جست‌وجوی استاد و پژوهشگر

Kaiqun Fuمشاهده پروفایل

استادیار

• Spatial Data Mining
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Kaiqun Fu is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. and M.S. in Computer Science from Virginia Tech (2021 and 2016). His research focuses on spatial data mining, spatiotemporal event analysis, graph neural networks, and urban computing applications such as traffic impact prediction and social media-driven insights. He also explores physics-informed machine learning for power systems and interdisciplinary topics like 'deaths of despair' in rural areas. Education: Ph.D. in Computer Science, Virginia Tech, 2021 M.S. in Computer Science, Virginia Tech, 2016 Research Interests: His work emphasizes machine learning and deep learning applications in spatial-temporal domains, including: Graph neural networks for traffic incident prediction Social media analysis for urban challenges Physics-informed models for power grid stability Citation forecasting in scientific publications Grants & Projects: NSF CRII ($174,734): Spatiotemporal impacts of traffic events via graph neural networks (2024–2026) NSF EAGER ($300,000): Socio-economic impacts of emerging technologies (2024–2026) SDSU RSCA ($10,118): Graph transformer-based location learning (2023–2024) Professional Involvement: He chairs ACM SIGSPATIAL's SRC committee, serves on SDSU's Computer Science curriculum committees, and is an IEEE member. He co-edits Frontiers in Big Data and advises on interdisciplinary projects like climate-impacted grid security (NSF RII Track-2, $750,000). Labs/Teams: Collaborates with interdisciplinary groups focusing on smart cities, data-driven infrastructure resilience, and GeoAI applications.

Yongjie Jessica Zhangمشاهده پروفایل

استاد

• Finite Element Method
• Isogeometric Analysis
• Image-Based Geometric Modeling
• +۵ مورد دیگر

Dr. Yongjie Jessica Zhang is a Professor at Carnegie Mellon University, holding appointments in both the Department of Mechanical Engineering and the Department of Biomedical Engineering . She received her B.S. and M.S. in Engineering Mechanics from Tsinghua University, followed by an M.S. in Aerospace Engineering and a Ph.D. in Computational Engineering and Sciences from the University of Texas at Austin. After a postdoctoral fellowship at ICES, she joined CMU in 2007, advancing from assistant to full professor by 2016. Research Interests : Image-based geometric modeling, mesh generation, finite element analysis (FEA), isogeometric analysis, and applications in computational biomedicine, materials science, and computer-assisted surgery. Leadership Roles : Chair of Solid Modeling Association (2019-2020), USACM Executive Committee Member-at-Large (2017-2021), and ELATE Fellow (2017-2018). Her work addresses the critical challenge of automating high-fidelity geometric modeling and mesh generation for complex domains (e.g., human anatomy), which traditionally consumes ~80% of FEA time. Her group develops AI-driven methods for multiscale modeling (molecular to organ), with applications in neuroscience , biomechanics , and 4D printing . Notable awards include the Presidential Early Career Award (PECASE) , NSF CAREER Award , and ASME Van C. Mow Medal (2025) . Dr. Zhang’s publications span over 170 peer-reviewed articles, focusing on truncated hierarchical B-splines , polycube meshing , and neurite transport modeling . She has advised more than 40 students, including PhD candidates and postdoctoral fellows. Her editorial roles include Associate Editor of Computer Aided Geometric Design and editorial board memberships in Computer-Aided Design and Engineering with Computers .

• Viscoelastic Materials
• Vibration and Shock Isolation
• Smart Materials and Structures
• +۶ مورد دیگر

Sunday Oyadiji is an Associate Professor in the Department of Mechanical and Aerospace Engineering at The University of Manchester. He holds a BSc (First Class) and PhD in Mechanical Engineering from the same institution (1979 and 1983). Before joining the University of Manchester in 1991, he served as a Lecturer at Obafemi Awolowo University, Nigeria, and conducted postdoctoral research at Heriot-Watt University and the University of Manchester. His research focuses on viscoelasticity, smart materials for vibration isolation, structural fault identification, and composite materials. Key areas include fracture mechanics, multibody dynamics, and biomechanics. He contributes to the Aerospace Research Institute and Digital Futures platforms, advancing aerospace engineering and structural fire safety. Recent work involves stress-intensity factor analysis using 3D-DIC and finite element methods, constitutive modeling of elastomeric foams, and graphene-based energy storage systems. His publications span high-impact journals like Engineering Fracture Mechanics and Polymer .

Herbert Kochمشاهده پروفایل

استاد

• Partial differential equations
• real harmonic analysis
• dispersive equations
• +۱ مورد دیگر

Herbert Koch is Professor of Mathematics at the University of Bonn, leading the Research Group for Analysis and Partial Differential Equations. His work addresses fundamental problems in nonlinear PDEs and mathematical physics. Research focuses on dispersive equations, fluid dynamics, and harmonic analysis, with recent investigations into soliton stability and blow-up phenomena in wave maps. Publications demonstrate consistent mathematical rigor in analyzing complex nonlinear systems.

Matthias Ihmeمشاهده پروفایل

استاد

• Combustion Science
• Fluid Dynamics
• Computational Fluid Dynamics
• +۵ مورد دیگر

Matthias Ihme is a Professor in the Department of Mechanical Engineering and Photon Science Directorate at Stanford University. His research focuses on large-eddy simulation (LES) of turbulent reacting flows, aeroacoustics, combustion-generated noise, numerical methods, and high-order schemes. He holds a Ph.D. from Stanford University (2008), an M.Sc. in Computational Engineering from the University of Erlangen (Germany, 2002), and a Dipl.-Ing. in Mechanical Engineering from Munich University of Applied Sciences (Germany, 2000). His work bridges computational fluid dynamics, combustion science, and photon science, with notable contributions to supercritical fluid dynamics, machine learning integration in fluid simulations, and high-fidelity atmospheric transport modeling. Recent research emphasizes ultrafast cluster dynamics, shock-induced interface behavior, and stochastic ignition mechanisms in advanced fuel systems. Publications highlight interdisciplinary advancements, including physics-informed ML frameworks for reacting flows and experimental studies using X-ray photon correlation spectroscopy. His projects often involve high-performance computing and collaboration with national labs like SLAC.

Ton Diekerمشاهده پروفایل

دانشیار

• Stochastic Processes
• Operations Research
• Queueing Theory
• +۳ مورد دیگر

Ton Dieker is an Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University's School of Engineering and Applied Science. He is a DSI Member and affiliated with the Center for Financial and Business Analytics. His research focuses on stochastic models, simulation techniques, and high-dimensional stochastic analysis. Dieker holds a Master’s in Operations Research from Vrije Universiteit Amsterdam (2002) and a PhD in Mathematics from the University of Amsterdam (2006). Dieker’s research explores stochastic processes, queueing theory, and rare-event simulation. He has contributed to methodologies like QPLEX for stochastic systems and advanced techniques in sequential analysis and exact simulation. His work bridges theoretical foundations with computational applications, addressing challenges in large-scale networks and high-dimensional problems. Education: PhD in Mathematics, University of Amsterdam, 2006 Master’s in Operations Research, Vrije Universiteit Amsterdam, 2002 Dieker’s articles emphasize computational modeling, stochastic calculus, and optimization, reflecting his focus on bridging theory and practice. His work often addresses efficiency in simulation, exactness in algorithms, and scalability in complex systems. Awards: Goldstine Fellowship (IBM Research) NSF CAREER Award Erlang Prize (INFORMS) Fouts Family Early Career Professorship (Georgia Tech) He serves on editorial boards for Operations Research and Mathematics of Operations Research . His research also addresses capacity management in stochastic networks and applications in cloud computing and commodity sourcing.

Joshua Socolarمشاهده پروفایل

استاد

• Condensed Matter Physics
• Quasicrystals
• Granular Materials
• +۲ مورد دیگر

Joshua Socolar is Professor of Physics at Duke University specializing in collective phenomena in condensed matter systems. His research explores exotic material structures including quasicrystals and granular systems. Education: Ph.D., University of Pennsylvania (1987) B.A., Haverford College (1980) Research Focus: Non-periodic structures, self-assembly of colloidal particles, shear jamming in granular matter, and topological properties of mechanical lattices. Leadership: Served as Chair of Duke's Academic Council (2013-2015) and Trinity College Arts & Sciences Council (2021-2024).

Scott T. M. Dawsonمشاهده پروفایل

استادیار

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +۵ مورد دیگر

Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.

Young-Jin Chaمشاهده پروفایل

استاد

• Deep Learning
• Structural Health Monitoring (SHM)
• Autonomous UAVs
• +۵ مورد دیگر

Dr. Young-Jin Cha is a tenured full Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. He holds a PhD from Texas A&M University and has postdoctoral experience at MIT. His research focuses on deep learning-based structural health monitoring (SHM), autonomous UAVs for infrastructure inspection, and smart transportation systems, with over 100 peer-reviewed publications and $1.2M in grants. He is a Fellow of ASCE and has received notable awards including the 2021 Merit Award and 2022 International Association of Advanced Materials Scientist Award. His work has been cited over 9,200 times globally. Research interests include automated SHM with UAVs, nonlinear system identification, unsupervised deep learning for damage detection, and sustainable infrastructure design. He serves as an editor for journals like Structural Control & Health Monitoring and Engineering Reports . His lab, the Laboratory for Infrastructure Science and Technology (LIST), develops advanced technologies for infrastructure resilience. Key achievements include pioneering deep learning-based SHM with UAVs, top-cited papers in civil engineering journals, and leadership in organizing international conferences. He actively seeks graduate students for research in AI-driven infrastructure solutions.

• Geometric Analysis
• Partial Differential Equations
• Calculus of Variations
• +۳ مورد دیگر

Andrea Pinamonti is an Associate Professor at the University of Trento. His research focuses on geometric analysis, partial differential equations, calculus of variations, and functional analysis in metric measure spaces, particularly in sub-Riemannian and Carnot group settings. He frequently collaborates with researchers from institutions such as the Universities of Pisa, Jyväskylä, and others, addressing topics like geometric measure theory, regularity of solutions, and nonlocal functionals. His recent work examines structures in Heisenberg groups, such as perimeter minimization, CR geometry, and differentiability theorems. He has also explored equations involving the p-Laplacian, fractional operators, and universal differentiability sets in non-Euclidean spaces. These studies reflect a sustained engagement with the interplay between geometry and analysis in sub-Riemannian frameworks. Events and Contributions: Speaker at Warsaw Analysis Days Event WADE25 (2025), Summer school in fluid dynamics (2024), and Workshop on Synthetic Curvature Bounds (2024). Organizer of Three days between Analysis and Geometry in Trento (2025, 2024) and EUregio School on Control Theory and Applications (2024). He has maintained a prolific publication record across high-impact journals such as Journal of Geometric Analysis , Advances in Mathematics , and Communications in Contemporary Mathematics . His academic activities include promoting collaborative research through workshops and open positions at his institution.

Dani S. Bassettمشاهده پروفایل

استاد

• Network Science
• Complex Systems
• Brain Connectivity
• +۵ مورد دیگر

Dani S. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania with primary appointment in the Department of Bioengineering (School of Engineering and Applied Science) and secondary appointments in Physics & Astronomy, Electrical & Systems Engineering, Neurology, and Psychiatry. They serve as an external professor at the Santa Fe Institute and lead a research group focused on complex systems and network science. B.S. in Physics, Penn State University (2004) Ph.D. in Physics, University of Cambridge as Churchill Scholar and NIH Health Sciences Scholar (2009) Postdoctoral position at UC Santa Barbara and Junior Research Fellow at Sage Center for the Study of the Mind Their research integrates complex systems science, statistical mechanics, and applied mathematics to study network dynamics in physical and biological systems. Key areas include brain connectivity mechanisms, cognitive processes, neurological disease modeling, granular matter physics, and collective human curiosity. Bassett employs advanced methodologies including algebraic topology, network control theory, and multilayer network analysis to investigate how network architecture influences system function across diverse domains. Recent publications reveal a strong trend toward interdisciplinary network science applications, particularly in modeling human curiosity through Wikipedia navigation patterns and analyzing brain network reconfiguration during cognitive development. Their work bridges physics, neuroscience, and behavioral science with emphasis on topological network properties and dynamical processes. American Psychological Association's Rising Star (2012) MacArthur Fellow Genius Grant (2014) Lagrange Prize in Complex Systems Science (2017) Erdos-Renyi Prize in Network Science (2018) American Physical Society Fellow (2021) Web of Science Highly Cited Researcher (3 consecutive years) Bassett's research is supported by major agencies including NSF, NIH, DoD, ONR, and private foundations (MacArthur, Sloan, Paul Allen). Their lab actively recruits students from physics, engineering, neuroscience, and computer science backgrounds, emphasizing diversity in academic perspectives. Current projects include the 'Curious Minds' initiative exploring collective knowledge building and network-based models of neurological disorders. Bassett co-authored the MIT Press book 'Curious Minds: The Power of Connection' with philosopher Perry Zurn.

• Numerical Analysis
• Differential Equations
• Geometric Numerical Integration
• +۵ مورد دیگر

Elena Celledoni is a Professor in the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU). She has been employed at NTNU since 2004 and has held the position of professor since 2009. She is a member of the Differential Equations and Numerical Analysis Group at the Department of Mathematical Sciences and serves as its leader. Her educational background includes: Master's degree in Mathematics from the University of Trieste (1993) Ph.D. in Computational Mathematics from the University of Padua, Italy (1997) Elena Celledoni's research focuses on numerical analysis, particularly structure preserving algorithms for differential equations and geometric numerical integration. Her work bridges theoretical mathematics with practical computational methods, developing algorithms that maintain the geometric properties of the systems they approximate. She has made significant contributions to Lie group integrators, energy-preserving methods, and the application of these techniques to mechanical systems and shape analysis. In recent years, her research has expanded to include the intersection of numerical methods with machine learning, exploring how structure-preserving approaches can enhance neural networks and data-driven modeling. Her publications demonstrate a clear trend toward integrating traditional numerical analysis with modern machine learning techniques while maintaining a strong foundation in geometric integration and structure preservation. This interdisciplinary approach has led to innovations in neural ODEs, structure-preserving neural networks, and physics-informed machine learning models that respect the underlying mathematical structures of the systems they model. Elena Celledoni has received recognition for her work through the following honors: Member of the Royal Norwegian Society of Sciences and Letters Member of the European Consortium of Mathematics in Industry Council Member of the board of the International Council of Mathematics in Industry and Applications Editorial board member for SIAM Review, Journal of Computational Dynamics, Journal of Geometric Mechanics, Calcolo, and Networks and Heterogeneous Media As an advisor, she has mentored several students including Torbjørn Ringholm who completed his doctoral dissertation on 'Discrete gradient methods in image processing and partial differential equations on moving meshes.' Her research has been supported by various grants enabling her to lead projects on geometric numerical integration, collaborate internationally, and organize significant academic events such as the special semester at Isaac Newton Institute of MS in 2019 on 'Geometry, compatibility and structure preservation.' She leads the Differential Equations and Numerical Analysis Group at NTNU, which focuses on developing and analyzing numerical methods that preserve the geometric structure of differential equations. The group maintains active collaborations with researchers worldwide and has made substantial contributions to advancing the field of geometric numerical integration and its applications to real-world problems.

Chengzong Pangمشاهده پروفایل

دانشیار

• Power Systems
• Electrical Engineering
• Control Systems
• +۷ مورد دیگر

Chengzong Pang is an Associate Professor and MSECE Graduate Coordinator at the Department of Electrical and Computer Engineering, College of Engineering, Wichita State University. His work focuses on power systems, electrical engineering innovations, and renewable energy integration. He specializes in transient stability analysis, control systems, and smart grid technologies. Research Interests: Dr. Pang's expertise includes advanced control strategies for power electronics (e.g., PMSM, UPQC), machine learning applications for grid stability (LSTM/SVM), and energy storage solutions for renewable integration. His research also addresses challenges in microgrid operation, subsynchronous oscillation mitigation, and battery storage systems. Key Trends in Publications: Over 20 years of publications (2002–2022) emphasize: (1) Machine learning for power system analysis, (2) Control system design for renewable integration, (3) Grid stability enhancement via advanced algorithms, and (4) Smart grid infrastructure optimization. Recent works (2021–2022) highlight transient stability prediction and ANFIS-based power quality solutions. Labs/Teams: Active in energy systems research groups focusing on renewable integration and grid modernization, though specific lab names are not explicitly stated in the provided texts.

Bauyrzhan Primkulovمشاهده پروفایل

استادیار

• Interfacial Fluid Dynamics
• Soft Matter Physics
• Porous Media Flow
• +۳ مورد دیگر

Bauyrzhan Primkulov is an Assistant Professor of Mechanical Engineering at Yale University. His research focuses on interfacial fluid dynamics and soft matter physics, with emphasis on fluid-fluid displacement in disordered environments and hydrodynamic pilot-wave theory. He holds a Ph.D. from MIT (2022) and B.Sc./M.Sc. from the University of Alberta. Primkulov's work bridges theoretical and experimental approaches to address energy and environmental challenges. His team investigates phenomena such as capillary flow dynamics in porous media, wettability effects on displacement patterns, and pilot-wave systems that mimic quantum behaviors. Key contributions include advancing Lenormand's phase diagram for multiphase flows and studying avalanches in imbibition processes. Recipient of InterPore PoreLab Award (2024) and MIT's CEE Best Doctoral Thesis (2022) Expertise spans experimental hydrodynamics, multiphase flow modeling, and granular media mechanics Active in developing novel methods like photoporomechanics to visualize stress fields in fluid-filled granular systems His recent studies explore crossover dynamics between stick-slip and steady sliding regimes in viscous slugs, as well as confinement effects in pilot-wave hydrodynamics. Primkulov collaborates across disciplines to translate fundamental fluid mechanics insights into practical solutions for energy storage and environmental systems.

• Topological Quantum Computing
• Quantum Many-Body Physics
• Condensed Matter Physics
• +۴ مورد دیگر

Jay D. Sau is a Professor of Physics at the University of Maryland, College Park, and Co-Director of the Joint Quantum Institute (JQI). His research focuses on theoretical condensed matter physics, particularly topological quantum computing, quantum many-body systems, and Majorana fermions. He holds affiliations with the Condensed Matter Theory Center (CMTC) and JQI. Sau received his Ph.D. from UC Berkeley in 2008. His work bridges theoretical concepts in topological materials, superconductivity, and quantum information processing. Research Interests: Sau's primary interests include applying topological principles to solid-state and cold-atomic systems for quantum computation. Key areas include topological superconductivity, Majorana fermions, quantum Hall effects, and spin-orbit coupled systems. His group explores phenomena like topological degeneracy, Weyl semimetals, and cold atomic gases. Awards: He has been recognized with the National Science Foundation CAREER Award (2016) and the Sloan Research Fellowship (2016). His work has been published extensively in high-impact journals and covers topics ranging from Majorana physics to quantum phase transitions. Advising & Labs: Sau mentors graduate students including Tamoghna Barik, Stuart Thomas, Huan-Kuang Wu, and Shuyang Wang. His research group collaborates on projects at JQI and CMTC, focusing on experimental realizations of topological qubits and quantum devices.