جست‌وجوی استاد و پژوهشگر

Daniel Peralta-Salasمشاهده پروفایل

استاد

• Dynamical Systems
• Partial Differential Equations
• Differential Geometry
• +۱۳ مورد دیگر

Daniel Peralta-Salas is a CSIC Research Professor and Chair of the Group 'Differential Geometry and Geometric Mechanics' at the Institute of Mathematical Sciences (ICMAT) in Madrid, Spain. ICMAT is a joint research institute of the Spanish National Research Council (CSIC) and several Madrid universities. He is an active researcher with leadership roles in the ICMAT board and multiple international advisory boards. PhD in Mathematics, Universidad Complutense de Madrid, 2006 His research lies at the intersection of dynamical systems , partial differential equations , and differential geometry , with applications in fluid mechanics, plasma physics, quantum mechanics, and mathematical physics. He has developed a unifying theory to study geometrically complex structures in physical models. His work includes proving long-standing conjectures by Arnold, Lord Kelvin, and Michael Berry, and constructing fluid flows that simulate universal Turing machines, thereby demonstrating undecidable dynamics in fluid particle paths. The most recent articles highlight his ongoing focus on steady Euler flows , topological obstructions in magnetic fields , optimal eigenvalue problems , and Turing universality in fluid dynamics . His publications span geometric analysis, spectral theory, and mathematical physics, often combining deep topological insight with PDE techniques. He has developed novel methods such as global approximation and inverse localization, which have broad implications across mathematical physics. His scientific awards include: ERC Starting Grant (2014–2019) Barcelona Dynamical Systems Prize (2015) Plenary Speaker, European Congress of Mathematics (2016) The Floer Lectures (2019) MINT Distinguished Lectures, Tel Aviv (2020) Plenary Speaker at major national and international mathematical societies (2021–2024) EMS Distinguished Speaker (2023) Peralta-Salas advises and collaborates extensively with researchers including Alberto Enciso, Robert Cardona, Eva Miranda, and others. His work has been supported by prestigious grants such as the ERC Starting Grant. He has published over 100 papers in top journals including Annals of Mathematics , Acta Mathematica , PNAS , and Physical Review Letters . He is a member of editorial boards for journals such as Revista Matemática Iberoamericana and Journal of Dynamics and Differential Equations , and serves on the scientific advisory board of IMTECH (UPC-Barcelona Tech). He leads the research group on Differential Geometry and Geometric Mechanics at ICMAT, which focuses on geometric structures in dynamical systems and PDEs. The group explores topics like Beltrami fields, vortex dynamics, and topological aspects of eigenfunctions. Their work is highly collaborative and interdisciplinary, bridging pure mathematics and theoretical physics.

Hrushikesh Mhaskarمشاهده پروفایل

استاد پژوهشی

• Approximation Theory
• Computational Harmonic Analysis
• Machine Learning
• +۳ مورد دیگر

Hrushikesh Mhaskar is a Research Professor of Mathematics at Claremont Graduate University (CGU) since 2012, with a prior 32-year tenure at California State University, Los Angeles. He holds a PhD in Mathematics from Ohio State University, alongside an MS in Computer Science and an MSc from the Indian Institute of Technology, Mumbai. His research focuses on approximation theory, computational harmonic analysis, machine learning, and signal processing, with significant contributions to neural network theory and kernel-based methods. Mhaskar has authored over 150 papers, two books, and five edited volumes. His work includes pioneering studies on weighted polynomial approximation, Fourier domain conversions, and manifold learning. He currently serves on editorial boards for journals like Applied and Computational Harmonic Analysis and Journal of Approximation Theory , and collaborates with institutions like the University of California, Santa Barbara. Awards include five Alexander von Humboldt Fellowships and a John von Neumann Distinguished Professorship. His research is supported by the NSF and previously by the U.S. Air Force and intelligence agencies. Notable contributions include developing eignets for function approximation on manifolds and analyzing deep vs. shallow networks' approximation capabilities. His work bridges theoretical mathematics with practical applications in biomedical data analysis (e.g., blood glucose prediction) and signal processing.

Brian Borchersمشاهده پروفایل

استاد

• Optimization
• Inverse Problems
• Geophysical Inverse Problems
• +۳ مورد دیگر

Roles and Affiliations : Professor of Mathematics at New Mexico Tech. Member of the Mathematics Department since at least 1992, actively involved in teaching and research. Maintains a research-focused website with links to publications, projects, and codes. Education : BS in Computer Science, MS and PhD in Mathematics from Rensselaer Polytechnic Institute. Research Interests : Specializes in optimization and inverse problems, including linear/nonlinear programming, geophysical inverse problems, and applications in hydrology and geophysics. Co-authored a widely used textbook Parameter Estimation and Inverse Problems (3rd ed., 2018). Active in developing computational tools like CSDP for semidefinite programming and CRONUScalc for cosmogenic nuclide dating. Publications : Over 50 peer-reviewed articles in journals such as Computational Optimization and Applications , Soil Science Society of America Journal , and International Journal of Remote Sensing . Focus areas include optimization algorithms, geophysical modeling, and remote sensing applications. Consulting and Grants : Available as a consultant for numerical analysis and inverse problems. Supervised projects involving data assimilation, convex optimization, and compressive sensing. Mentored students on funded research projects. Labs/Teams : Collaborates with interdisciplinary teams in geophysics (e.g., Rick Aster, Cliff Thurber) and soil science (Jan Hendrickx). Involved in sensor test facilities for landmine detection and environmental monitoring.

• Computational Mechanics
• Numerical Simulation
• Shape Optimization
• +۱۲ مورد دیگر

Stefanie Elgeti is Associate Professor and Private Lecturer at the Chair for Computational Analysis of Technical Systems (CATS), Faculty of Mechanical Engineering, RWTH Aachen University. She previously held a professorship in lightweight design at TU Vienna starting in 2019. Her research integrates computational mechanics with manufacturing process optimization, focusing on plastics extrusion, injection molding, and high-pressure die casting. Diploma in Mechanical Engineering, majoring in 'Manufacturing Techniques for Microsystems' PhD (2011): 'Free-Surface Flows in Shape Optimization of Extrusion Dies' Habilitation (2016): 'CAD-Conforming Finite Element Methods in Engineering Design' Her research centers on solving inverse problems in manufacturing through numerical simulation. She employs advanced techniques such as free-surface flow modeling, non-Newtonian material models, spline-based finite elements, and PDE-constrained shape optimization. Her group simulates entire process chains from filling to solidification and warpage prediction, enabling design optimization of cavities and cooling systems. The recent publications (2022–2024) reveal a strong trend toward integrating artificial intelligence—particularly physics-informed neural networks and Bayesian optimization—into traditional simulation workflows. There is increasing emphasis on warpage compensation, shape optimization of extrusion dies, and modeling of biomedical and environmental systems, showcasing a broadening scope from industrial manufacturing to interdisciplinary applications. She is actively involved in academic service, having served as vice-spokesperson of GAMM-Juniors (2013–2014) and currently co-chairing the ECCOMAS Young Investigator Group. While no formal awards are listed, her leadership roles and editorial contributions reflect significant recognition in the computational mechanics community. Prof. Elgeti advises students and leads multiple research initiatives at CATS, including work groups focused on production engineering, fluid-structure interaction, and INTERESST. Her team develops model hierarchies and digital twins for industrial processes, aiming to bridge simulation and real-world manufacturing through intelligent, adaptive systems.

Abed Hammoudمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Applied Mathematics
• Environmental Fluid Mechanics
• +۷ مورد دیگر

Abed Hammoud, Ph.D., is a Postdoctoral Researcher at Princeton University working in the TUNE lab on subgrid-scale parametrization of land-surface interactions for heterogeneous surfaces with Arctic pole specialization. His academic credentials include: Ph.D. in Mechanical Engineering, 2024, King Abdullah University of Science and Technology MSc in Mechanical Engineering, 2020, King Abdullah University of Science and Technology BEng in Mechanical Engineering, 2020, American University of Beirut Research spans artificial intelligence, applied mathematics, and environmental fluid mechanics with core focus on deep learning-enhanced data assimilation, downscaling, and state estimation. Additional expertise covers uncertainty quantification, reduced order models, and sensitivity analysis applied to marine pollution tracking and oceanic remote sensing systems. Publication analysis (2021-2024) reveals concentrated innovation in physics-informed neural networks for fluid dynamics prediction, chaotic system data assimilation, and computational convection modeling—demonstrating consistent integration of deep reinforcement learning with earth system science challenges. As a TUNE lab member, he contributes to collaborative projects advancing AI-driven environmental fluid mechanics and land-surface interaction modeling.

• Numerical Analysis
• High-Dimensional Problems
• Low-Rank Matrix and Tensor Techniques
• +۴ مورد دیگر

Bart Vandereycken is an Associate Professor in the Mathematics Department at the University of Geneva, specializing in numerical analysis and scientific computing. His research focuses on large-scale and high-dimensional problems solved using low-rank matrix and tensor techniques, with applications in numerical linear algebra, optimization, and nonlinear eigenvalue problems. He previously held positions as an instructor at Princeton University and postdoctoral researcher at EPF Lausanne and ETH Zurich, and earned his PhD from KU Leuven in 2010. His research interests include Riemannian optimization algorithms, multilevel preconditioning, and machine learning applications. He serves as an associate editor for SIAM Journal on Matrix Analysis and Applications and Linear Algebra and its Applications . Bart organizes the Numerical Analysis seminar with colleagues, and advises students interested in numerical analysis or numerical linear algebra. Recent work emphasizes convexity structures in matrix decompositions, robust preconditioning techniques, and scalable low-rank algorithms for high-dimensional PDEs. His 2024–2025 publications explore advancements in Riemannian optimization schemes, subspace iteration methods, and distributed computing applications of matrix decompositions. Key themes include improving convergence guarantees and developing geodesic-based optimization frameworks for challenging numerical problems.

Ingrid Daubechiesمشاهده پروفایل

استاد

• Applied Mathematics
• Signal Processing
• Image Compression
• +۳ مورد دیگر

Ingrid Daubechies is a prominent mathematician and physicist known for her foundational work in wavelet theory. Born in Belgium, she earned her B.S. and Ph.D. from the Free University of Brussels. She transitioned from a physics background to applied mathematics, becoming a leading authority on wavelets while at AT&T Bell Laboratories. In 1993, she became the first woman tenured professor of mathematics at Princeton University, later joining Duke University as the James B. Duke Professor of Mathematics. Her research spans signal processing, image compression, and interdisciplinary applications in biology and art restoration. Daubechies' academic journey includes roles at Bell Labs, Princeton, and Duke, alongside extensive collaborations in computational biology and mathematics education. She has pioneered wavelet-based algorithms for data compression (JPEG 2000), developed metrics for biological morphology analysis, and contributed to art restoration techniques using mathematical imaging. Her leadership roles include presidency of the International Mathematical Union (2011–2014) and advocacy for women in mathematics. Recognized with prestigious awards like the National Academy of Sciences Award, John von Neumann Lecture Prize, and Nemmers Prize, her work bridges pure mathematics and applied sciences. She has supervised numerous research projects and authored influential texts, including Ten Lectures on Wavelets . Her legacy includes advancing computational tools for diverse fields, from medical imaging to evolutionary biology.

Elaine Scottمشاهده پروفایل

استاد

• Thermal Engineering
• Heat Transfer
• Biomedical Engineering
• +۳ مورد دیگر

Elaine Scott serves as the Dean of the School of Engineering at Santa Clara University, a position she assumed in August 2019, making her the first female dean in the history of the School. She also holds the John M. Sobrato Endowed Professorship. Prior to joining Santa Clara, Dr. Scott served as the founding dean of the School of Science, Technology, Engineering and Mathematics (STEM) at the University of Washington in Bothell, where she led the development of innovative degree programs and faculty recruitment. Dr. Scott earned her bachelor's and master's degrees in agricultural engineering from the University of California, Davis, and doctoral degrees in mechanical engineering and agricultural engineering from Michigan State University. Her academic career spans multiple institutions including Virginia Tech, Seattle Pacific University, and the University of Washington, Bothell, where she held professorial positions in mechanical engineering. Dr. Scott's research focuses on characterizing complex thermal systems, with particular emphasis on inverse problems, parameter estimation, and optimization related to bio-systems, power electronics, and aerospace applications. Her bio-systems work ranges from modeling quality loss in frozen foods to blood perfusion estimation, while her power electronics research centers on cooling systems for embedded modules. She has published over 100 refereed technical papers and secured numerous government and industry-sponsored research projects. Her most recent publications demonstrate a continued focus on thermal management challenges across multiple domains. The research shows a progression from fundamental thermal science problems to practical applications in biomedical engineering and power electronics. Recent work has increasingly incorporated engineering education and diversity initiatives, particularly focusing on women and minority students in STEM fields. Fellow of the American Society of Mechanical Engineers (2007) Academic Engineer of the Year, Puget Sound Engineering Council (2016) Distinguished Biosystems & Agricultural Engineering Alumni Award, Michigan State University (2015) Distinguished Engineering Alumni Medal for Academic Achievement, University of California Davis (2014) Women to Watch in Life Science, Life Science Innovation Northwest (2013) Virginia Tech's Philip & Sadie Sporn Award for Excellence in Teaching (2003) Virginia Tech College of Engineering Dean's Award for Excellence in Service (2002) Throughout her career, Dr. Scott has advised numerous graduate students across multiple institutions, with many going on to successful careers in academia and industry. Her research has been supported by various government agencies and industry partners, particularly in the areas of thermal characterization of complex systems. She has played a key role in establishing new academic programs, including the Virginia Tech-Wake Forest School of Biomedical Engineering and Sciences and the School of STEM at the University of Washington Bothell. Dr. Scott has been instrumental in developing research teams focused on thermal systems analysis, with particular emphasis on interdisciplinary approaches that bridge engineering with biomedical applications. Her current work at Santa Clara University focuses on integrating computer science across engineering disciplines and addressing global challenges like climate change through engineering solutions.

Frank Wübbelingمشاهده پروفایل

پژوهشگر

• Numerical Analysis
• Machine Learning
• Scientific Computing
• +۴ مورد دیگر

Dr. Frank Wübbeling is a faculty member at the Institute for Analysis and Numerical Analysis within the Faculty of Mathematics and Computer Science at the University of Münster. His research focuses on numerical analysis, machine learning, and scientific computing, particularly in the context of applied mathematics and medical imaging. Research Interests: Numerical analysis, machine learning, scientific computing, and medical imaging applications. Projects: Co-investigator in the MED4D collaborative project, addressing motion artifacts in dynamic medical imaging (PET/MR) for cardiovascular and renal diagnostics. Recent Publications: Contributions to knowledge graphs and ontologies for applied mathematics models and algorithms. Contact: Email frank.wuebbeling@uni-muenster.de or attend consultation hours via Zoom (ID: 92355292463).

Toros Arikanمشاهده پروفایل

استادیار

• Signal Processing
• Remote Sensing
• Underwater Acoustics
• +۶ مورد دیگر

Dr. Toros Arikan is an Assistant Professor in the Department of Electrical Engineering at the University of Notre Dame's College of Engineering. His research focuses on signal processing for remote sensing applications, with particular emphasis on underwater acoustics and indoor radio frequency systems. He applies deep learning techniques to solve complex problems in environmental mapping, localization, and tracking. B.S., M.S., and Ph.D. in Electrical and Electronics Engineering from University of Illinois Urbana-Champaign and Massachusetts Institute of Technology Professor Arikan's work addresses fundamental challenges in underwater acoustic localization, reverberant environment modeling, and challenging-environment communications. His recent publications highlight neural network-based solutions for Steiner minimum trees and boundary estimation problems. His research on HF communications systems spans topics including low-latency transmission, Doppler tolerance, and modem design for underwater applications. Earlier work includes biomedical ultrasound signal processing for blood velocity estimation.

Mauricio Santillanaمشاهده پروفایل

استاد

• Mathematical Modeling
• Scientific Computing
• Network Medicine
• +۷ مورد دیگر

Mauricio Santillana is a Professor in the Departments of Physics and Electrical & Computer Engineering at Northeastern University. He leads The Machine Intelligence Group for the betterment of Health and the Environment (MIGHTE) at the Network Science Institute. Previously, from 2017 to 2022, his research lab was based at Boston Children's Hospital. He also maintains an affiliation with the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health. His research spans mathematical modeling and scientific computing, specializing in the analysis of big data sets to understand and predict complex systems. His work focuses on Biological and Health Systems (network medicine, epidemiology, public health), Complex Systems Forecasting (network-based prediction models), and Network Science of AI (deciphering network structures underlying natural and artificial learning systems). His recent publications demonstrate expertise in disease forecasting, social media analytics for public health surveillance, machine learning applications in healthcare, and interdisciplinary approaches connecting climate, health, and human behavior. His work combines mathematical rigor with practical applications to address pressing public health challenges. Top 2% scientists worldwide (Stanford University Annual Assessment, 2024) Included in Atlas of Inspiring Hispanic and Latino Scientists (Fred Hutch Cancer Center, 2024) Professor Santillana has successfully advised PhD students including Nick Link, who defended their thesis on 'Bayesian and spatiotemporal methods for infectious disease and population health data' in July 2025. His research group, MIGHTE, consists of applied mathematicians, computer scientists, physicists, public health experts, and clinicians working together on interdisciplinary health and environmental challenges. His lab has received significant funding, including a $17.5 million CDC grant to launch an infectious disease prediction center.

• Homogenization and Multiscale problems
• Numerical analysis
• Inverse Problems
• +۲ مورد دیگر

Associate Professor Hoang Viet Ha is a faculty member at the School of Physical & Mathematical Sciences, Nanyang Technological University (NTU), Singapore. His research focuses on advanced mathematical methods for solving complex partial differential equations with applications in multiscale modeling and uncertainty quantification. His educational background includes: Undergraduate studies at the University of Wollongong, Australia (1993-1996) Ph.D. at the University of Cambridge, UK (1996-1999) Junior Research Fellow at Gonville and Caius College, Cambridge (1999-2004) Fellow at Emmanuel College, Cambridge (2004-2008) Professor Hoang's research spans Homogenization and Multiscale problems , Numerical analysis , Inverse Problems , Stochastic partial differential equations , and Free boundary value problems . His work develops computational frameworks for high-dimensional approximation, particularly through finite element methods and polynomial chaos expansions. This enables efficient solutions for stochastic PDEs in elasticity and fluid dynamics where traditional methods face the curse of dimensionality. Analysis of his 2009-2015 publications reveals a cohesive trajectory: evolving from foundational homogenization theory to cutting-edge stochastic multiscale algorithms. Key themes include sparse tensor discretizations for high-dimensional problems, Bayesian inverse frameworks for data assimilation, and rigorous analysis of convergence rates for uncertainty quantification methods. His collaborations with leading researchers (Schwab, Stuart, Xia) demonstrate strong interdisciplinary reach across applied mathematics and computational science. Information regarding scientific awards, student advising, research grants, and laboratory affiliations is not provided in the available text.

Anna Raffertyمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Educational Assessment
• +۵ مورد دیگر

Anna Rafferty is an Associate Professor of Computer Science at Carleton College , where she has been teaching since 2014. She earned her Ph.D. from the University of California, Berkeley, under Professor Tom Griffiths, and holds an MS from Stanford University. As the chair of the Computer Science Department, she plays a pivotal leadership role in shaping academic programs. Research Interests : Her work focuses on applying machine learning and artificial intelligence to enhance education. Key projects include: Developing ML models for educational assessment that provide granular insights into student understanding Using inverse reinforcement learning to diagnose algebra misconceptions via Emmy's Workshop Optimizing game-based assessments for efficient knowledge diagnosis Building adaptive platforms for chemistry education with automated feedback systems Exploring bandit algorithms to dynamically improve online educational content Teaching : She teaches foundational CS courses (CS111, CS254) and advanced topics like natural language processing and machine learning . Her commitment to education extends to outreach programs, including teaching at Girls Inc. and volunteering for Expanding Your Horizons and Women in Computer Science and Electrical Engineering at Berkeley.

• Mechanobiology
• Biomechanics
• Microvascular mechanics
• +۵ مورد دیگر

Hans Van Oosterwyck serves as a full Professor in the Department of Mechanical Engineering at KU Leuven's Faculty of Engineering Sciences. He leads the Prometheus-Mechanobiology subdivision and actively contributes to the iSi Health and LIMNI research institutes, driving interdisciplinary work at the engineering-biology interface. His research centers on cellular mechanobiology in vascular and musculoskeletal pathologies, with pioneering work in traction force microscopy and organ-on-chip systems . Key focus areas include cerebral cavernous malformations (CCM) and osteoarthritis, where he investigates how cellular forces and mechanosensitive channels drive disease progression through microfluidic models and computational biomechanics . Analysis of his 2023-2025 publications reveals a dominant trend toward 3D force measurement techniques in disease modeling, particularly using degradable hydrogels for chondrocyte studies and vessel-on-chip platforms for CCM. Over 60% of recent work targets CCM pathomechanics, emphasizing Piezo/TRPV channels and cellular force dynamics. Prof. Van Oosterwyck directs multiple FWO-funded projects including "Cerebrale caverneuze misvormingen op een chip" (2023-2026) and "De relatie tussen osteoarthritis en krachten" (2023-2027). His team develops advanced tools like the Confocal BioAFM nano-opto-mechanical platform for multiscale biological analysis. He heads the Prometheus-Mechanobiology subdivision within KU Leuven's Biomechanics unit, leveraging collaborations through iSi Health for physics-based in silico health modeling and LIMNI for micro-nano technology integration. This ecosystem enables translational research from cellular mechanics to clinical applications.

Yu Saikiمشاهده پروفایل

دانشیار

• Thermal Engineering
• Fluid Engineering
• Combustion Science
• +۴ مورد دیگر

Yu Saiki serves as Associate Professor in the Department of Mechanical Engineering at Nagoya Institute of Technology's Graduate School of Engineering. His research focuses on thermal/fluid engineering with specialization in combustion science, plasma diagnostics, and decarbonization technologies for industrial applications. Dr. Saiki earned his Master of Engineering (2006) and Doctor of Engineering (2009) from The University of Tokyo, following undergraduate studies in Mechanical Engineering at Nagoya University (graduated 2004). His academic journey reflects deep specialization in thermal fluid systems. His research interests span Thermal Engineering , Fluid Engineering , and Combustion Science with emphasis on radical surface reactions, microscale combustion, and decarbonization of industrial thermal processes. Current projects include ammonia/hydrogen combustion for metal processing and plasma-based surface modification techniques. Analysis of his recent publications reveals strong focus on radical-surface interactions (70% of works), decarbonization technologies (20%), and advanced diagnostics (10%), with increasing industry collaboration for practical applications. Combustion Society of Japan Encouragement Award (2019) Nagoya Institute of Technology Excellent Faculty Evaluation Award (2017) Combustion Society of Japan Paper Award (2016) Japan Society of Mechanical Engineers Encouragement Award (2014) Dr. Saiki leads significant research funding including NEDO's Green Innovation Fund Project (2023-2032) on thermal process decarbonization and multiple JSPS grants. He actively serves on committees for the Combustion Society of Japan (Colloquium Chair), Japan Society of Mechanical Engineers, and Japan Heat Transfer Society. His laboratory collaborates with DENSO Corporation on particle velocity measurements and jet flow control.