جست‌وجوی استاد و پژوهشگر

Ayman Moussaمشاهده پروفایل

مدرس

• Mathematical Analysis
• Numerical Analysis
• Partial Differential Equations
• +۶ مورد دیگر

Ayman Moussa is a Lecturer at Sorbonne University, affiliated with the Jacques-Louis Lions Laboratory , with a part-time role at the Department of Mathematics (DMA) at École Normale Supérieure (ENS) since 2021. His research focuses on the mathematical and numerical analysis of models in biology, including population dynamics and particle transport . PhD in Mathematics (2009), Sorbonne University (UPMC) HDR (Habilitation à diriger des recherches) in 2018 His work spans cross-diffusion systems , Vlasov-Navier-Stokes coupling , and nonlinear parabolic equations , with a focus on stability, uniqueness, and entropy structures. Recent submitted work includes perturbative solutions for cross-diffusion and revisiting quasi-linear parabolic systems. He has held research grants such as the research investment bonus (2013–2017) and led international projects like the Franco-Japanese PHC Sakura (2019–2022). His teaching includes advanced courses on L p spaces , Sobolev spaces , and Vlasov equations at Sorbonne University, ENS, and institutions abroad. He also co-founded Aromaths , a student seminar at Sorbonne University, and is a passionate advocate for the sport Plumfoot , having served as chair of France Plumfoot and organized world championships.

Philippe Pasquierمشاهده پروفایل

استاد

• Geothermal Energy
• Hydrogeology
• Mathematical Modeling
• +۴ مورد دیگر

Philippe Pasquier is a Full Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. He holds the NSERC Industrial Chair in Geothermal Energy and leads the Geothermal and Hydrogeology Research Group. His research integrates spectral methods, artificial intelligence, and stochastic modeling to advance geothermal system design, with experimental validation at the Geothermal Research Unit (URG). Education & Teaching: Teaches Applied Hydrogeology (GLQ2601) and Low-Temperature Geothermal Energy (GML6113). Recognized with multiple teaching awards, including Best Professor in Civil/Geological/Mining Engineering (2011-2015). Research Focus: Specializes in low-temperature geothermal systems (open/closed loops, standing column wells), thermal response testing, inverse problems in hydrogeology, and energy-efficient building integration. His work aims to reduce costs and promote geothermal adoption through advanced computational tools. Publications: Recent articles (2022-2025) emphasize machine learning applications for geothermal modeling, thermal plume dynamics, and system optimization, reflecting a trend toward AI-driven sustainable energy solutions. Awards & Leadership: NSERC Industrial Chair in Geothermal Energy Best Professor Awards (2010-2015) Head, Geothermal Research Unit (URG) Students & Grants: Supervised 7 doctoral and 11 master's students. Secured NSERC Alliance grants for large-scale geothermal deployment in Quebec institutions. Tools & Outreach: Developed open-source software TRT-SInterp for thermal response test analysis. Frequently featured in media (Le Devoir, La Presse) advocating geothermal solutions.

Shihao Yangمشاهده پروفایل

استادیار

• Statistics
• Machine Learning
• Data Science
• +۵ مورد دیگر

Shihao Yang serves as a Harold E. Smalley Early Career Professor and tenure-track Assistant Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He maintains critical affiliations with the Institute for Data Engineering and Science (IDEaS), Center for Machine Learning at Georgia Tech (ML@GT), and Institute of People and Technology (IPaT). His academic credentials include: Ph.D. in Statistics (2019), Harvard University A.M. in Statistics (2016), Harvard University B.Sc. in Actuarial Science (2014), University of Hong Kong Professor Yang's research program centers on harnessing big data through methodological development, computational tools, and probabilistic modeling to solve real-world problems. His expertise spans statistics, machine learning, and data science with concentrated applications in infectious disease modeling, dynamic system inference (parameter estimation in differential equations), and electronic medical records mining. His work consistently bridges theoretical innovation with practical healthcare implementations. Analysis of his recent publications reveals dominant themes in time series forecasting, causal inference, and physics-informed machine learning. His research integrates deep learning with statistical methods to enhance dynamic system analysis and healthcare applications, particularly in electronic health records interpretation and infectious disease prediction using internet search data. This synthesis drives methodological advances in handling noisy, sparse real-world datasets. His notable recognition includes: Harold E. Smalley Early Career Professorship Professor Yang actively mentors undergraduate, master's, and PhD students, encouraging prospective candidates to contact him with CVs and transcripts. His research receives substantial institutional support through Georgia Tech's interdisciplinary centers. He leads methodological development for real-world data challenges while maintaining strong industry and healthcare partnerships. As a core member of IDEaS, ML@GT, and IPaT, he drives cross-disciplinary collaboration between engineering, computing, and social sciences to advance data-driven solutions for complex societal problems.

Ozgur Ozdemirمشاهده پروفایل

استاد

• Signal Processing
• Electromagnetics
• Microwave and Antenna Technologies
• +۵ مورد دیگر

Professor Ozgur Ozdemir is a distinguished academic at Istanbul Technical University, where he serves in the Department of Electronics and Communication Engineering within the Faculty of Electrical and Electronics Engineering. With a PhD from New Jersey Institute of Technology, he has established himself as a leading researcher in multiple interdisciplinary fields that bridge electrical engineering with biomedical applications. His educational background includes a Licence (Bachelor's) and Degree (Master's) from Istanbul Technical University's Department of Electronics and Communication Engineering, followed by doctoral studies at New Jersey Institute of Technology. His career progression shows his advancement from Assistant Professor (2008-2014) to Associate Professor (2014-2015), and ultimately to his current position as Professor. Professor Ozdemir's research spans several interconnected domains with a strong emphasis on Signal Processing , Electromagnetics , and Microwave and Antenna Technologies , with significant applications in Biomedical Engineering . His work particularly focuses on Photoacoustic Imaging and Thermoacoustic Imaging techniques for medical diagnostics, including cancer detection and immune cell analysis. His research bridges theoretical electromagnetic concepts with practical medical applications, creating innovative imaging solutions for healthcare challenges. The analysis of his publication record reveals a strong trend toward biomedical applications of electromagnetic principles, with recent work emphasizing photoacoustic imaging for cancer diagnosis, immune cell characterization, and drug treatment monitoring. His publications demonstrate expertise in both theoretical electromagnetic modeling and practical implementation of imaging systems. IEEE Member (2020-2022) Professor Ozdemir has been actively involved in numerous research projects, serving as Principal Investigator on several significant grants. His work demonstrates strong leadership in directing research teams toward practical biomedical applications of electromagnetic theory. His current projects include developing photoacoustic tomographic imaging methods for skin cancer diagnosis, photoacoustic imaging for lymphoma treatment, and oxygen saturation detection using photoacoustic tomography. His laboratory work focuses on photoacoustic tomography systems, microwave imaging techniques, and electromagnetic applications in biomedical contexts. His team appears to specialize in developing imaging solutions for cancer diagnosis and monitoring, with particular emphasis on skin cancer and breast cancer applications.

Lei Zhaoمشاهده پروفایل

دانشیار

• Hamiltonian Dynamical Systems
• Celestial Mechanics
• Symplectic Geometry
• +۲ مورد دیگر

PD Dr. Lei Zhao is a Heisenberg Fellow and core member of the Analysis and Geometry research group at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg. He leads an Emmy Noether Junior Research Group focused on dynamical systems since 2017. His academic credentials include: 2008: B.Sc. in Mathematics from Nanjing University, China 2013: Ph.D. from Université Paris Diderot, France 2020: Habilitation at University of Augsburg 2021: Venia Legendi (authorization to lecture) at University of Augsburg Dr. Zhao's research centers on Hamiltonian Dynamical Systems , Celestial Mechanics , and Symplectic Geometry , with emphasis on periodic orbits, integrable billiard systems, and geometric structures in n-body problems. His work bridges pure mathematics and theoretical physics through advanced techniques in differential geometry and topological dynamics, yielding novel insights into collision orbits and regularization methods. Analysis of his 15 most recent publications (2019-2024) reveals consistent thematic focus on geometric transformations in mechanical systems, particularly projective/conformal methods applied to Kepler problems, restricted three-body dynamics, and higher-dimensional billiards. His research demonstrates increasing sophistication in connecting symplectic invariants with celestial mechanical phenomena. His scientific recognition includes: Heisenberg Fellowship from German Research Foundation (DFG) Emmy Noether Fellowship enabling independent research leadership As an Emmy Noether Group Leader, Dr. Zhao has secured significant DFG funding for his research program while maintaining extensive international collaborations with institutions in France, China, and the Netherlands. He actively contributes to the mathematical community through seminar organization and workshop participation. Within the University of Augsburg, he is integral to the Analysis and Geometry group that organizes the Oberseminar on Differential Geometry and Topics in Symplectic Geometry, fostering interdisciplinary research in geometric dynamics.

• Optimization
• Multi-criteria Decision Making
• Production Planning
• +۲ مورد دیگر

Selin Özpeynirci is a Professor in the Department of Industrial Engineering at the Faculty of Engineering, İzmir University of Economics, where she has been employed since February 2008. Her academic career began as a research assistant at Middle East Technical University's Department of Industrial Engineering from 2002 to 2007, where she also completed all her degrees. She has held visiting researcher positions at prestigious French institutions including Ecole Centrale Paris (2014-2015) and Centrale Supelec (2019-2020), supported by TÜBİTAK 2219 grants. Her educational background includes: B.S. in Industrial Engineering, Middle East Technical University (2002) M.S. in Industrial Engineering, Middle East Technical University (2004) Ph.D. in Industrial Engineering, Middle East Technical University (2007) Özpeynirci's research focuses on optimization methodologies applied to complex industrial systems. She specializes in multi-criteria decision making frameworks that address real-world constraints in production environments. Her work bridges theoretical advances in mathematical programming with practical applications in manufacturing scheduling and resource allocation. Current investigations emphasize interactive algorithms that incorporate human decision-maker preferences into computational models, particularly for sorting and selection problems with equity considerations. Analysis of her 15 most recent publications reveals a strong trajectory in inverse multiple criteria problems and constrained resource allocation. Her work consistently integrates flexible manufacturing systems concepts with advanced decision theory, showing increasing sophistication in handling preference uncertainty and balance requirements. The publications demonstrate methodological evolution from classical scheduling approaches toward hybrid interactive-optimization frameworks that accommodate human judgment in complex decision environments. Her scientific recognition includes: TÜBİTAK 2219 Research Grant for Ecole Centrale Paris (2014-2015) TÜBİTAK 2219 Research Grant for Centrale Supelec (2019-2020) Özpeynirci actively collaborates with international researchers including Vincent Mousseau (France), Özgür Özpeynirci (Turkey), and Burak Gokgur (Turkey) on projects spanning multiple criteria decision analysis and production systems optimization. Her current research agenda explores drone-integrated logistics and advanced decomposition methods for large-scale inverse sorting problems, supported by ongoing institutional affiliations and research grants. While specific lab information isn't provided in the source material, her work clearly operates within the industrial engineering research ecosystem focused on computational decision support systems.

• Autonomous Systems
• Digital Twinning
• Human Factors
• +۸ مورد دیگر

Professor Yifan Zhao (FHEA, SMIEEE) is a faculty member at the Centre for Life-cycle Engineering and Management , Cranfield University. His research spans Autonomous Systems, Digital Twinning, Human Factors, Sensor Technologies, and Through-life Engineering Services , integrating AI, computer vision, and signal processing to solve inverse problems in engineering and healthcare. Clients include Jaguar Land Rover, BAE Systems, and the NHS. Dr. Zhao received his PhD in Automatic Control and System Engineering from the University of Sheffield (2007), with MSc and BEng in Automatic Control Engineering from the Beijing Institute of Technology. His work focuses on data-driven solutions for decarbonisation, non-destructive testing, driver monitoring, and digital healthcare, supported by grants such as the Royal Academy of Engineering Industrial Fellowship and an Innovate UK £815K grant . Recent publications highlight his expertise in EEG signal processing , structural health monitoring , and trustworthy AI implementation , with applications in aerospace, construction, and neurodegenerative disease diagnosis. Scientific awards include recognition as a Fellow of the Higher Education Academy and Senior Member of IEEE . Collaborations with industry leaders like Airbus and Thales, alongside supervision of research students (e.g., Isa Ismail, Yizhong Wang), underscore his impact. Key projects include TRAMS-Enterprise (Trustworthy AI for construction) and CogShift (driver-cognition control authority shifting for autonomous vehicles). His lab, part of Cranfield's Manufacturing and Materials theme, develops innovative NDT technologies and AI frameworks for real-world applications.

Clifford Nolanمشاهده پروفایل

دانشیار

• Medical Imaging
• Geophysical Imaging
• Radar Imaging
• +۵ مورد دیگر

Clifford Nolan is an Associate Professor in the Department of Mathematics and Statistics at the University of Limerick. He is affiliated with the Centre for Research Training in Foundations of Data Science and the Mathematics Applications Consortium for Science and Industry (MACSI). Education : PhD in Global Analysis of Linearized Inversion for the Acoustic Wave Equation from Rice University (1997), Master of Applied Analysis (Rice University, 1993), M.Sc. from Trinity College Dublin (1992), and B.Sc. from National University of Ireland - Galway (1990). His research focuses on Medical, Geophysical, and Radar Imaging , with specialization in Inverse Problems , Wave Propagation/Scattering , and Microlocal Analysis . Recent work includes time-harmonic diffuse optical tomography and microlocal analysis of seismic data. Key trends in his publications include applications of inverse problems to imaging technologies, mathematical analysis of wave equations, and collaborations in geophysical and radar imaging. His work often involves interdisciplinary approaches bridging mathematics with real-world engineering and medical applications. Teaching interests include Partial Differential Equations , Analysis , and Discrete Mathematics .

Michael J Demkowiczمشاهده پروفایل

استاد

• Materials Science
• Nuclear Materials
• Mechanical Engineering
• +۴ مورد دیگر

Michael J Demkowicz is a Professor in the Department of Materials Science and Engineering at Texas A&M University . He holds prestigious titles such as Presidential Impact Fellow and Chancellor EDGES Fellow , reflecting his leadership in materials science research. Ph.D., Mechanical Engineering, Massachusetts Institute of Technology – 2005 S.M., Mechanical Engineering, Massachusetts Institute of Technology – 2004 B.S., Aerospace Engineering, The University of Texas at Austin – 2000 B.S., Physics, The University of Texas at Austin – 2000 B.A., Plan II Liberal Arts Honors Program, The University of Texas at Austin – 2000 His research focuses on fundamental mechanisms of damage and deformation in solids , integrating experiments, simulations, and theory . Key areas include radiation damage resistance, hydrogen embrittlement, and microstructure evolution in nanocomposites and metallic alloys. Recent publications highlight his work on radiation-induced defects , helium bubble dynamics , and dislocation mobility in metals like copper and nickel. His group applies advanced computational methods, including phase-field modeling and machine learning , to optimize material microstructures. Scientific Awards : Brimacombe Medal, The Minerals, Metals and Materials Society (TMS) – 2022 Outstanding Teaching Award, MIT Graduate Materials Council – 2014, 2015 NSF CAREER Award – 2012 Early Career Faculty Fellow, TMS – 2012 Los Alamos Postdoctoral Distinguished Performance Award – 2007 Demkowicz’s work bridges experimental and computational approaches to address challenges in nuclear materials, radiation tolerance, and mechanical failure mechanisms. His group has pioneered methods for high-throughput materials analysis and microstructure control .

Jasper Vrugtمشاهده پروفایل

استاد

• Hydrology
• Computational Science
• Geophysics
• +۴ مورد دیگر

Jasper Vrugt is a Professor at the University of California, Irvine, with joint appointments in Civil and Environmental Engineering and Earth System Science. He holds a Ph.D. (2004) and M.S. (1999), both earned cum laude from the University of Amsterdam. His research focuses on complex Earth system dynamics through integrated numerical modeling, measurement techniques, and inverse methodologies. Research interests span computational science, hydrology, geophysics, and uncertainty quantification. Key themes include diagnosing epistemic errors in models, quantifying uncertainty in environmental predictions, and developing next-generation inference methods. Applications range from soil moisture-precipitation feedbacks to carbon-nitrogen cycling and hydropower optimization. His recent publications demonstrate strong emphasis on Bayesian uncertainty quantification, diagnostic model evaluation, and climate-hydrology interactions. Trends include advanced statistical methods for nonstationary extremes, soil moisture-cloud coupling, and novel sensitivity analysis techniques. Dr. Vrugt leads an active research group advising multiple graduate students. His team develops computational tools for environmental modeling and maintains collaborations with institutions like Los Alamos National Laboratory.

Giorgio Kaniadakisمشاهده پروفایل

استاد

• Statistical Physics
• Theoretical Physics
• Complex Systems
• +۴ مورد دیگر

Giorgio Kaniadakis is a Full Professor at the Department of Applied Science and Technology (DISAT) of Politecnico di Torino. His research focuses on generalized statistics, theoretical physics, and complex systems, with a strong emphasis on κ-statistics and power-law distributions. Research interests include: κ-Statistics, Power-law Distributions, Quantum Computation, Nonlinear Kinetics, Statistical Physics of Complex Systems His recent publications (2024-2016) span urban scaling laws, epidemiological models, predator-prey dynamics, and quantum statistical mechanics, often applying κ-statistics to interdisciplinary problems in physics, biology, and economics. He has organized major conferences like SigmaPhi2014, SigmaPhi2011, and SigmaPhi2008 as Program Chair. He actively teaches courses in statistical mechanics and physics at both PhD and undergraduate levels. Key teams/groups: Institute of Fundamental Physics and Materials for Nanotechnology (DISAT), Generalized Statistical Mechanics of Complex Systems

Spyros Alexakisمشاهده پروفایل

استاد

• General Relativity
• Geometric Analysis
• Inverse Problems
• +۳ مورد دیگر

Spyros Alexakis is a Professor at the Department of Mathematics, University of Toronto. His research focuses on General Relativity, Geometric Analysis, and Inverse Problems. He has organized significant academic events, including a 2015 thematic program on Einstein's equations at the Fields Institute, featuring conferences and public lectures. His work includes groundbreaking contributions to black hole stability, Penrose inequalities, and conformal invariants. Current students include Gregorios Fournodavlos, Tracey Balehowsky, and Ali Feiz. Collaborators include leading mathematicians such as Arick Shao and Sergiu Klainerman. His research extends to geometric scattering theory, nonlinear wave equations, and boundary regularity of minimal surfaces. Education: Not explicitly stated in the provided text. Affiliations: University of Toronto (Department of Mathematics), Fields Institute (Collaborative programs). Research Interests: General Relativity: Black hole dynamics, Penrose inequalities, uniqueness theorems. Geometric Analysis: Ricci flow, minimal surfaces, conformal invariants. Inverse Problems: Uniqueness theorems in wave equations, boundary rigidity. Recent Article Trends: Focus on black hole stability, geometric invariants, and nonlinear wave behavior. Recent works address the Penrose inequality, Bondi energy bounds, and singular Ricci solitons. Grants & Awards: No explicit awards listed, but significant research grants inferred from thematic program organization and publication activity. Advising & Collaborations: Active in mentoring graduate students and collaborating with international researchers. Notable collaborations include projects on black hole rigidity and geometric uniqueness theorems. Labs/Teams: Engaged in interdisciplinary research through the Fields Institute and the University of Toronto's Analysis/Applied Math Seminar.

Johannes Tauschمشاهده پروفایل

استاد

• Numerical approximation
• Boundary integral equations
• Computational electromagnetics
• +۴ مورد دیگر

Johannes Tausch is a Professor at Southern Methodist University specializing in numerical approximation and fast methods for boundary integral equations. His research spans computational electromagnetics, optics, fluid mechanics, shape optimization, and high-dimensional quadrature. Applications include heat transfer, anomalous diffusion, wave propagation, and electromagnetic analysis. Research focuses on developing efficient computational techniques for boundary integral reformulations of PDEs. Key methodologies include fast multipole methods, adaptive quadrature, Galerkin formulations, and mesh-free approaches for complex geometries. Current work emphasizes parabolic problems, moving boundaries, and high-dimensional integration. Publications demonstrate consistent focus on accelerating integral equation solvers through hybrid algorithms, matrix compression techniques, and specialized quadrature. Recent advancements target time-dependent domains and multiphysics coupling. No awards or student advising information is provided in the source materials.

Mikhail Zaslavskiyمشاهده پروفایل

استادیار

• Computational Mathematics
• Model Reduction Techniques
• Inverse Problems
• +۲ مورد دیگر

Mikhail Zaslavskiy is an Assistant Professor at Southern Methodist University, holding a PhD from Lomonosov Moscow State University. His computational mathematics research focuses on forward and inverse PDE solutions through model reduction and multi-scale methods. Applications span geophysical exploration, radar/medical imaging, NASA propellant tank analysis, and unsupervised machine learning techniques for clustering large datasets in bioinformatics, finance, and social networks. Research pillars include Krylov subspace methods for wave propagation problems, homogenization techniques, and data-driven nonlinear inverse problem solutions.

Noel Naughtonمشاهده پروفایل

استادیار

• Bio-Inspired Engineering
• Biomechanics
• Imaging
• +۴ مورد دیگر

Noel Naughton is an Assistant Professor in the Department of Mechanical Engineering at Virginia Tech's College of Engineering. His research focuses on bio-inspired engineering, robotics, and biomechanics, with applications in healthcare and soft robotics. He leads the Naughton Lab and holds a PhD in Mechanical Engineering from the University of Illinois at Urbana-Champaign. Education: PhD in Mechanical Engineering (2019), University of Illinois at Urbana-Champaign MS in Mechanical Engineering (2016), University of Illinois at Urbana-Champaign BS in Mechanical Engineering (2014), University of Saint Thomas Research interests include bio-inspired soft robot design, imaging techniques for musculoskeletal tissues, and numerical modeling of diffusion MRI. His work combines modeling, experiments, and theory to bridge mechanical engineering and biology. Key publications explore topics like neural reservoir control of bio-hybrid arms, muscle-architected soft robotics, and diffusion MRI microstructure analysis. Awards include the Beckman Image of Research award (2022) and NSF Graduate Research Fellowship (2014). His lab focuses on developing innovative solutions for soft robotics and medical imaging technologies, with ongoing projects in biohybrid systems and musculoskeletal mechanics.