جستجوی استاد راهنمای دانشگاهی

Kyle T Spikesمشاهده پروفایل

دانشیار

• Seismic Reservoir Characterization
• Rock Physics Modeling
• Geophysical Inversion Techniques
• +۴ مورد دیگر

Kyle T Spikes is an Associate Professor in the Department of Earth and Planetary Sciences at the Jackson School of Geosciences, The University of Texas at Austin. His research focuses on integrating geologic data with quantitative tools for seismic reservoir characterization, emphasizing forward and inverse problems in rock physics, stochastic modeling, and seismic inversion. He works across scales from sub-micron rock samples to surface seismic data, developing effective medium models and numerical techniques to estimate heterogeneous and anisotropic rock properties. His work spans applications in carbonates, shales, and fractured reservoirs, with a strong emphasis on Bayesian methods, stochastic inversion, and machine learning for data analysis. Recent studies include fluid flow effects in porous media, distributed acoustic sensing (DAS), and rock physics modeling of unconventional reservoirs like the Haynesville Shale. He has contributed to CO2 sequestration monitoring through inversion of 3D VSP data at the Cranfield site. Notable trends in his publications include advancements in Bayesian-based rock physics modeling, integration of multi-scale data (laboratory to field scale), and innovative applications of DAS technology for seismic monitoring. His research bridges geophysics, petrophysics, and reservoir engineering, addressing challenges in reservoir characterization and monitoring under varying fluid and stress conditions. Dr. Spikes advises postdoctoral researchers and graduate students in the Jackson School, though specific advisee names are not listed. His work is supported by collaborative projects with industry and academic partners, focusing on practical solutions for subsurface reservoir challenges.

Ngoc Cuong Nguyenمشاهده پروفایل

پژوهشگر

• Computational Mechanics
• Molecular Mechanics
• Nanophotonics
• +۷ مورد دیگر

Ngoc Cuong Nguyen is a Principal Research Scientist in the Department of Aeronautics and Astronautics at MIT and a member of the MIT Center for Computational Engineering. His research focuses on computational mechanics, numerical simulation, and advanced numerical methods such as hybridizable discontinuous Galerkin (HDG) methods for multi-scale and multi-physics problems. Education: PhD in High Performance Computation for Engineered Systems (2005), National University of Singapore BEng in Aeronautical Engineering (2001), Ho Chi Minh City University of Technology Research Interests: Computational Mechanics, Molecular Mechanics, Nanophotonics Numerical Simulation & Optimization, Scientific Computing, Machine Learning Reduced Basis Methods, High-Order Methods (e.g., HDG), Uncertainty Quantification Key Projects: Development of HDG methods for fluid dynamics, structural mechanics, and electromagnetics Plasmonic nanostructure simulations using quantum hydrodynamic models Space weather modeling via GPU-accelerated HDG approaches Optimization of photonic crystals and nanostructured materials Large-eddy simulation (LES) of hypersonic flows and buffet phenomena Labs & Teams: Active contributor to the MIT Center for Computational Engineering, leading projects in numerical methods, computational fluid dynamics, and interdisciplinary applications of advanced simulation techniques.

Maria Christina Marianiمشاهده پروفایل

استاد

• Applied Mathematics
• Nonlinear partial differential equations
• Stochastic differential equations
• +۵ مورد دیگر

Maria Christina Mariani is a Professor and Department Chair in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP). Her interdisciplinary research bridges mathematics with applications in public health, geophysics, physics, and finance, with a focus on developing novel mathematical models for complex data analysis. Dr. Mariani earned her Ph.D. in Mathematics from the University of Buenos Aires in 1992, where she received an Outstanding dissertation award. She also holds an M.S. in Physics (1996) and an M.S. in Mathematics (1987), both from the University of Buenos Aires with highest honors. Her research interests span Applied Mathematics, Nonlinear partial differential equations, Stochastic differential equations, Machine Learning techniques, Mathematical Finance, Mathematical Physics, and Numerical Methods. She has developed mathematical models for medical data analysis (particularly breast cancer, heart disease, and prostate cancer), seismic and explosive data, and financial markets. Her work emphasizes the development of mathematical models to enhance understanding of medical data and extreme events in various phenomena. Dr. Mariani's recent research focuses on applying machine learning and stochastic models to complex data sets across multiple domains. Her work demonstrates consistent innovation in developing novel algorithms for medical diagnosis and prognosis, analyzing seismic data, and modeling financial markets using Levy processes, Ornstein-Uhlenbeck models, and wavelet techniques. She has mentored numerous students throughout her career, including PhD candidates, MS students, and post-doctoral researchers, demonstrating her commitment to academic development and knowledge transfer. Dr. Mariani has served as Department Chair and holds the Shigeko K. Chan Distinguished Professor title in Mathematical Sciences, reflecting her significant contributions to the field and institution.

Wenchao Geمشاهده پروفایل

استادیار

• Quantum Information Science
• Quantum Optics
• Quantum Metrology
• +۳ مورد دیگر

Wenchao Ge is an Assistant Professor in the Department of Physics at the University of Rhode Island . Previously, he held faculty positions at Southern Illinois University (2020–2022) and was a postdoctoral researcher at institutions including Texas A&M University and the U.S. Army Research Lab. He earned his Ph.D. in Physics from Texas A&M University (2015) and a B.S. in Applied Physics from Xi’an Jiaotong University (2009). His research focuses on theoretical quantum optics and quantum information science , with emphasis on quantum sensing, quantum computing, and quantum communication. His group explores fundamental limits of quantum mechanics for information processing, including protocols for multi-parameter sensing, parametric amplification in trapped-ion systems, and quantum resource theories. He leads the Ge Quantum Theory Group , which collaborates with experimentalists to advance quantum technologies. Research interests: Quantum metrology, quantum computing, trapped-ion systems, and optomechanical systems. Notable achievements: Developed protocols for Heisenberg-limited quantum sensing and quantum-enhanced entangling gates. Published in Nature Physics , Phys. Rev. Lett. , and Phys. Rev. A . Teaching: Courses on quantum entanglement and quantum information science. He has received grants from NSF (ExpandQISE and quantum sensing programs) and the SIU Open Textbook Initiative. He was elected Secretary/Treasurer of the APS New England Section (2023) and actively participates in international conferences and quantum education initiatives. His group advises graduate and undergraduate students on projects spanning theoretical and applied quantum physics, including summer internships and capstone research.

Fletcher Hallidayمشاهده پروفایل

استادیار

• Disease Ecology
• Plant Pathology
• Biodiversity
• +۴ مورد دیگر

Fletcher Halliday is an Assistant Professor at the Department of Botany and Plant Pathology at Oregon State University, where he leads the Disease Ecology and Diversity Lab . His research focuses on understanding the interplay between host-pathogen communities, climate change, and land-use impacts on disease risk. Primary affiliation: Oregon State University Research lab: Disease Ecology and Diversity Lab Dr. Halliday's work explores how biodiversity moderates disease dynamics, with studies on fungal epidemics, coinfection interactions, and trait-based susceptibility in plant systems. His research spans elevational gradients, successional ecosystems, and climate manipulation experiments to uncover patterns in pathogen distribution and community assembly. Recent publications highlight trends in biodiversity-disease relationships, climate change effects on herbivory and infection, and multiscale ecological interactions. Articles from 2020–2025 emphasize meta-analytic frameworks, priority effects in parasite assembly, and the role of functional traits in disease risk prediction. The Disease Ecology and Diversity Lab investigates disease risk in complex ecological systems, prioritizing integrative approaches to pathogen community structure and climate-driven changes in host-parasite interactions.

Arturo S. Leonمشاهده پروفایل

دانشیار

• Water Resources Engineering
• Reservoir Systems Optimization
• Multi-phase Flows
• +۵ مورد دیگر

Arturo S. Leon is an Associate Professor at the Department of Civil and Environmental Engineering, Florida International University (FIU). His work focuses on water resources engineering, reservoir optimization, multi-phase flows, and resilient flood control. He leads a research group developing innovative solutions for urban infrastructure challenges, including real-time control systems for reservoirs and stormwater management. Education: Ph.D., University of Illinois at Urbana-Champaign, 2007. Research Interests: Dr. Leon’s group pioneers computational models for hydraulic systems, such as the Illinois Transient Model. They investigate geysers in storm sewers via experimental and numerical methods, achieving landmark results with lab-replicated 30m geysers. Their work also includes IoT-enabled remote control of wetland storage systems for flood mitigation, as seen in the Automated and Remotely Controlled 6″ Siphon project. They integrate machine learning for flood forecasting and sewer overflow optimization, highlighted in frameworks like FIDLAR and EcoFLow. Awards: EPA Early CAREER Award Diplomate by the American Academy of Water Resources Engineers (D.WRE) Advancing Flood Resilience: His group’s dynamic storage management approach retrofits wetlands and detention ponds with IoT-controlled systems. They also study climate-driven urban hydrology, such as thermal comfort in tropical cities like Miami. Their patented technologies, including remote siphon control frameworks, enhance infrastructure adaptability during extreme weather events. Labs & Projects: Active in the Resilient MAST@FIU initiative and the EcoFlow project, focusing on green infrastructure for sustainable water use. Ongoing work includes AI-driven flood hazard mapping and exergo-economic optimization of energy systems.

Irina Paciمشاهده پروفایل

استاد

• Self-assembly on solid surfaces
• Monte Carlo methods
• Multi-scale computational methods
• +۳ مورد دیگر

Dr. Irina Paci is a Professor of Chemistry at the University of Victoria, specializing in theoretical and computational chemistry. Her research focuses on understanding molecular self-assembly processes, particularly at solid surfaces, and developing multi-scale computational methods to model materials' dynamic behavior under external fields. She holds a PhD from Queen's University and completed postdoctoral research at Queen's and Northwestern University. Her work integrates Monte Carlo simulations, density functional theory (DFT), and quantum chemistry to study surface adsorption mechanisms, catalytic reactions, and nanocomposite dielectric properties. Key areas include cysteine adsorption on gold surfaces, palladium-catalyzed cross-coupling reactions, and the impact of solvent effects on reaction pathways. Recent studies explore aluminum oxide degradation in perovskite solar cells and quantitative reactivity models for nucleophilic aromatic substitution. Teaching interests span physical chemistry, quantum mechanics, and computational methods. Dr. Paci leads the Paci Research Group, which includes graduate students like Archita Adluri and Natalie Stubb, focusing on advancing computational methodologies for energy materials and surface science. Her lab's work bridges fundamental theory with applications in nanotechnology and sustainable energy systems.

Kevin Schneiderمشاهده پروفایل

استاد

• Software Architecture and Design
• Software Evolution and Maintenance
• Software Analysis and Navigation
• +۶ مورد دیگر

Dr. Kevin Schneider is a Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on software architecture, evolution, analysis, and visualization, with notable work in quantum computing applications and machine learning. He also explores collaborative software teams and domain-specific languages to enhance development processes. Education: Ph.D., Computing and Information Science, Queen’s University (2000) Research Associate, Computing and Information Science, Queen’s University (1991–94) M.Sc., Computing and Information Science, Queen’s University (1990) B.Sc.(Hon), Computational Science, University of Saskatchewan (1980) Research interests include software design principles, maintenance strategies, and the integration of AI/quantum computing into software engineering. He emphasizes reproducibility in scientific workflows and tools like VizSciFlow. His work often bridges theory and practice, addressing challenges in code clone stability, user feedback management, and healthcare-related machine learning frameworks. Scientific Awards: Most Influential Paper at SCAM 2001 for his work on software engineering via source transformation. Advising and grants: While no specific advisees are listed, his research involves large-scale projects such as the Nutrient App and automated polyp segmentation tools. He collaborates on grants related to quantum computing applications and cloud-based software systems. Labs and teams: His work centers on collaborative scientific data analysis groups and developing tools for real-time groupware systems in complex workflows. He contributes to projects like CloneCognition and FSECAM, aiming to improve software design and maintenance through advanced analytics.

Umberto Desideriمشاهده پروفایل

استاد

• Renewable Energy Systems
• Thermal Engineering
• Hydrogen Technology
• +۱۵ مورد دیگر

Umberto Desideri is a Full Professor of Thermal Machines at the University of Pisa since November 2014, following his tenure as Full Professor at the University of Perugia from 2004 to 2014. He has held leadership roles, including Head of the Department of Engineering at the University of Perugia (2014) and Head of the Department of Energy, Systems, Territory and Construction Engineering at the University of Pisa (2016–2020). He is actively involved in editorial boards, such as the ASME Journal of Fuel Cell Science and Technology (2005–2017), Journal Applied Energy (since 2007, Associate Editor from 2011, Senior Editor since 2022), and Journal Applied Thermal Engineering (since 2013). Additionally, he co-founded two spin-off companies in 2005: Tecnologie per la riduzione delle emissioni engineering srl and Biomasse e nuove tecnologie srl . Desideri obtained his MSc in Mechanical Engineering from the University of Florence in 1988 and his PhD in Energy Engineering from the same university in 1993. His academic career includes roles as Assistant and Associate Professor at the University of Perugia (1992–2004) before becoming a Full Professor there in 2004. His research focuses on renewable energy systems (geothermal, solar, biomass), hydrogen and fuel cell technologies, carbon capture and storage, and energy-efficient buildings. He explores polygeneration systems, sustainable energy storage, and decarbonization strategies for industries like tissue paper production. Recent work emphasizes the integration of renewables with thermal storage and CO2 utilization in circular energy systems. Awards: Fellow of the American Society of Mechanical Engineers (since 1994) Listed in the Top Italian Scientists in Engineering Recognized as a World Top 2% Cited Scientist In advising and grants, he has supervised over 200 Master’s theses and led EU-funded projects such as: H2020 COMPBAT (2020–2024): Computer-aided design for next-gen flow batteries H2020 Regen-by-2 (2020–2027): Multi-energy systems using two-phase fluids LIFE Augia (2020–2023): Sewage oxy-gasification for chemicals production He directed the Fuel Cell Laboratory (2007–2014) and Wind Tunnel (2010–2014) at the University of Perugia. His current work at the University of Pisa’s DESTEC department addresses hydrogen technologies, energy transition, and sustainable systems.

Burhaneddin İzgiمشاهده پروفایل

دانشیار

• Game Theory
• Stochastic Differential Equations
• Numerical Analysis
• +۳ مورد دیگر

Burhaneddin İzgi serves as an Associate Professor in the Department of Mathematics Engineering at Istanbul Technical University, where he maintains an active research profile with projects extending through 2025. His work bridges theoretical mathematics with computational applications, particularly in game-theoretic problem solving. Research interests center on Game Theory and Stochastic Differential Equations, with specialized focus on matrix norm-based solution methods for zero-sum, fuzzy, and stochastic matrix games. Recent work integrates Artificial Intelligence techniques to address large-scale game complexity, reflecting interdisciplinary innovation across Numerical Analysis and Fuzzy Mathematics. Article trends (2022-2025) reveal consistent advancement of matrix norm methodologies across diverse game types, increasingly incorporating machine learning for computational efficiency. Publications demonstrate strong theoretical grounding in Mathematics while addressing practical applications in finance (e.g., IPO modeling) and decision systems. Scientific recognition includes: 2210 - Yurt İçi Yüksek Lisans Burs Programı (2008) 2211 - Yurt İçi Doktora Burs Programı (2010) Matematik Bölüm Birinciliği (2008) Üniversite İkinciliği ödülü (2008) Research funding includes TUBITAK-supported projects such as 'Stokastik Oyunlar İçin Matris Norm Tabanli Yeni Çözüm Yöntemleri Ve Yapay Zeka Uygulamalari' (2021-2023) and current grants developing chaos theory approaches for stochastic matrix games (2024-2025), demonstrating sustained external validation of his research program.

• Remote Sensing
• Geographic Information Systems (GIS)
• Environmental Monitoring
• +۷ مورد دیگر

Giorgos Mountrakis is a Professor in the Department of Environmental Resources Engineering at SUNY College of Environmental Science and Forestry (ESF). His research focuses on environmental monitoring using remote sensing, environmental modeling through geographic methods, and decision support systems for ecological and urban challenges. He holds a Dipl. Eng. from the National Technical University of Athens (1998), an M.S. (2000), and Ph.D. (2004) from the University of Maine. His work integrates advanced technologies like satellite imagery, LiDAR, and machine learning to address land cover dynamics, climate impacts, and wildlife conservation. Current advisees include Atef Amriche (PhD candidate in Geospatial Information Science), Babak Haji Seyed asadollah (PhD in Environmental Resources Engineering), Ahmadreza Safaeinia (PhD in Environmental Resources Engineering), and Zhixin Wang (PhD in Geospatial Information Science). Key research themes include: land use/cover classification using deep neural networks, climate change impacts on forests and rangelands, and optimizing spatial-temporal models for large-scale environmental analysis. His projects span global datasets (e.g., Landsat, MODIS) and regional case studies in the US, Mongolia, and Algeria. Publications emphasize methodological advancements in remote sensing, such as fusion of multisensor data, accuracy assessment frameworks, and applications in biodiversity conservation. His work bridges technical innovation with practical environmental decision-making, addressing issues like urban growth prediction and wildlife-vehicle collision mitigation.

Alexander Lanzonمشاهده پروفایل

استاد

• Robust Control
• Nonlinear Systems
• Multi-Agent Systems
• +۲ مورد دیگر

Prof. Alexander Lanzon is Chair of Control Engineering at the University of Manchester, where he directs the Control, Dynamics and Robotics Laboratory and leads research in the Department of Electrical and Electronic Engineering. Research Focus: Robust/nonlinear control via passivity and dissipativity methods Negative imaginary systems theory for precision motion control Multi-agent coordination for UAV swarms and robotic networks His recent publications demonstrate theoretical advances in stability analysis frameworks and applications to energy storage optimization and aerial swarm control. This reflects dual emphasis on fundamental theory and engineering implementations. Leadership Roles: Deputy Head of Research for School of Engineering Associate Editor for IEEE Control Systems Letters Fellow of IET, IMA, and InstMC Current Projects: Micro-scale Co-generation Compressed Air Energy Storage Centre for Robotics and Artificial Intelligence Distributed control for multi-tilt tricopter systems

Timothy Verstynenمشاهده پروفایل

استاد

• Cognitive Neuroscience
• Cognitive Science
• Computational Neuroscience
• +۱ مورد دیگر

Timothy Verstynen is a Professor of Psychology and Neuroscience Institute at Carnegie Mellon University, affiliated with the Dietrich College of Humanities and Social Sciences. His research focuses on cognitive neuroscience, cognitive science, computational modeling, and learning science. He investigates how neural pathways regulate action planning, skill learning dynamics, and structure-function relationships in the brain. His work integrates psychophysics, computational models, and neuroimaging techniques (fMRI, TMS, diffusion imaging). Key research themes include: 1) Action selection and stopping mechanisms under sensory input; 2) Neurobiological bases of skill acquisition timelines; 3) White matter architecture's role in cognitive functions. Recent studies explore links between brain structure, cardiovascular health, and decision-making processes using multimodal imaging and machine learning. Notable publications address cortico-basal ganglia-thalamic circuits' role in decision policies, stress-brain connectivity, and reward-based learning. His CoAx Lab develops tools like CBGTPy for modeling decision-making systems. Research spans neuroimaging methodological advancements (e.g., local connectome fingerprinting) and translational health neuroscience projects.

Michael C. Gurnisمشاهده پروفایل

استاد

• Computational Geodynamics
• Mantle Dynamics
• Plate Tectonics
• +۱۲ مورد دیگر

Michael C. Gurnis is the John E. and Hazel S. Smits Professor of Geophysics at the California Institute of Technology and has served as Director of the Seismological Laboratory (2009-2024) and Schmidt Academy for Software Engineering (2019-). With a B.S. from the University of Arizona (1982) and a Ph.D. from Australian National University (1987), he has held faculty positions at Caltech since 1994, becoming full Professor in 1996 and Smits Professor in 2005. His research spans computational geodynamics, mantle convection, and plate tectonics. California Institute of Technology Division of Geological and Planetary Sciences Seismological Laboratory Schmidt Academy for Software Engineering Research Interests: Gurnis focuses on computational geodynamics to link mantle convection to surface tectonics, using forward/inverse models to study plate motion dynamics , subduction zone mechanics , and deep mantle structures . His work bridges present-day geophysics with historical models through 4D Earth modeling , integrating seismic tomography, mineral physics, and paleogeography via the GPlates consortium. Recent projects include dynamic emergence of continents , plume interactions , and impact-driven subduction . Publications: Gurnis' research spans high-resolution global models of mantle flow, subduction zone rheology, and cratonic basin dynamics. Key trends include multi-physics coupling , Bayesian inversion techniques , and planetary-scale tectonic drivers . His 2024 papers address nonlinear mantle viscosity , shear zone weakening , and impact origins of tectonics . Scientific Awards: Gordon Bell Prize Finalist (2008, 2010) Springer CSE Prize (2011) for plate tectonics simulation work Advising: Mentors graduate students in computational geodynamics, including Jiaqi Fang, Erin Hightower, Yida Li, and Ojashvi Rautela. Collaborates with postdocs and international institutions like ETH-Zurich and National University of Mexico on mantle wedge dynamics and seismic geography. Labs & Teams: Leads the Seismological Laboratory and co-founded the Schmidt Academy for Software Engineering , driving innovations in geodynamic simulations and computational tools.

Athanasios Angeloudisمشاهده پروفایل

دانشیار

• Environmental Fluid Mechanics
• Offshore Renewable Energy
• Coastal Processes
• +۴ مورد دیگر

Dr Athanasios Angeloudis is a Reader (Associate Professor) and Director of Impact in the School of Engineering at the University of Edinburgh. He is affiliated with the Institute for Infrastructure and the Environment, leading research on hydro-environmental applications and offshore renewable energy systems. His academic roles include coordinating the Edinburgh Fluid Dynamics Group and serving as a member of NERC’s Peer Review College. Dr Angeloudis holds a PhD in Hydro-environmental Engineering (2014) and an MEng in Civil Engineering (2010) from Cardiff University. He is Chartered with the Technical Chamber of Greece (CEng) and a member of the Institution of Civil Engineers. His research focuses on environmental fluid mechanics, coastal processes, numerical methods, and data analytics. His teaching responsibilities include courses like Civil Engineering Hydraulics and Water Engineering Transport and Treatment . He has secured significant research funding, including the NERC Industrial Innovation Fellowship, and leads projects such as CoTide (tidal stream energy optimization) and ILIAD (maritime data services). Key research contributions span tidal energy systems, climate change impacts on river flows, and computational modeling frameworks like Thetis-SWAN . His work emphasizes collaboration with industry and international partners to advance sustainable energy solutions and environmental resilience.