جست‌وجوی استاد و پژوهشگر

Stéphane Descombesمشاهده پروفایل

استاد

• Numerical analysis
• Scientific computing
• High-performance computing
• +۷ مورد دیگر

Stéphane Descombes is a Full Professor of Applied Mathematics at the University of Nice Sophia Antipolis since 2007, where he is a member of the JA Dieudonné Laboratory (UMR CNRS-UNS N°7351) and the Nachos project team at Inria Sophia Antipolis - Méditerranée. He currently serves as the Director of the House of Modeling, Simulation and Interactions (MSI) of the University of Côte d'Azur. From September 2012 to September 2016, he was Director of the Mathematics Department, Head of the Mathematics and Interactions master's degree program, and Coordinator of the Mathematical Engineering specialty from 2012 to 2014. His research focuses on numerical analysis, scientific computing, and high-performance computing, particularly in the analysis of partial differential equations. His specific interests include numerical analysis of steep evolution problems arising from biological, medical, or chemical phenomena; methods for decomposing high-order operators in time; high-order time methods for partial differential equations; and artificial boundary conditions and space-time domain decomposition. His recent publications show a strong focus on computational electromagnetics, particularly discontinuous Galerkin methods for Maxwell's equations, and multi-scale reaction-diffusion systems. His work bridges theoretical numerical analysis with practical applications in combustion, biomedical modeling, and electromagnetic wave propagation. His scientific achievements include significant contributions to operator splitting methods, with numerous publications in top journals. His students have received prestigious awards including the Smai-Gamni prize and the Eccomas prize. Prof. Descombes has supervised several PhD students including Julien Olivier, Max Duarte, Ludovic Moya, Lord Youmbi Bienvenu, and Marin Deresco, with research spanning reaction-diffusion systems, computational electromagnetics, and multi-scale modeling.

• Core-collapse supernovae
• Neutrino radiation hydrodynamics
• Numerical simulations
• +۴ مورد دیگر

Dr. Wakana Iwakami is an Associate Professor at Waseda University's School of Advanced Science and Engineering, specializing in core-collapse supernovae simulations and neutrino radiation hydrodynamics. With 28 publications and over 1050 citations, her work focuses on multi-dimensional modeling of supernova mechanisms, particularly neutrino-driven explosions and standing accretion shock instability (SASI). Education: Tohoku University (2000-2009): BSc and MSc in Mechanical/Aerospace Engineering Her research interests span fluid dynamics , supernova physics , neutrino transport , and numerical simulation techniques . Recent projects include 3D Boltzmann neutrino transport simulations and gravitational wave analysis from supernovae. She has received the 2005 Japan Society of Mechanical Engineers Miura Award. Notable trends in publications include: Advancing 3D supernova simulations with full neutrino radiation Investigating neutrino oscillations in extreme supernova environments Developing high-accuracy numerical methods like volume penalization Studying rotational effects on explosion mechanisms Scientific awards: 2005 Japan Society of Mechanical Engineers Miura Award Grants & Research Projects: Continuously funded by Japan Society for the Promotion of Science (2010-2015, 2024-2026) and other institutions for supernova mechanism studies and numerical method development.

Vijaya Raghavendra Bollapragadaمشاهده پروفایل

استادیار

• Nonlinear Optimization
• Stochastic Optimization
• Distributed Optimization
• +۳ مورد دیگر

Raghu Bollapragada is an Assistant Professor in the Operations Research and Industrial Engineering program within the Mechanical Engineering department at The University of Texas at Austin. He is affiliated with the Machine Learning Laboratory, the Oden Institute for Computational Engineering and Sciences, and the Center for Dynamics and Controls of Material (an NSF MRSEC), focusing on algorithmic solutions for large-scale optimization challenges in machine learning and computational physics. His educational background includes: PhD in Industrial Engineering and Management Sciences, Northwestern University MS in Industrial Engineering and Management Sciences, Northwestern University Visiting Researcher at INRIA, Paris during graduate studies Bollapragada's research centers on nonlinear optimization, with expertise in constrained, stochastic, and distributed optimization frameworks. He develops algorithms that leverage modern computational infrastructure to solve complex problems in logistics, control systems, and machine learning, emphasizing scalability and hardware-aware efficiency. His work bridges theoretical convergence guarantees with practical implementation. Recent publications reveal dominant trends in decentralized optimization (gradient tracking, network pruning) and stochastic methods (adaptive sampling, finite-difference estimation), with strong connections to machine learning applications. Key themes include communication-computation tradeoffs, non-convex optimization, and second-order acceleration techniques across 10+ high-impact publications (2024-2025). His scientific awards include: IEMS Nemhauser Dissertation Award for best dissertation IEMS Arthur P. Hurter Award for outstanding academic excellence McCormick terminal year fellowship Walter P. Murphy Fellowship Bollapragada mentors PhD students (including Cem Karamanli, recent thesis defense) and undergraduate researchers (Marissa Llamas, STARS Conference presenter), with research funded by NSF grant DMS-2324643, Argonne National Laboratory, and Lawrence Livermore National Laboratory. His SANDOPT and ZOAdaQN GitHub repositories provide open-source implementations of novel optimization algorithms. He leads the Optimization, Inversion, Machine Learning, and Uncertainty for Complex Systems research group within UT Austin's Machine Learning Laboratory ecosystem, collaborating with the Center for Scientific Machine Learning on cross-disciplinary projects.

• Theoretical condensed matter physics
• Frustrated magnetism
• Strongly correlated electron systems
• +۵ مورد دیگر

Matthew Enjalran is Professor of Physics at Southern Connecticut State University , a position he has held since fall 2003. In addition to teaching across the undergraduate and graduate physics curriculum, he maintains an active research program in theoretical condensed-matter physics focused on strongly correlated and geometrically frustrated systems. Education & Background: While explicit degree details are not provided, the profile notes that Dr. Enjalran arrived at SCSU with prior experience in the private sector and secondary education, bringing a broad perspective to his academic role. Research Interests: Dr. Enjalran’s work centers on theoretical condensed-matter physics , particularly strongly correlated electron systems and frustrated magnetism . He investigates emergent phenomena in spin-ice materials, Hubbard-model realizations on geometrically frustrated lattices (kagome, pyrochlore, triangular), and the quantum phase transitions that arise from competing interactions. A signature theme is the development of advanced numerical techniques—such as extensions of the Thouless–Anderson–Palmer method and Monte Carlo algorithms—to go beyond conventional mean-field treatments. Publication & Dissemination Trends: Across more than a decade of output, his articles consistently target the intersection of geometric frustration , magnetism , and strongly correlated electrons . Key contributions include studies of Dy₂Ti₂O₇ spin ice, fluctuation-driven selection of multi-k order on the pyrochlore lattice, and metal–insulator transitions in the anisotropic Hubbard model. The body of work is published in high-impact journals such as Physical Review B and Physical Review Letters , and is regularly presented at premier venues including the American Physical Society March Meeting and the International Conference on Highly Frustrated Magnetism. Grants & Funding: Nanotechnology Industry Academic Fellowship Program – Co-PI with C. C. Broadbridge & T. C. Schwendemann, Werth Family Foundation (2014-2024) Going beyond mean-field theory in frustrated magnetism with the extended-TAP method – Principal Investigator, SCSU internal grant, $2,500 (2017-2018) Development of Monte Carlo methods to study many-body models of magnetism – PI, Connecticut State University System, $3,550 (2014-2015) A mean-field study of magnetic and charge correlations of electrons on the kagome lattice – PI, SCSU internal grant, $2,500 (2013-2014) Center For Research On Innovative Structure and Phenomena (CRISP) – Supporting Team Member, National Science Foundation (2012-2013) Numerical studies of correlated electrons in confined geometries – PI, SCSU internal grant, $2,500 (2011-2012) Theoretical investigations of interacting electrons on a triangular lattice – PI, Connecticut State University System, $4,750 (2011-2012) A study of geometric frustration in the two-dimensional Hubbard model – PI, multi-year internal award (2006-2010) Laboratory & Team Environment: While no dedicated laboratory name is listed, Dr. Enjalran’s research is conducted within the Department of Physics at Southern Connecticut State University. He collaborates extensively with national and international colleagues, as evidenced by multi-author publications and joint conference presentations, and mentors both undergraduate and master’s students in theoretical and computational projects.

Makrand Khanwaleمشاهده پروفایل

پژوهشگر

• Multiphase flows
• Finite Element Methods
• Numerical Analysis
• +۳ مورد دیگر

Makrand Khanwale is a Physical Science Research Scientist in the Department of Mechanical Engineering at Stanford University, working with Professors Ali Mani and Gianluca Iaccarino. His research focuses on developing numerical methods for simulating multiphase flows, turbulence, and complex physical processes using advanced computational frameworks. He holds a PhD in Mechanical Engineering and Applied Mathematics from Iowa State University (2021), advised by Drs. Baskar Ganapathysubramanian and James Rossmanith, and a B.Tech. in Chemical Technology from the Institute of Chemical Technology, Mumbai (2015). Education: PhD, Iowa State University (2016–2021): Co-major in Mechanical Engineering and Applied Mathematics B.Tech, Institute of Chemical Technology (2011–2015) Research Interests: Khanwale specializes in multiphase flow simulations, finite element methods, numerical analysis, and turbulence modeling. His work emphasizes energy-stable numerical schemes, adaptive mesh refinement, and high-performance computing for applications in green hydrogen production, compressible multiphase flows, and electrochemical systems. Key Projects: INSIEME project under PSAAP III program at Stanford Development of the PROTEUS open-source framework for multiphase flow simulations Modeling of Reynolds Stress Models using forced turbulence simulations Scientific Awards: Finalist, Scientific Visualization and Data Analytics Showcase (Super Computing ‘22) Center for Turbulence Research Postdoctoral Fellowship (Stanford) FMJH/LMH Postdoctoral Fellowship (EM2C, CentraleSupélec, Paris) Research & Teaching Excellence Awards (Iowa State University) Labs & Collaborations: His work is part of collaborative efforts at Stanford’s Center for Turbulence Research and involves partnerships with institutions like Iowa State University and LSU. He contributes to open-source tools like PROTEUS, advancing adaptive mesh techniques and parallel computing.

Robert Jankowskiمشاهده پروفایل

استاد

• earthquake engineering
• structural pounding
• dynamics of metal structures
• +۴ مورد دیگر

Professor Robert Jankowski is a distinguished academic at Gdańsk University of Technology, serving as Professor, Head of the Department of Building Engineering, and Director of the Doctoral School at the Faculty of Civil and Environmental Engineering. With over 309 publications and numerous research projects, he is a leading expert in earthquake engineering and structural dynamics. His educational background includes a doctorate from the University of Tokyo, Japan (1997), where he was supported by a Japanese Government Scholarship (1994). He earned his habilitation degree in 2007 and was awarded the title of Professor in 2014. Professor Jankowski's research focuses on earthquake engineering , structural pounding , and dynamics of metal structures . His work addresses critical challenges in structural safety during seismic events, developing innovative control techniques for buildings and bridges. He has pioneered research in structural shape and stress control, base isolation systems, and the application of machine learning in predicting structural behavior. His extensive publication record demonstrates a strong trend toward integrating computational methods and artificial intelligence with traditional structural engineering. Recent work explores machine learning applications for predicting concrete properties, optimal control systems for seismic response mitigation, and the behavior of innovative composite structural elements. Nagroda indywidualna Rektora Politechniki Gdańskiej za osiągnięcia naukowe (multiple years) Nagroda Naukowa im. W. Nowacki za wybitne osiągnięcia naukowe (2015) Medal Komisji Edukacji Narodowej (2011) Brązowy Krzyż Zasługi (2006) Stypendium Rządu Japonii na studia doktoranckie (1994) As an academic advisor, he has successfully supervised eight PhD students to completion, with research focusing on structural dynamics and earthquake engineering. He has led significant research projects including "GASŁUPMIKROS" on microseismic monitoring technology for hydraulic fracturing processes. Professor Jankowski actively contributes to the academic community through his leadership roles in the Doctoral School and the Department of Building Engineering, where he shapes the next generation of civil engineers and researchers in structural dynamics and earthquake-resistant design.

Nestor Matthewsمشاهده پروفایل

استاد

• Visual Perception
• Temporal Processing
• Motion Perception
• +۵ مورد دیگر

Nestor Matthews is a Professor in the Department of Psychology at Denison University, where he has served since 2001, progressing from Assistant Professor to Associate Professor and finally to Professor in 2016. He maintains affiliations with both the Neuroscience Program and Data Analytics Program at Denison University. His research focuses on visual perception, temporal processing, and perceptual learning, with particular expertise in motion perception, simultaneity judgments, and visual attention mechanisms. Dr. Matthews received his Ph.D. in Experimental Psychology from Brown University in 1997, following an M.Sc. in 1995 and a B.A. in Psychology (Summa Cum Laude With Honors) from Fairleigh Dickinson University in 1993. Prior to joining Denison, he completed a Postdoctoral Research Fellowship at Columbia University's Center for Neurobiology & Behavior from 1997-2001. Matthews' research program investigates fundamental aspects of visual perception and temporal processing. His work has established important findings regarding visual field asymmetries in temporal processing, demonstrating left visual field advantages in simultaneity judgments and right hemifield deficits. He has made significant contributions to understanding the relationship between motion perception and temporal processing through studies of expert populations like drum corps performers. His research on perceptual learning has revealed how practice can hasten visual attention and improve sensitivity to orientation, speed, and temporal order. Matthews employs rigorous psychophysical methods to explore how the visual system processes information across space and time, with implications for understanding both basic visual mechanisms and potential applications in visual rehabilitation. His publication record shows a consistent trajectory of high-impact research in visual perception, with recent work expanding into areas such as sentence length perception and cross-modal interactions. A notable pattern in his work is the application of rigorous psychophysical methods to investigate fundamental questions about visual processing, often using expert populations to reveal specialized perceptual abilities. His research bridges cognitive psychology, neuroscience, and perceptual learning, contributing to our understanding of how experience shapes visual processing capabilities. Denison University 2014 Charles A. Brickman Teaching Excellence Award NIH National Research Service Award (F32 EY06969-01A1) NIH Research Training Program grant (MH15174-22) H-Index of 13 with 454 citations across 108 journals Multiple Denison University Research Foundation grants Dr. Matthews has mentored numerous undergraduate researchers through Denison's summer research program and directed studies. His supervision spans topics including timing sensitivity in vision and audition, visual temporal order judgments in athletes, right visual field deficits on RSVP tasks, and the effects of practice on visual attention. He has served as faculty advisor for the Denison University Chapter of Nu Rho Psi (The Neuroscience Honor Society) and N.E.R.D (Neuroscience Education & Research at Denison). His commitment to undergraduate research is further evidenced by his service as Secretary and Councilor for the Psychology Division of the Council on Undergraduate Research (CUR). At Denison, Matthews has held leadership positions including Chair of the Department of Psychology (2018-2021), Chair of the Neuroscience Program (multiple terms), and Chair of the Denison University Faculty (2014-2015). He has been actively involved in curriculum development, particularly in statistics education and the integration of research methods throughout the psychology curriculum. His teaching portfolio includes courses in Sensation & Perception, Advanced Statistics, Research Methods, Psychology of Music, and Neuroscience capstone seminars. He has also contributed to the development of the Data Analytics program at Denison, teaching Introduction to Data Analytics.

Garrett Granrothمشاهده پروفایل

پژوهشگر

• Magnetic excitations
• Lattice dynamics
• Neutron scattering
• +۶ مورد دیگر

Garrett Granroth is a Neutron Scattering Scientist at Oak Ridge National Laboratory (ORNL), working on the ARCS direct geometry spectrometer at the Spallation Neutron Source. His research focuses on studying magnetic and lattice excitations in various material systems using neutron scattering techniques. Dr. Granroth's research interests include: Exploiting event data in neutron scattering experiments Monte Carlo Ray tracing for revealing subtle sample effects Advanced computational techniques for neutron data analysis Neutron instrument design Quantum and low dimensional magnetic systems Disordered magnetic systems Neutron scattering measurements in high magnetic fields His recent publications demonstrate expertise across multiple domains including condensed matter physics, materials science, neutron instrumentation, and computational methods. His work spans fundamental research on quantum materials and magnetism to practical advancements in neutron scattering instrumentation and data analysis techniques. A significant portion of his recent work focuses on developing computational tools and simulation methods to enhance neutron scattering capabilities, including GPU acceleration and Monte Carlo ray-tracing approaches. Dr. Granroth has contributed to numerous publications in high-impact journals including Physical Review B, Nature Communications, PNAS, and The Journal of Physical Chemistry Letters, reflecting the interdisciplinary nature of his research. His technical expertise includes: Monte Carlo simulation for neutron instrumentation GPU acceleration for neutron data analysis Development of software platforms for neutron scattering data interpretation Advanced manufacturing techniques for neutron instrumentation components

• Teacher Education
• Educational Policy
• School Leadership
• +۳ مورد دیگر

Eve Eisenschmidt is a Professor at Tallinn University's School of Educational Sciences since 2016. Previously, she served as Vice-Rector for Development (2011-2016) and Director of Haapsalu College (1998-2011) at Tallinn University. She holds a Doctor's Degree in Educational Sciences (2006) and a Master's Degree (2000), both from Tallinn University institutions. Her educational background includes: Doctoral studies in Educational Sciences at Tallinn University (2002-2006) Master's studies at Tallinn Pedagogical University (1998-2000) Diploma in primary school teaching from Tallinn Pedagogical Institute (1983-1988) Eisenschmidt specializes in teacher education, school leadership, and educational policy. Her research focuses on school improvement processes, teacher development, and data-driven educational decision making. She has extensively studied Estonian educational reforms, particularly the transition from the Soviet system to high-performing models reflected in PISA results. Her work often employs comparative approaches, especially between Estonia and Finland. Eisenschmidt's recent scholarship reveals strong patterns in examining how educational institutions implement evidence-based practices, develop professional learning communities, and navigate crises like the pandemic. Her work consistently bridges theoretical frameworks with practical implementation challenges in the Estonian context, while maintaining valuable cross-national comparisons with Finland. Her significant contributions to Estonian education have been recognized with: Order of the White Star, 4th Class (2024) Professor Eisenschmidt has held numerous influential positions in educational governance, including serving as Chairman of the Quality Assessment Council for Higher Education in Estonia (2018-2024) and currently as Vice Chair of Tallinn University Council (2025-present). She has advised on multiple national educational quality assurance initiatives and participated in EU educational programs including ESF programme Eduko and Comenius programme. Her leadership extends to international educational networks as a longstanding member of the Teacher Education Policy in Europe Network (TEPE) and the European Association for Research on Learning and Instruction (EARLI), where she contributes to shaping educational research and policy across Europe.

Andrea Mentrelliمشاهده پروفایل

دانشیار

• Extended Thermodynamics
• Mathematical Physics
• Hyperbolic Equations
• +۴ مورد دیگر

Andrea Mentrelli is an Associate Professor of Mathematical Physics at the Department of Mathematics, University of Bologna, and Scientific Coordinator of the Alma Mater Research Center on Applied Mathematics (AM²). He holds a MS in Nuclear Engineering and a PhD in Mechanics of Materials from the University of Bologna. His research focuses on Extended Thermodynamics, High-Performance Computing (HPC), and computational modeling of plasma and fluid dynamics. Key roles include leadership in projects such as PRIN2022 (Mathematics of Nonlinear Wave Propagation) and Combustion-Wave Interactions. He is affiliated with INFN Bologna Section and AM². Notable contributions include shock wave analysis, hyperbolic models for incompressible fluids, and fractional calculus applications in viscoelasticity. His work combines theoretical rigor with numerical methods, addressing challenges in plasma physics, astrophysics, and materials science. **Education:** MS in Nuclear Engineering, PhD in Mechanics of Materials (University of Bologna). **Affiliations:** AM², INFN Section Bologna. **Research Projects:** Over 10+ grants including PRIN2022 and EU-funded initiatives. Extensive publications in journals like *Phys. Rev. D*, *Annals of Physics*, and *Journal of Computational Physics*.

Michelle Michelleمشاهده پروفایل

استادیار مهمان

• Numerical Methods for Wave Propagation
• Wavelet Methods for PDEs
• Scientific Machine Learning
• +۳ مورد دیگر

Michelle Michelle is a Golomb Visiting Assistant Professor of Mathematics at Purdue University, mentored by Jianlin Xia and Haizhao Yang. She holds a PhD in Applied Mathematics from the University of Alberta (2022), advised by Bin Han and Yau Shu Wong. Her research focuses on numerical methods for wave propagation, elliptic interface problems, wavelet methods in PDEs and data science, and scientific machine learning. She has taught courses including MA 303 and MA 266 at Purdue and held teaching roles at the University of Alberta. Her research explores advanced numerical techniques for solving Helmholtz equations and electromagnetic scattering problems, with notable contributions to wavelet-based Galerkin methods and stable fast multipole algorithms. Recent work emphasizes high-order accuracy, stability analysis, and applications in computational physics. Michelle has presented at major conferences such as SIAM Applied Linear Algebra (2024) and SIAM Great Lakes Section (2022), focusing on numerical methods for wave equations and algorithmic stability. She has also contributed to workshops like the NSF Computational Mathematics PI Meeting (2024) and ICERM Modern Applied and Computational Analysis (2023). Her publications span journals like SIAM Journal on Numerical Analysis and Applied and Computational Harmonic Analysis.

Robyn Macdonaldمشاهده پروفایل

استادیار

• Hypersonic Flows
• Chemically Reacting Flows
• Chemical Kinetics
• +۴ مورد دیگر

Robyn Macdonald is an Assistant Professor in the Department of Aerospace Engineering Sciences at the University of Colorado Boulder, affiliated with the Aerospace Mechanics Research Center (AMREC). She holds a PhD and BS in Aerospace Engineering from the University of Illinois at Urbana-Champaign (2019 and 2013, respectively). Her research focuses on predictive modeling of hypersonic flows, including gas-phase chemistry modeling, turbulent flow dynamics, and laminar-to-turbulent transition predictions. She employs computational tools ranging from molecular dynamics to high-fidelity CFD simulations to study hypersonic vehicles' flowfields. Key research interests include hypersonic flows, chemically reacting flows, chemical kinetics, and radiation modeling. Her lab develops reduced-order models and advanced simulation techniques to address challenges in non-equilibrium aerothermodynamics and high-speed aerodynamics. Educations: PhD in Aerospace Engineering, University of Illinois at Urbana-Champaign, 2019 BS in Aerospace Engineering, University of Illinois at Urbana-Champaign, 2013 Dr. Macdonald has received prestigious awards such as the NASA Early Career Faculty Fellow (2023), Air Force Office of Scientific Research Young Investigator Program (2021), and the President’s Postdoctoral Fellowship at the University of Minnesota (2019–2020). Her work bridges fundamental physics and engineering applications, with contributions to hypersonic vehicle design and aerothermal environment modeling. Her lab actively explores computational methods for hypersonic boundary layer transition, compressible LES modeling, and energy transfer processes in hypersonic flows. She collaborates on predictive tools for next-generation aerospace systems, emphasizing both academic and defense-related applications.

• Numerical Analysis
• Computational Fluid Dynamics
• Mathematical Modeling
• +۴ مورد دیگر

Jacques Rappaz is a Professor Emeritus at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences (SB) . His primary role is within the Department of Mathematics (MATH) , and he also serves as a Scientific Consultant in the GR-PI Group . Rappaz’s research focuses on numerical analysis, computational fluid dynamics, and mathematical modeling of complex physical systems such as glacier dynamics, magnetohydrodynamics (MHD), and industrial processes like aluminum electrolysis. He has advised numerous PhD students and contributed to advancements in finite element methods, phase-field modeling, and turbulence simulation. Education & Academic Background: While detailed educational history is not explicitly stated, his long-standing position at EPFL and extensive publication record in applied mathematics and engineering suggest a strong academic trajectory in computational sciences. He holds an honorary professorship, reflecting his contributions to mathematics and engineering disciplines. Research Interests: Numerical analysis of PDEs and nonlinear systems Modeling multiphase flows and phase transitions Glaciology dynamics and climate impact studies Magnetohydrodynamic phenomena in industrial contexts Finite element methods for engineering applications Advising & Collaborations: Rappaz has supervised multiple PhD students specializing in computational mechanics and applied mathematics. His work integrates interdisciplinary approaches, collaborating with engineering teams on projects like aluminum production optimization and glacier motion simulation. Labs & Teams: Active within the GR-PI Group (likely related to plasma or industrial processes) and the PH-SB unit, focusing on applied physics and mathematical modeling.

Thomas Eibertمشاهده پروفایل

استاد

• High-Frequency Engineering
• Electromagnetic Modeling
• Antenna Design
• +۳ مورد دیگر

Prof. Thomas Eibert holds the position of Full Professor of High-Frequency Engineering at the Technical University of Munich (TUM), part of the TUM School of Computation, Information and Technology. His academic journey includes a doctorate from the University of Wuppertal (1997), research at the University of Michigan (USA), and roles at Deutsche Telekom's Technology Center and the Fraunhofer Institute. Specializing in high-frequency electromagnetic processes, his research focuses on novel antenna technologies, electromagnetic modeling algorithms, and field transformation techniques. Key contributions include advancements in near-field imaging, inverse source solutions, and microwave sensor systems. Notable awards include the Best Measurement Paper Award at EuCAP (2020, 2019), the ESA Antenna Workshop's Best Innovative Paper (2013), and the VDE ITG Literature Prize (2008). His work bridges theoretical developments with practical applications in automotive antennas, radar systems, and environmental impact analysis of antenna performance. Current research emphasizes UAV-based measurement systems and low-frequency stabilization methods for electromagnetic simulations. Education: Doctorate in Electrical Engineering (University of Wuppertal, 1997) Professional Roles: Head of Fraunhofer Institute's Antennas and Scattering Department (2002–2005), Professor at University of Stuttgart (2005–2008) Research Groups: Active in the Department of Electrical Engineering, collaborating on projects involving radar imaging, antenna design, and electromagnetic field transformations. Publications span journals such as IEEE Transactions on Antennas and Propagation, focusing on topics like phase retrieval, decoupling networks, and frequency-selective surfaces. His lab develops practical measurement frameworks for large-scale antenna systems and environmental resilience testing.

Cornel Sultanمشاهده پروفایل

استاد

• Tensegrity Structures
• Rotorcraft Modeling and Control
• Planning and Coordination
• +۳ مورد دیگر

Cornel Sultan is a Professor in the Department of Aerospace & Ocean Engineering at Virginia Tech. His research focuses on Tensegrity Structures, Rotorcraft Modeling and Control, and Energy Harvesting. He earned his Ph.D. in Aerospace Engineering from Purdue University. Sultan has advised numerous Ph.D. students, with recent graduates contributing to academia and industry. He holds awards including AIAA Associate Fellow (2017) and Fellow of Virginia Tech’s College of Engineering (2016). Education: Ph.D., M.S. (Applied Mathematics) from Purdue University; B.S./M.S. from Polytechnic University, Romania. Research Interests: Tensegrity Structures (prestressable, dynamics, membrane integration); Rotorcraft Control (helicopter models, shipboard operations); Energy Harvesting (ocean current turbines, vibration energy). Key publications include works on tensegrity deployment, helicopter control algorithms, and ocean energy systems. His work spans theoretical mechanics, control theory, and applied engineering solutions. Professional Contributions: Technical chairs for AIAA conferences (2015-2016), member of AIAA Guidance, Navigation, and Control Technical Committee. Grants supported by NSF and ONR, focusing on hydrokinetic power systems and structural dynamics. Labs/Teams: Active in Virginia Tech’s aerospace robotics and autonomous systems research, collaborating on projects like ocean current turbine arrays and tensegrity-based robotics.