جستجوی استاد راهنمای دانشگاهی

Brian Woodمشاهده پروفایل

استاد

• Transport Phenomena
• Multiscale Analysis
• Environmental Engineering
• +۳ مورد دیگر

Brian Wood is a Professor in the Department of Chemical, Biological, and Environmental Engineering at Oregon State University (OSU), part of the College of Engineering. His research focuses on multiscale systems analysis, particularly in transport phenomena, tissue modeling, and environmental engineering applications. He holds a Ph.D. in Civil and Environmental Engineering from the University of California, Davis (1999), an M.S. in Environmental Engineering from Washington State University (1990), and a B.S. in Civil Engineering from Washington State University (1988). Wood’s research interests span transport of mass, momentum, and energy in natural and engineered systems, including multiscale tissue analysis and cancer biology modeling. His work integrates computational methods with experimental data to address challenges in porous media dynamics, biofilm growth, and environmental remediation. Notable contributions include studies on non-equilibrium transport, chemotaxis in granular media, and biofilm architecture visualization using synchrotron-based X-ray tomography. His recent publications (2020–2025) emphasize advancements in upscaling techniques for porous media flow, machine learning applications in biomedical modeling, and regulatory frameworks for end-user control systems. He leads the Computation and Simulation research group at OSU, fostering interdisciplinary approaches to multiscale hydrogeologic modeling. Wood’s research has been supported by collaborative grants focusing on bacterial transport in heterogeneous environments and hybrid multiscale methods. His work bridges theoretical developments in continuum mechanics with practical applications in environmental engineering and biomedical systems.

Eric Keavenyمشاهده پروفایل

استاد

• Applied Mathematics
• Fluid Dynamics
• Biophysics
• +۴ مورد دیگر

Dr. Eric Keaveny is a Reader in Applied Mathematics at the Department of Mathematics, Imperial College London. He is affiliated with multiple research groups including Applied Mathematics and Mathematical Physics, Biomathematics Group, Fluid Dynamics, and Mathematics in Medicine. His work focuses on computational modeling of fluid-structure interactions, particularly in biological systems such as microorganism locomotion and cilia-driven transport. He holds an EPSRC Standard Grant and an Imperial European Partners Fund Grant, and has received the Faculty of Natural Sciences Excellence in Teaching Award (2017). His research explores topics like coordinated motion of active filaments, stochastic suspensions, and hydrodynamic interactions in complex fluids. Education: Ph.D. in Applied Mathematics (Brown University, 2008), ScM Applied Mathematics (Brown University, 2006), B.S. Applied Physics (Columbia University, 2001). Research interests include microorganism locomotion, suspensions of interacting particles, low Reynolds number hydrodynamics, and numerical methods for fluid dynamics. His recent work involves simulating ciliary transport in lungs and developing algorithms for large-scale particle simulations. Grants/Awards: EPSRC Standard Grant (2017-2021), Imperial European Partners Fund Grant (2017-2019), Fulbright Fellowship (2012). He has supervised multiple interdisciplinary collaborations, including a joint Imperial-TUM doctoral program. Teaching: Currently lectures on Computational Dynamical Systems (MATH60023/70023). Previous courses include M3/4/5N9 (Autumn 2016).

• Stochastic Control
• Financial Mathematics
• Optimization
• +۶ مورد دیگر

Professor Harry Zheng is a Professor of Mathematics at Imperial College London's Faculty of Natural Sciences, Department of Mathematics. He is affiliated with the CFM-Imperial Institute of Quantitative Finance and Mathematical Finance. His research focuses on stochastic control, optimization, and financial mathematics with applications in banking, numerical methods, and statistics. Key research areas include stochastic control theory, mean field games, financial derivative pricing, and machine learning integration in financial systems. His work addresses optimal investment strategies, risk management, and systemic risk modeling in dynamic markets. Notable contributions include applying deep learning to solve high-dimensional stochastic control problems and analyzing governance dynamics using mean field frameworks. Recent publications span innovations in neural network-based solvers for Hamilton-Jacobi-Bellman equations, robust optimization under uncertainty, and behavioral finance models incorporating transaction costs and prospect theory. Professor Zheng collaborates with institutions like the CFM-Imperial Institute to advance quantitative finance methodologies. His research bridges theoretical advancements with practical applications in portfolio management, risk assessment, and computational finance.

Yunus Emre Harmanciمشاهده پروفایل

پژوهشگر

• Structural Strengthening of Reinforced Concrete
• Structural Health Monitoring (SHM)
• Carbon Fiber Reinforced Polymers (CFRP)
• +۳ مورد دیگر

Yunus Emre Harmanci is a Scientist at the Swiss Federal Laboratories for Materials Science and Technology (Empa) within the Department of Structural Engineering. He holds a B.Sc. from Middle East Technical University (METU) in Ankara, Turkey (2010) and an M.Sc. from ETH Zurich (2013). His research focuses on structural strengthening of reinforced concrete (RC) using carbon fiber-reinforced polymers (CFRP), structural health monitoring (SHM), and advanced sensor technologies like Digital Image Correlation (DIC) and fiber-optic sensors. He has conducted experimental and theoretical studies on the long-term performance of gradient anchorages for prestressed CFRP strips, freeze-thaw effects on anchorage systems, and the use of iron-based shape memory alloys (Fe-SMA) for structural retrofitting. His work includes collaborative projects with ETH Zurich and Empa, such as the SNSF-funded study on gradient anchorages and environmental exposure testing of Fe-SMA-reinforced beams. He has also contributed to structural monitoring of complex structures like the Zurich Zoo’s Elephant Park and the Gotthard Street Tunnel. His research emphasizes data-driven approaches for infrastructure lifecycle analysis and non-invasive diagnostic techniques. Harmanci’s publications span topics from finite element modeling of bond behavior to autonomous strain-based monitoring frameworks. His expertise includes structural dynamics, nonlinear analysis, and computational mechanics, with applications in both academic and industrial settings.

• Materials Engineering
• Polymer Science
• High Strain Rate Deformation
• +۳ مورد دیگر

Professor Clive Siviour is the Head of Department and Professor of Materials Engineering at the University of Oxford's Department of Engineering Science. He holds a Fellowship at St Anne's College and has held leadership roles such as Associate Head of Department for Infrastructure. His research focuses on high strain rate deformation of materials, particularly using Split-Hopkinson bar techniques, and spans polymers, composites, and thermomechanical behavior analysis. He leads strategic initiatives for departmental infrastructure and new building development. Education: MPhys (2001) and PhD (2005) in Physics from the University of Cambridge, specializing in material properties under dynamic loading. Research Interests: Experimental characterization of polymers and composites under extreme conditions, development of predictive models for material behavior, and advanced testing methodologies. His work bridges fundamental material science with engineering applications in defense, aerospace, and biomedical fields. Awards: Fellowship of St Anne's College (recognizing academic excellence and contribution to the college). Advising & Grants: While specific grant details are not listed, his leadership roles suggest involvement in large-scale research funding. Advising focuses on PhD/MSc students in materials engineering and mechanics. Labs/Teams: Active in Oxford's Engineering Science Department, leading research groups focused on dynamic material testing and polymer innovation.

• Hybrid Semiconductors
• Perovskite Materials
• Charge-Carrier Dynamics
• +۳ مورد دیگر

Dr. Silvia Motti is a Lecturer at the University of Southampton, focusing on optoelectronic and quantum materials. Her research centers on hybrid low-dimensional semiconductors, particularly perovskite materials, investigating charge-carrier dynamics, lattice dynamics, and device applications such as solar cells and LEDs. She leads projects on exciton formation, charge transport mechanisms, and energy-level alignment in semiconductor heterostructures. Her current roles include supervising three PhD students in Physics and Quantum Technology Engineering. She collaborates with the Quantum, Light and Matter Group and contributes to the EPSRC Centre for Doctoral Training in Quantum Technology Engineering. Her work bridges computational modeling, experimental physics, and device engineering, with a focus on understanding material properties at the atomic scale to improve optoelectronic performance. Key research areas include perovskite solar cells, light-emitting diodes, and the structural dynamics of halide materials. Dr. Motti's publications highlight advancements in understanding defects, phase segregation, and energy-level alignment critical for high-efficiency devices.

Melika Payvandمشاهده پروفایل

استادیار

• Neuroinformatics
• Artificial Intelligence
• Neuromorphic Computing
• +۵ مورد دیگر

Melika Payvand is an Assistant Professor at the Institute of Neuroinformatics, University of Zurich and ETH Zurich, leading the Emerging Intelligent Substrates lab. She holds MSc and PhD degrees in Electrical and Computer Engineering from the University of California, Santa Barbara (2012 and 2016). Her research focuses on understanding natural intelligence and translating its principles into efficient artificial intelligence systems, particularly exploring hierarchical structures and memristive devices to reduce AI operational costs. She received the 2023 Swiss National Science Foundation Starting Grant and is actively involved in neuromorphic initiatives, including co-coordinating the NEUROTECH project and co-chairing the International Conference on Neuromorphic Systems (ICONS). Her work bridges neuroscience and engineering, emphasizing neuromorphic computing’s potential for scalable and energy-efficient AI. Key contributions include developing the Neurobench benchmarking framework and advancing 3D integration for neuromorphic systems. She also chairs the Neural Systems and Applications committee of the IEEE Circuits and Systems Society, reflecting her leadership in the field. Research interests span neuromorphic architectures, memristive technologies, and spiking neural networks. She has pioneered studies on temporal hierarchy, in-memory computing, and hardware-aware training methods. Her lab’s interdisciplinary approach integrates circuit design, machine learning, and neurobiology to create next-generation intelligent systems.

Jean Lynch-Stieglitzمشاهده پروفایل

استاد

• Climate Science
• Paleoceanography
• Biogeochemistry
• +۳ مورد دیگر

Dr. Jean Lynch-Stieglitz is a Professor and Chair in the School of Earth & Atmospheric Sciences at Georgia Institute of Technology. Her research focuses on understanding Earth's ocean and climate system over the past 100,000 years through geochemical and oceanographic methods. She combines isotopic and geochemical proxies to reconstruct paleoceanographic conditions, particularly in the tropical and Atlantic regions. Dr. Lynch-Stieglitz holds a Ph.D. in Geological Sciences from Columbia University, alongside M.A. and dual B.S. degrees (Physics and Geology) from Duke University. Her work emphasizes the interplay between ocean circulation and climate change, with contributions to understanding glacial cycles, carbon cycling, and ENSO variability. Her recent studies highlight advancements in proxy development (e.g., clumped isotopes, preformed phosphate) to trace deep-ocean processes and AMOC dynamics. Collaborations with the IPCC reflect her engagement in translating paleoclimatic insights into contemporary climate science. Her lab (ES&T 1185) focuses on integrating field data from marine sediment cores with modeling to address questions about climate tipping points and past climate extremes. She advises on high-impact projects such as synTrACE-21, which synthesizes transient climate evolution over the last 21,000 years.

David Mguniمشاهده پروفایل

مدرس

• Multi-Agent Systems
• Reinforcement Learning
• Game Theory
• +۳ مورد دیگر

Dr. David Mguni is a Lecturer in Computer Science at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on multi-agent systems, reinforcement learning, game theory, and decision-making under uncertainty. He holds a PhD in applied mathematics from University College London and prior degrees from the University of Cambridge and the University of London. Before academia, he was a Principal Scientist at Huawei, leading the Multi-Agent Systems team in London. His work bridges theoretical foundations with practical applications, particularly in strategic decision-making and autonomous systems. He teaches the ECS656U/ECS796P Distributed Systems course and supervises students in reinforcement learning, game theory, and optimization. Research interests include non-zero-sum game frameworks, safe reinforcement learning (e.g., SAUTE RL), and reward shaping techniques. His publications span venues like AAMAS, NeurIPS, and IJCAI, addressing challenges in multi-agent coordination, adversarial environments, and algorithmic efficiency. Collaborations involve designing scalable methods for large systems and integrating language models into decision-making processes. David is part of Queen Mary’s Centre for Multimodal AI, contributing to projects on socially-attentive policies, risk-aware systems, and distributed optimization. His recent work emphasizes safety in multi-agent learning (e.g., SAUTE RL), variance reduction in estimator models, and frameworks for managing adversarial scenarios. He actively seeks PhD candidates to explore reinforcement learning and game-theoretic applications.

Scott J. McCormackمشاهده پروفایل

استادیار

• Materials Science
• Crystallography
• Thermodynamics
• +۵ مورد دیگر

Scott J. McCormack is an Assistant Professor in the Department of Materials Science and Engineering at the University of California, Davis. His research focuses on materials in extreme environments, with an emphasis on crystallography, thermodynamics, and synthesis of complex oxides, carbides, and nitrides under high-temperature and high-pressure conditions. The McCormack Lab explores how material symmetry and atomic structure influence thermochemical and thermophysical properties to develop materials for space exploration and hypersonic applications. Key research areas include entropy-stabilized oxides, ultra-high-temperature ceramics, and non-equilibrium materials. McCormack has secured significant funding, including a $1.4M grant for ceramic processing and an NSF CAREER Award. His work merges experimental methods (e.g., in-situ XRD, aerodynamic levitation) with computational modeling to characterize materials under extreme conditions. Awards: NSF CAREER Award, $1.4M Research Grant Labs/Teams: McCormack Lab Key Technologies: High-temperature synthesis, carbothermal reduction, entropy stabilization His publications span advanced ceramics, thermal expansion analysis, and phase transformation kinetics, with a focus on applications in aerospace and energy systems.

• Quantitative Genetics
• Evolutionary Biology
• Plant Breeding
• +۳ مورد دیگر

J. Bruce Walsh is a Professor at the University of Arizona, with a focus on evolutionary and quantitative genetics. His research explores genetic variation, selection dynamics, and breeding strategies across plant and animal systems. Walsh's work bridges theoretical frameworks with applied challenges in agriculture and conservation. Key research areas include genomic selection methodologies, evolutionary potential of populations, and the genetic basis of quantitative traits. He has contributed significantly to understanding how non-adaptive forces (e.g., genetic drift) interact with natural and artificial selection. Walsh's publications span topics from theoretical population genetics to applied breeding programs, emphasizing sustainable solutions for orphan crops and biodiversity preservation. His work often integrates computational models with empirical data to address complex evolutionary questions.

• Fluid-Structure Interaction
• Geophysics Applications
• Immersed Boundary Methods
• +۳ مورد دیگر

Maria Giuseppina Chiara Nestola is a Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), and partially affiliated with the Department of Earth Sciences at ETH Zurich. Her work focuses on developing advanced numerical methods for fluid-structure interaction (FSI), immersed boundary techniques, and fracture network modeling in biomedical and geophysical contexts. She contributes to projects like FASTER (geothermal reservoir simulations) and HPC-PREDICT (cardiovascular prognosis), and leads software development for tools like AV-FLOW (FSI library), Parrot (fracture network modeling), and Utopia (linear algebra). Her research integrates high-performance computing with applications in geophysics (e.g., seismic hazard assessment, geothermal systems) and biomechanics (e.g., aortic valve dynamics, turbulent blood flow). Key collaborations include the Center for Computational Medicine in Cardiology and the Swiss Competence Center for Energy Research (SCCER-SoE). She holds roles as FOMICS administrator and project co-PI/co-investigator in PASC initiatives. Her technical expertise spans parallel algorithms, embedded finite-element methods, and multi-physics simulations. Current efforts emphasize real-time geothermal reservoir modeling using Multilevel Monte Carlo methods and benchmarking fracture flow simulations.

Morten Eskildsenمشاهده پروفایل

استاد

• Condensed Matter Physics
• Superconductivity
• Magnetic Materials
• +۳ مورد دیگر

Morten Eskildsen is a Professor and Chair of the Department of Physics & Astronomy at the University of Notre Dame. His research focuses on mesoscale magnetic structures, including skyrmions in chiral magnets and vortices in superconductors. He employs small-angle neutron scattering at facilities like Oak Ridge National Laboratory, complemented by molecular dynamics simulations. Education: Completed a M.Sc. (1994) and Ph.D. (1998) at the University of Copenhagen and Risø National Laboratory, Denmark. Research interests include topological materials, vortex lattice dynamics, and superconductivity. His work investigates skyrmion manipulation by electrical/thermal currents and vortex lattice behavior in unconventional superconductors like UPt₃ and Sr₂RuO₄. Honors: Rev. Edmund P. Joyce, C.S.C., Teaching Award (2016) Fellow of the American Physical Society (2014) Alfred P. Sloan Research Fellowship (2005-2009) Lab & Group: Leads the Eskildsen Research Group, focusing on experimental studies of magnetic and superconducting phenomena. The group's work bridges mesoscopic structure studies with fundamental condensed matter physics.

Sharath Girimajiمشاهده پروفایل

استاد

• Turbulence Modeling
• Fluid Dynamics
• Computational Fluid Dynamics
• +۳ مورد دیگر

Sharath S. Girimaji is a Professor of Aerospace Engineering and Mechanical Engineering and currently serves as the Department Head of Ocean Engineering at Texas A&M University. He holds the Wofford Cain Chair II and serves as Chief Scientist at the ASTRO Center. His research focuses on turbulence modeling, computational fluid dynamics, and high-speed flows. Girimaji earned his Ph.D. from Cornell University in 1990, following degrees from Cornell (M.S., 1986) and the Indian Institute of Technology Madras (B.Tech., 1983). Education: Ph.D., Cornell University, 1990 M.S., Cornell University, 1986 B.Tech., Indian Institute of Technology Madras, 1983 Research Interests: Dr. Girimaji specializes in turbulence closure modeling, scale-resolving simulations (e.g., PANS), and computational methods for compressible and multiphase flows. His work addresses challenges in high-speed aerodynamics, combustion, and magnetohydrodynamics. Recent efforts include data-driven approaches for turbulence modeling and applications in renewable energy systems like wave energy converters. Awards & Recognition: 2010: Texas Engineering Experiment Station Fellow 2007: Dow Chemical Fellow 2006: Brockett Professor & TEES Fellow 2001: TEES Special Research Fellow Grants & Advising: His research is supported by grants from agencies including the U.S. Department of Energy and the Office of Naval Research. He has advised numerous graduate students and collaborates with industry and academic partners on advanced fluid dynamics projects. Labs & Teams: He leads the ASTRO Center and collaborates with interdisciplinary teams in the Texas A&M College of Engineering, focusing on innovative solutions for fluid mechanics challenges in energy and aerospace systems.

Tianbao Yangمشاهده پروفایل

دانشیار

• Optimization
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Tianbao Yang is an Associate Professor of Computer Science & Engineering at Texas A&M University and a Herbert H. Richardson Faculty Fellow. His research focuses on machine learning, optimization, and their applications in AI, medical imaging, and fair AI. He holds a Ph.D. from Michigan State University and a B.Eng. from the University of Science and Technology of China. Key research interests include deep AUC maximization, non-convex optimization, stochastic algorithms, and self-supervised learning. His work on LibAUC, a library for deep learning optimization, has achieved top performance in competitions like the Stanford CheXpert and MIT AI Cures challenges. Education: Ph.D. in Computer Science (Michigan State University, 2012), B.Eng. in Automation (University of Science and Technology of China, 2007). Awards: NSF CAREER Award (2019), UIowa Dean's Excellence in Research Scholar (2019), COLT Best Student Paper (2012). Grants: NSF-Amazon Joint Fair AI Grant ($800K, 2022–2025), NSF RI Program for imbalanced data (2021–2024). His lab (OptMAI Lab) develops algorithms for X-risk optimization, stochastic optimization, and distributed learning. Notable contributions include first-place wins in medical imaging competitions and advances in federated learning and fair AI.