جست‌وجوی استاد و پژوهشگر

Bhuvana Srinivasanمشاهده پروفایل

استاد

• Fusion Energy
• Plasma-based Propulsion
• High-Energy-Density Plasmas
• +۵ مورد دیگر

Bhuvana Srinivasan is a Professor in the Department of Aeronautics and Astronautics at the University of Washington, directing the PLASMAWISE Laboratory. Previously, she held the rank of Associate Professor and served as Director of the Plasma Dynamics Computational Laboratory at Virginia Tech, supported by the Crofton Faculty Fellowship. Her research focuses on fusion energy, plasma-based propulsion, and computational plasma physics, with an emphasis on plasma-material interactions and instabilities across diverse plasma regimes. She has authored over 30 peer-reviewed publications and secured grants from the NSF, DOE, and AFOSR. Education: Ph.D. in Aeronautics and Astronautics, University of Washington (specializing in computational plasma physics) M.S. in Aeronautics and Astronautics, University of Washington B.S. in Aerospace Engineering and Mechanical Engineering, Illinois Institute of Technology Research Interests: Her work spans fusion energy concepts, plasma propulsion systems, high-energy-density plasma instabilities, and ionospheric dynamics. Key areas include plasma-surface interactions in fusion devices, magnetic field effects on plasma mixing, and algorithm development for fluid-kinetic models. She emphasizes high-fidelity multi-fluid simulations using discontinuous Galerkin methods. Awards & Recognition: NSF CAREER Award (2019-2024) Crofton Faculty Fellow (Virginia Tech, 2021-2023) Dean’s Outstanding Assistant Professor (Virginia Tech, 2017) Amelia Earhart Fellowship (Zonta International, 2007-2009) Advocacy & Leadership: She chairs DEI initiatives in academic departments and serves on national committees including the DOE Fusion Energy Sciences Advisory Committee and the APS Division of Plasma Physics Executive Board. Her work bridges computational plasma physics with societal impact, including fusion energy democratization and space exploration propulsion systems.

Alexandros Kontogiannisمشاهده پروفایل

پژوهشگر ارشد

• Fluid Dynamics
• Applied Mathematics
• Bayesian Inference
• +۱۰ مورد دیگر

Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.

Corrado Mauriniمشاهده پروفایل

استاد

• Fracture Mechanics
• Damage Mechanics
• Computational Mechanics
• +۵ مورد دیگر

Corrado Maurini is a Professor in Mechanics at Sorbonne University , Paris, France. He leads two international master programs: Mécanique des Solides (Solid Mechanics) and Computational Mechanics .

Gregory J. Wagnerمشاهده پروفایل

دانشیار

• Additive Manufacturing
• Multi-scale Simulations
• Multi-physics Modeling
• +۴ مورد دیگر

Gregory J. Wagner is an Associate Professor of Mechanical Engineering and Director of Graduate Studies at Northwestern University's McCormick School of Engineering. His research focuses on developing computational methods for multi-scale and multi-physics problems in additive manufacturing, fluid dynamics, and heat transfer. He leads the Wagner Research Group, which specializes in high-performance computing tools for complex engineering simulations. Education includes a Ph.D., M.S., and B.S. in Mechanical Engineering from Northwestern University and Boston University. His work integrates machine learning with traditional computational methods to model material behavior, microstructure evolution, and process-structure-property relationships in advanced manufacturing. Notable contributions include the GO-MELT framework for thermal simulations and the C-HiDeNN neural network approach for large-scale systems. Research interests span additive manufacturing process modeling, multiphysics coupling, and data-driven approaches for material design. Awards include the Bette and Neison Harris Chair in Teaching Excellence. Publications emphasize thermal modeling, phase change phenomena, and computational fluid dynamics innovations. His lab's work bridges mesoscopic and multiscale modeling, with applications in energy systems, biomedical devices, and environmental engineering. Collaborations focus on experimental validation and industrial-scale simulation challenges.

• Nonlinear Partial Differential Equations
• Mathematical Physics
• Semiconductor Theory
• +۷ مورد دیگر

Ansgar Jüngel is a Full Professor for Analysis of Nonlinear Partial Differential Equations (PDEs) at the Technische Universität Wien (TU Vienna), affiliated with the E101-Institute for Analysis and Scientific Computing. His academic journey includes roles at universities in Berlin, Konstanz, Mainz, and Vienna since 1991. He specializes in mathematical analysis of cross-diffusion systems, entropy methods, semiconductor models, and quantum fluid dynamics. Notable achievements include an ERC Advanced Grant (2021) and the Tsungming-Tu Award (2011). Research focuses on nonlinear PDEs with applications in physics, engineering, and biology, emphasizing rigorous existence theory, numerical methods, and entropy-based approaches. Recent projects include 'Emerging network structures and neuromorphic applications' and 'Taming complexity in partial differential systems.' His teaching includes courses on partial differential equations, calculus of variations, and computational finance. Publications span over 200 works, with key contributions on cross-diffusion models, quantum hydrodynamics, and energy-transport systems. He has supervised numerous PhD students and collaborates internationally on topics like semiconductor simulations and stochastic interacting particle systems. Grants include an FWF Special Research Programme and ERC funding.

Kobus Barnardمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Scientific Applications
• +۳ مورد دیگر

Kobus Barnard is a Professor in the Department of Computer Science at the University of Arizona, with his office located in GS 708. His research bridges computer vision, machine learning, and interdisciplinary scientific applications across diverse domains. Education: Ph.D. from Simon Fraser University (1999) His research interests focus on extracting meaningful insights from complex data through computer vision and probabilistic modeling. Key areas include machine learning for environmental monitoring (flood detection, plant disease analysis), social dynamics (interpersonal coordination, emotional coregulation), astronomy (transient classification), and multimodal learning (visual-linguistic integration). His work consistently applies deep learning to real-world problems requiring high-resolution data interpretation. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Environmental applications using satellite imagery for flood mapping and agricultural monitoring, (2) Cognitive modeling of human teams and emotional dynamics through probabilistic frameworks, and (3) Astronomical data analysis leveraging host galaxy properties for transient classification. These threads demonstrate his commitment to solving practical scientific challenges through computational innovation. While scientific awards aren't documented in available sources, his leadership in projects like FloodPlanet and ToMCAT indicates significant contributions to data infrastructure. His advising and grant activities remain unreported in the source material, though his extensive interdisciplinary collaborations suggest substantial mentorship impact. Barnard's work operates at the intersection of multiple scientific communities, evidenced by applications spanning neuroscience, agriculture, astronomy, and social science. His current focus on high-resolution data fusion and multimodal modeling positions him at the forefront of real-world AI deployment.

Barbara Wohlmuthمشاهده پروفایل

استاد

• Numerical Simulation
• Partial Differential Equations
• Discretization Techniques
• +۴ مورد دیگر

Prof. Barbara Wohlmuth is a full professor in Numerical Mathematics at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. She leads the International Graduate School of Science and Engineering at TUM and has held professorships at Stuttgart, Darmstadt, and Berlin universities. Her research focuses on numerical simulation of partial differential equations, multiscale solvers, and coupled multi-field problems with applications in engineering. Education: Studied mathematics at TUM and Université Joseph Fourier in Grenoble, received her doctorate from TUM in 1995, and completed habilitation in Augsburg. Visiting professorships in USA, France, and Hong Kong. Research interests include discretization techniques, predictive modeling, and interdisciplinary collaboration with engineering disciplines. Notable achievements: 2012 Gottfried Wilhelm Leibniz Prize (Germany’s highest academic honor in sciences), 2005 Sacchi-Landriani Prize. Publications emphasize advanced numerical methods in fluid dynamics, geophysics, and biomedical engineering. Active in editorial roles for international journals and scientific committees across Europe and USA. Elected member of Bavarian and European Academies of Sciences. Key contributions include: Development of robust numerical algorithms for exascale simulations Pioneering work in coupled multi-physics modeling Innovative methods for computational contact mechanics Leadership in graduate education initiatives

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.

John Dolbowمشاهده پروفایل

استاد

• Computational Mechanics
• Fracture Mechanics
• Numerical Methods
• +۵ مورد دیگر

John Dolbow is a Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University, with secondary appointments in Civil and Environmental Engineering and Mathematics. He is a Bass Fellow and holds leadership roles as Associate Vice President for Research & Innovation since 2024. Education: B.S.M.E. (University of New Hampshire, 1995), M.S. (Northwestern, 1998), Ph.D. (Northwestern, 1999) Research Focus: Computational fracture mechanics, phase-field modeling, hydrogels, and multiphysics problems in geomechanics and biomedical engineering His recent work advances phase-field methods for fracture nucleation, hydraulic fracturing in geothermal systems, and laser lithotripsy simulations. Dolbow leads Duke's Computational Mechanics Laboratory, integrating civil, mechanical, and materials science approaches. Key contributions include: Erratum corrections for computational mechanics frameworks Nitsche-stabilized methods for interface constraints Phase-field models for surfactant-driven particle raft fracture Multi-resolution approaches for hydraulic fracture simulation Embedded FEM techniques for moving boundary problems Scientific Recognition: R. H. Gallagher Young Investigator Award (2005) Robert J. Melosh Medal for Finite Element Analysis (1999) DOE Computational Science Graduate Fellowship (1997) DOE CSGF Steering Committee Chair

Joseph N.S. Eisenbergمشاهده پروفایل

استاد

• Infectious disease epidemiology
• Waterborne pathogens
• Vector-borne diseases
• +۵ مورد دیگر

Joseph N.S. Eisenberg, PhD, MPH is Professor of Epidemiology and Professor of Global Public Health at the University of Michigan School of Public Health . A leading infectious-disease epidemiologist, Eisenberg integrates theoretical transmission modelling with large-scale field studies to understand how environmental and social determinants drive water- and vector-borne disease dynamics across the globe. Education PhD, University of California, Berkeley/San Francisco, 1992 MPH, University of California, Berkeley, 1991 BS, University of California, Berkeley, 1982 Research Focus Eisenberg’s work centres on infectious disease epidemiology , particularly the environmental determinants of waterborne and vector-borne pathogens. His group develops dynamic transmission models that move microbial risk assessment from static individual-based calculations to population-based frameworks capable of capturing feedbacks between human behaviour, climate, infrastructure and pathogen spread. Empirical validation of these models occurs through multi-country collaborations in Ecuador (birth cohort, dengue mapping, zoonotic E. coli), Mexico (waste-water irrigation), Israel (poliovirus environmental surveillance), Ethiopia (urban water-system risk) and Kenya (WASH impacts). Recent Publication Trends Over 2024–2025 Eisenberg has published extensively on dengue and Zika transmission in Ecuador, WASH intervention modelling , antimicrobial resistance in enteric E. coli , and COVID-19 epidemiology . These works combine high-resolution field data (human landing catches, multiplex PCR diagnostics, eye-tracking neurodevelopment assessments) with advanced computational approaches (agent-based models, Bayesian spatial risk mapping, mechanistic QMRA), underscoring a commitment to methodological innovation grounded in real-world public-health problems. Scientific Awards & Recognition While no specific prizes are enumerated in the supplied text, Eisenberg’s sustained funding from the CDC (recent $17.5 M award establishing the Michigan Integrated Center for Outbreak Analytics and Modeling – MICOM) and repeated publication in PNAS , American Journal of Epidemiology and other high-impact journals attest to significant peer recognition. Advising & Grants Eisenberg mentors graduate students and post-doctoral researchers across epidemiology, biostatistics and global health. Active funding includes: CDC / MICOM – Michigan Integrated Center for Outbreak Analytics and Modeling (Principal Investigator) National Institutes of Health – Environmental influences on child diarrheal disease and the microbiome (Co-Investigator) NSF – Coupled natural-human systems: road development and infectious disease in Ecuador (Principal Investigator) Laboratories & Teams Eisenberg directs the EcoDess: Environmental Change and Diarrheal Disease in Ecuador research platform, coordinating interdisciplinary teams in Ann Arbor and field stations in Esmeraldas and Quito provinces. He is affiliated with the University of Michigan Center for Global Health Equity and the Global Public Health IDEAS initiative , fostering cross-campus collaborations in modelling, microbiology and social epidemiology.

Niels Otaniمشاهده پروفایل

دانشیار

• Cardiac Electrophysiology
• Computational Biology
• Biomechanical Imaging
• +۹ مورد دیگر

Niels Otani is an Associate Professor in the School of Mathematics and Statistics at the College of Science, Rochester Institute of Technology (RIT). He holds a BA from the University of Chicago and a Ph.D. from the University of California, Berkeley. His research focuses on cardiac electrophysiology, computational biology, and biomechanical imaging, with a particular emphasis on understanding and controlling cardiac arrhythmias such as ventricular fibrillation. Dr. Otani has pioneered methods for visualizing action potential propagation using ultrasound and developed novel defibrillation strategies that reduce energy requirements. He also investigates the role of ephaptic coupling in cardiac dynamics and the mechanisms underlying spiral wave formation and termination. His work bridges mathematics, physics, and cardiology, with applications in clinical therapies and biomedical engineering. Dr. Otani's research has been supported by grants such as an NSF award for developing diagnostic tools for cardiac disease. He teaches advanced courses in multivariable calculus, linear algebra, and research thesis supervision. Collaborations span interdisciplinary teams, including veterinary cardiology and biomedical imaging groups. His contributions include over 50 publications on topics ranging from action potential dynamics to computational modeling of cardiac tissue, with a focus on translating theoretical findings into clinical solutions.

Larry Goldsteinمشاهده پروفایل

استاد

• Distributional approximation
• Stein’s method
• High dimensional statistics
• +۴ مورد دیگر

Larry Goldstein is a Professor of Mathematics at the University of Southern California, specializing in probability theory, mathematical statistics, and their applications. He holds a Ph.D. in Mathematics from the University of California, San Diego (1984). His research focuses on distributional approximation via Stein’s method, high-dimensional statistics, concentration inequalities, and statistical efficiency, with applications in epidemiology and biomedical monitoring. He has organized and participated in numerous conferences, including the 'Stein’s Method: The Golden Anniversary' in Singapore (2022) and the 'BIRS Stein Conference' in Banff (2022). Goldstein teaches advanced courses such as Probability Theory, Statistical Consulting, and Mathematical Statistics, often incorporating modern computational tools like R. He has led international summer programs at the University of Perugia, Italy, on topics including mathematical statistics and high-dimensional probability. His work bridges theoretical foundations with practical applications, including modeling transdermal alcohol concentration and analyzing complex sampling designs in cohort studies. His contributions to Stein’s method include developing couplings for distributional approximation and concentration inequalities. Goldstein’s teaching emphasizes statistical inference, machine learning, and data analysis, reflecting his dual focus on rigorous theory and real-world problem-solving.

Darrell S Croweمشاهده پروفایل

استادیار

• Aerospace Engineering
• Propulsion Aerodynamics
• Computational Fluid Dynamics
• +۷ مورد دیگر

Lt Col Darrell S. Crowe, PhD, is an Assistant Professor of Aerospace Engineering in the Department of Aeronautics and Astronautics at the Air Force Institute of Technology (AFIT), part of the Graduate School of Engineering and Management at Air University. He is an active military officer and educator contributing to advanced aerospace research and graduate education within the U.S. Air Force. Education: PhD in Aeronautical Engineering, Air Force Institute of Technology, 2014 MS in Aeronautical Engineering, Air Force Institute of Technology, 2008 BS in Aerospace Engineering, Texas A&M University, 2003 Dr. Crowe's research focuses on propulsion aerodynamics, computational fluid dynamics (CFD), supersonic and hypersonic flows, jet interaction effects, and store separation dynamics. His work involves high-fidelity simulations of exhaust nozzles, thermal distortion modeling, and active flow control, often in collaboration with military and aerospace applications. He investigates complex phenomena such as hot streaks in serpentine nozzles, film cooling, and cavity acoustics, contributing to improved aircraft and propulsion system design. His recent publications demonstrate a strong trend in advancing CFD methodologies for defense-related aerospace problems, particularly in propulsion-airframe integration, weapon bay aerodynamics, and supersonic/hypersonic flow control. The articles span both experimental validation and numerical modeling, emphasizing accuracy, turbulence modeling, and multi-physics coupling in extreme environments. Scientific Awards and Honors: AFIT Dean's Distinguished Teaching Professor, 2023 AIAA Associate Fellow, 2020 Air Force Meritorious Service Medal (2018, 2021) Joint Service Commendation Medal, 2017 Southwestern Ohio Council for Higher Education Faculty Excellence Award, 2015 Field Grade Officer of the Quarter, Air University, 2015 Air Force Commendation Medal, 2011 Company Grade Officer of the Quarter (2005, 2009) Air Force Achievement Medal, 2006 Dr. Crowe advises MS thesis students in aerospace engineering and teaches graduate-level courses in his domain. He has been involved in flight testing and simulation projects, often funded through U.S. Air Force research programs. His work supports critical defense capabilities in aircraft performance, propulsion efficiency, and weapon system integration. He is actively involved in professional organizations such as the American Institute of Aeronautics and Astronautics (AIAA) and contributes to major conferences and workshops, including the Propulsion Aerodynamics Workshops. His research is conducted within AFIT’s advanced simulation and modeling environment, leveraging tools like Kestrel and BCFD for high-fidelity analysis.

• Fluid-Structure Interaction
• Isogeometric Analysis
• Nonlinear Structural Mechanics
• +۲ مورد دیگر

Thomas Spenke is a Researcher at the Chair for Computational Analysis of Technical Systems (CATS) within RWTH Aachen University's Center for Computational Engineering Science. He joined the research team in October 2017 after completing his master's degree in Computational Engineering Science at RWTH Aachen, where his academic journey began in 2011 with early engagement in CATS through a 2014 project thesis on isogeometric shell elements. His educational background includes: Studies in Computational Engineering Science at RWTH Aachen University (2011–2017) Bachelor and seminar theses on isogeometric shell elements for fluid-structure interaction Master thesis on numerical methods enhancing partitioned fluid-structure interaction algorithms Dr. Spenke's research spans Fluid-Structure Interaction , Isogeometric Analysis , Nonlinear Structural Mechanics (specializing in Shell Theory), and Interface Quasi-Newton Methods . His work develops efficient computational frameworks for multi-physics problems, emphasizing partitioned approaches that decouple fluid and structural solvers while ensuring stability and convergence through advanced acceleration techniques. His 9 publications (2020–2024) reveal a consistent focus on optimizing partitioned fluid-structure interaction simulations. Key trends include quasi-Newton acceleration for interface coupling, adaptive time-stepping strategies, and spline-based space-time finite elements for moving domains. His research frequently addresses challenges in fully enclosed systems and lubricated contact scenarios, demonstrating strong interdisciplinary links between computational mathematics and mechanical engineering. As an academic advisor, Dr. Spenke has supervised four student projects: J. Zwar (2019): Adaptive time stepping for fluid-structure interaction J. Spatka (2020): Fluid-structure-contact interaction in lubricated temper rolling M. Velioglu (2021): Space-time spline-based finite elements for moving domains S. Woyda (2023): Adaptive time stepping for multi-field problems The Chair for Computational Analysis of Technical Systems operates within RWTH Aachen's Center for Computational Engineering Science, founded by Professor Marek Behr in 2004. Located in the Rogowski building since 2009, CATS focuses on computational methods for engineering systems with expertise in fluid dynamics, structural mechanics, and multi-physics coupling algorithms.

Matthias Oliver Wilhelmمشاهده پروفایل

دانشیار

• Quantum Field Theory
• Mathematical Physics
• Special Functions
• +۳ مورد دیگر

Matthias Oliver Wilhelm is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the Quantum Mathematics research group. His work focuses on advanced theoretical physics topics including scattering amplitudes in gauge/gravity theories, Feynman integrals, special functions, and applications of machine learning in physics. He has contributed to groundbreaking research at the intersection of quantum field theory and mathematical physics, particularly in understanding gravitational wave phenomena and high-energy particle interactions. Research Interests: His research combines quantum field theory with algebraic geometry and computational methods, exploring topics like elliptic Feynman integrals, post-Minkowskian expansions, and machine learning-driven amplitude calculations. Recent work includes leveraging Calabi-Yau manifolds for gravity-related Feynman integrals and developing transformer-based algorithms for scattering amplitude computations. Awards: He received the Velux Grant - Villum Young Investigator in 2018, recognizing his innovative contributions to theoretical physics. Projects: Leveraging Algebraic Geometry for High-Precision Fundamental Physics (2024-2028, DFF-funded) Thermodynamics of strongly coupled Quantum Field Theory (2019-2027, private foundation-funded) Key Themes in Recent Work: His articles emphasize novel computational techniques (e.g., machine learning for integration-by-parts reduction), formal developments in scattering amplitude theory, and geometric approaches to quantum gravity problems. Notable contributions include classifying Feynman integral geometries for black-hole scattering and advancing elliptic function methodologies in perturbative QFT.