modeling complex phenomena. Despite the breadth of his contributions, no specific grants or advising roles are detailed in the provided materials.

Mats G LarsonView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Finite Element Methods
• +3 more

Mats G Larson is a Professor at the Department of Mathematics and Mathematical Statistics at Umeå University. He holds the research qualification of Docent and specializes in computational mathematics, numerical analysis, and finite element methods. His work focuses on advancing numerical techniques for partial differential equations, including CutFEM, Isogeometric Analysis (IGA), and hybridized methods for complex geometries and multiphysics problems. Research interests include error estimation, stabilized finite element methods, computational mechanics, and applications in engineering and fluid-structure interaction. Larson leads projects such as the 2022–2025 'Multi-shell CutFEM for problems with mixed dimensions' and the 2017–2021 project on computational methods for elliptic problems. His publications appear in top journals like Computer Methods in Applied Mechanics and Engineering . Key contributions include developments in CutFEM for embedded surfaces, augmented Lagrangian methods for contact problems, and geometric modeling with CAD integration. His research bridges theoretical analysis and practical applications, addressing challenges in mesh generation, stability, and high-performance computing.

Hristo ChipilskiView profile

Assistant Professor

• Data Assimilation
• Artificial Intelligence
• Numerical Weather Prediction
• +2 more

Hristo Chipilski is an Assistant Professor in the Department of Scientific Computing at Florida State University. His research focuses on data assimilation, artificial intelligence, numerical weather prediction, and atmospheric dynamics, combining theoretical, computational, and applied approaches. Education: 2021-2023 ASP Postdoctoral Fellow (NCAR), 2016-2021 PhD in Meteorology (University of Oklahoma), 2012-2016 MMet in Meteorology and Climate (University of Reading, UK) Research Interests: Data assimilation (optimal combination of models and observations) drives his work, particularly for atmospheric systems. He explores AI integration with traditional assimilation methods, targeting convective-scale weather prediction and real-time high-performance computing applications. His studies analyze bore-generating convection, cloud microphysics, and multiscale dynamics. Article Trends: Recent publications emphasize nonlinear ensemble filtering techniques, diffusion models, and generative AI for data assimilation in geophysical systems. Topics span surface quasi-geostrophic dynamics, partial observation handling, and real-time numerical weather prediction (NWP) frameworks optimized for high-performance computing.

Roland KaysView profile

Research Professor

• Mammal Ecology
• Conservation Biology
• Animal Movement Ecology
• +5 more

Roland Kays is Research Professor in the Department of Forestry and Environmental Resources at North Carolina State University and Director of the Biodiversity & Earth Observation Lab at the North Carolina Museum of Natural Sciences. His research investigates mammal ecology and conservation using advanced tracking technologies. Education includes: Ph.D., Zoology, University of Tennessee, 1999 B.S., Biology, Cornell University, 1993 Research combines camera trapping, GPS tracking, and community science to study animal movement, species distributions, and human-wildlife interactions. Current projects examine range expansion dynamics, impacts of human recreation on wildlife, and conservation of carnivores in human-dominated landscapes. Recent publications (2022-2025) reflect leadership in large-scale biodiversity monitoring (Snapshot USA), movement ecology databases (Movebank, MoveTraits), and conservation applications of wildlife tracking. Studies frequently integrate continental-scale data from collaborative networks. Directs global initiatives including the Movebank animal tracking database and eMammal camera trapping platform. Teaches mammalogy and mentors students in field ecology methods.

Eric LonsdorfView profile

Assistant Professor

• Ecosystem Services
• Sustainable Agriculture
• Urban Ecology
• +2 more

Dr. Eric Lonsdorf is an Assistant Professor at Emory University and Senior Fellow at the Natural Capital Project. He holds a BA in Biology from Carleton College (1996) and PhD in Ecology and Evolution from University of Minnesota (2004). His applied research integrates ecosystem service valuation into land management decisions using quantitative methods spanning agricultural and urban systems. Research focuses on sustainable agriculture practices, urban green infrastructure planning, pollinator conservation, and equitable nature-based solutions. Recent publications examine socioeconomic dimensions of urban greenspaces, pollinator habitat in renewable energy facilities, and pesticide risk modeling. Key research trends include spatial analysis of environmental justice in urban planning, multi-functional landscape design for biodiversity conservation, and policy-relevant models bridging ecological and economic systems across geographic scales. Articles demonstrate consistent focus on practical applications for conservation decision-making.

Jay FamigliettiView profile

Professor

• Hydrology
• Climate Change
• Remote Sensing
• +3 more

Jay Famiglietti is a Global Futures Professor at Arizona State University’s School of Sustainability, serving as Director of Science for the Arizona Water Innovation Initiative. He holds affiliated roles in the School of Sustainable Engineering and the Built Environment and the Swette Center for Sustainable Food Systems. Previously, he was a Professor Emeritus and Executive Director of the Global Institute for Water Security at the University of Saskatchewan, where he held the Canada 150 Research Chair in Hydrology and Remote Sensing. His career includes leadership roles at NASA’s Jet Propulsion Laboratory and founding Waterplan, a water tech startup. Education: B.S. in Geology (Tufts University); M.S. in Hydrology (University of Arizona); M.A./Ph.D. in Civil Engineering (Princeton University); Postdoctoral research at Princeton and the National Center for Atmospheric Research. Research focuses on satellite-based tracking of global water availability, groundwater depletion via GRACE mission data, and integrating hydrology with climate models. Key interests include groundwater sustainability, the food-water nexus, corporate water stewardship, and water diplomacy. He has pioneered methods for detecting groundwater changes from space and improving hydrological predictions in IPCC models. Publications highlight advancements in GRACE applications, groundwater sustainability, and climate-water interactions. His work has been featured in major media outlets like 60 Minutes and Nature . Awards include the AGU’s Peter S. Eagleson Hydrologic Sciences Award and Fellowships from AGU and the Geological Society of America. He advises governments and global leaders on water policy, hosts the podcast What About Water? , and collaborates internationally through roles like Chair of the Bolin Centre’s External Science Advisory Group. His research group includes postdocs, PhD candidates, and scientists addressing water security challenges globally.

Laura BalzanoView profile

Associate Professor

• Machine Learning
• Optimization
• Signal Processing
• +7 more

Laura Balzano is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, with a courtesy appointment in the Department of Statistics within the College of Literature, Science, and the Arts. Her research focuses on optimization, machine learning, and signal processing, particularly in high-dimensional data analysis, subspace learning, and low-rank structures. She holds affiliations with the Department of Statistics and is located at 323 West Hall, Ann Arbor, MI. Contact information includes girasole@umich.edu . Her work bridges theoretical foundations and practical applications, addressing challenges in heterogeneous data, online learning, and robust algorithms. Key research areas include subspace clustering, tensor decomposition, and model compression for deep learning systems. Recent contributions explore dynamic networks, control theory integration, and energy-efficient algorithms for large-scale models. Laura Balzano's publications span topics like PCA variants for noisy data, optimization on Riemannian manifolds, and neural collapse analysis. Her methods emphasize scalable solutions for real-time applications, such as energy disaggregation and medical imaging. While no explicit awards are listed, her extensive publication record reflects recognition in her field. Advising and grant details are not provided in the available text.

Yang ChenView profile

Assistant Professor

• Statistical Methodology
• Computational Algorithms
• Bayesian Modeling
• +7 more

Yang Chen is an Assistant Professor of Statistics and Research Assistant Professor at the Michigan Institute for Data Science (MIDAS), University of Michigan. Her research focuses on statistical methodology and computational algorithms for scientific applications, including Bayesian modeling, hidden Markov models, and applications in astronomy, space science, and epidemiology. She holds a Ph.D. in Statistics from Harvard University and a B.Sc. in Mathematics from the University of Science and Technology of China. Education: Ph.D. in Statistics, Harvard University, 2017 B.Sc. in Mathematics, University of Science and Technology of China, 2011 Research Interests: Her work emphasizes computational algorithms for statistical inference, with applications in astronomy (e.g., solar flare prediction, instrument calibration) and epidemiology. Key areas include spatial-temporal data analysis, uncertainty quantification, and machine learning for large-scale imaging data. Grants & Funding: Principal Investigator/Co-Investigator on multiple NSF, NASA, and industry grants totaling over $13M, including space weather modeling, solar physics, and data science initiatives. Teaching: Teaches advanced courses in Bayesian data analysis, statistical modeling, and computational methods at the University of Michigan. Professional Activities: Associate Editor for The American Statistician , Statistical Science , and Journal of the American Statistical Association . Active in organizing conferences and seminars on astrostatistics, machine learning, and space weather.