جست‌وجوی استاد و پژوهشگر

Claudia Felserمشاهده پروفایل

استاد

• Topological Materials
• Heusler Compounds
• Quantum Chemistry
• +۳ مورد دیگر

Claudia Felser is a Director at the Max Planck Institute for Chemical Physics of Solids in Dresden, Germany, and holds a C4 professorship at Johannes Gutenberg University Mainz. She studied Chemistry and Physics at the University of Cologne, earning her diploma (1989) and doctorate (1994). Her research focuses on the design and discovery of new inorganic compounds, particularly Heusler compounds and topological quantum materials. Education : Diploma in Solid-State Chemistry, University of Cologne (1989); Doctorate in Chemical Physics, University of Cologne (1994) Felser's work spans topological insulators, Weyl semimetals, Kondo behavior, non-centrosymmetric superconductivity, and spintronics. She explores multifunctional properties in Heusler compounds, such as band inversion, rare-earth element-induced superconductivity, and heavy fermion behavior. Her publications highlight advancements in magnetic Heusler compounds, Weyl semimetals, and Berry curvature-driven effects. Key areas include spin polarization, high Curie temperatures, and strong electromagnetic responses. Scientific Awards : ERC Advanced Grants (2011, 2017), APS James C. McGroddy Prize (2019), Max Born Prize (2022), EPS Europhysics Prize (2023), and memberships in prestigious academies. Felser leads a research team at the Max Planck Institute, focusing on quantum materials and functional devices for future technologies.

Hammou Elbarmiمشاهده پروفایل

استاد

• Order Restricted Inference
• Survival Analysis
• Categorical Data Analysis
• +۳ مورد دیگر

Hammou Elbarmi is a Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, CUNY. His academic journey includes a PhD in Statistics from the University of Iowa (1993), an MS in Statistics (1989), a DEA in Applied Mathematics from University Mohamed V, Morocco, and a BS in Applied Mathematics from the same institution. PhD, Statistics, University of Iowa (1993) MS, Statistics, University of Iowa (1989) DEA, Applied Mathematics, University Mohamed V, Morocco BS, Applied Mathematics, University Mohamed V, Morocco Elbarmi specializes in Order Restricted Inference , Survival Analysis , and Categorical Data Analysis . His research focuses on statistical methods for comparing survival and cumulative incidence functions under stochastic ordering constraints, with applications to competing risks and biased sampling. He has developed novel nonparametric estimation techniques for distributions with type I/II bias and pioneered empirical likelihood approaches for hypothesis testing under inequality constraints. His work spans over two decades of funded research projects from PSC-CUNY , including grants for "Nonparametric estimation under stochastic precedence" (2020-2022) and "Consistent estimation of survival functions under uniform stochastic ordering" (2023-2024). He has delivered over 30 presentations at institutions like Rice University, University of Iowa, and Joint Statistical Meetings. Elbarmi has received the Faculty Scholarship and Creative Achievement Award at Baruch College annually from 2003-2011 and served as a Co-Chair for the Seminar Series in Statistics & Operations Research. His editorial and peer-review roles include associate editorships for journals like Statistica Sinica and Journal of Nonparametric Statistics .

Sonja Ungerمشاهده پروفایل

پژوهشگر

• Specialty Optical Fibers
• Fiber Laser Technology
• Photodarkening in Rare-Earth Doped Fibers
• +۵ مورد دیگر

Sonja Unger is a Scientist at the Leibniz Institute of Photonic Technology (IPHT) in Jena, Germany, working within the Technology Group Competence Center for Specialty Optical Fibers. Her research focuses on the material science and engineering aspects of specialty optical fibers for laser and sensing applications. Dr. Unger's research interests center on the fundamental properties of doped silica glasses for optical fiber applications. She investigates photodarkening phenomena in rare-earth doped fibers, particularly examining how co-dopants like cerium and aluminum can mitigate degradation effects in ytterbium and thulium fiber lasers. Her work also explores the viscous flow behavior of various doped silica glasses during fiber manufacturing processes, including collapsing techniques used in MCVD (Modified Chemical Vapor Deposition). Analysis of her publication record reveals consistent focus on specialty optical fiber development, with particular emphasis on thulium and ytterbium doped systems. Her research spans from fundamental material characterization to practical high-power laser applications, with significant contributions to understanding photodarkening mechanisms and developing mitigation strategies. Recent work has expanded into crystal-derived fiber approaches, combining crystalline and amorphous materials to create novel fiber structures with enhanced performance characteristics. Dr. Unger actively collaborates with researchers across the IPHT institute and with external partners, as evidenced by her co-authorship on numerous publications with colleagues from various research groups. Her work contributes significantly to advancing high-power fiber laser technology and specialty optical fiber development.

Theo Scholtesمشاهده پروفایل

پژوهشگر

• Quantum Magnetometry
• Dark Matter Detection
• Atomic Physics
• +۳ مورد دیگر

Dr. Theo Scholtes is a researcher in the Quantum Systems department at the Leibniz Institute for Photonics (Leibniz-IPHT), specializing in quantum magnetometry and dark matter detection. He leads the Quantum Magnetometry working group and has developed advanced optically pumped magnetometer (OPM) systems for ultrasensitive magnetic field measurements. Key research areas: Quantum magnetometry, dark matter detection, sensor technology Notable collaborations: Global Network of Optical Magnetometers for Exotic physics searches (GNOME) His work focuses on overcoming technical limitations in OPMs, including dead zones, heading errors, and transient phase responses. He has pioneered innovations like the light-shift dispersed Mz mode, omnidirectional magnetic field sensitivity, and passivated gold mirror integration in alkali vapor cells. Recent publications (2023-2025) address vector magnetometry in Earth's magnetic field, dead-zone-free sensors, and domain wall detection for dark matter. Earlier work (2016-2020) includes laser stabilization techniques and magnetorelaxometry in biomedical contexts.

Weiwei Huمشاهده پروفایل

استاد

• Applied Mathematics
• Partial Differential Equations
• Mathematical Control Theory
• +۳ مورد دیگر

Weiwei Hu is a Professor in the Department of Mathematics at the University of Georgia, specializing in applied mathematics with a focus on control theory and fluid dynamics. Her research bridges theoretical analysis and computational methods to address complex problems in partial differential equations and optimal control systems. She received her Ph.D. in Mathematics from Virginia Tech, establishing the foundation for her expertise in mathematical modeling and analysis. Her educational background directly informs her innovative approaches to fluid dynamics and control problems. Professor Hu's research spans approximation and mathematical control theory of partial differential equations, optimal control of transport and mixing via fluid flows, well-posedness and long-time behavior of mathematical fluid dynamics, data-driven optimal control for network dynamics, computational methods for model reduction, and reliability analysis of renewable systems. She develops advanced theoretical frameworks and numerical algorithms to solve boundary control problems in Stokes and Navier-Stokes flows, with applications in engineering and environmental systems. Her publication record from 2018-2023 reveals consistent advancement in fluid flow control, particularly in optimal mixing and transport phenomena. She has pioneered numerical methods for boundary control of fluid systems while expanding into data-driven approaches for network dynamics and renewable energy applications. Her collaborative work with institutions including Kansas State University, Missouri S&T, and Carnegie Mellon University demonstrates the interdisciplinary impact of her research. Her scientific contributions have been recognized with the prestigious Humboldt Research Fellowship for Experienced Researchers in 2024. Professor Hu has secured over $1.5 million in research funding as principal investigator from NSF, AFOSR, and DARPA, including a 2023-2026 AFOSR grant on hybrid control of semi-dissipative systems and multiple NSF collaborative projects addressing deep-learning-enabled optimization for power systems and computational methods for optimal transport. Her grant portfolio reflects leadership in securing competitive funding for high-impact mathematical research. She actively collaborates with researchers across the United States and Europe on interdisciplinary projects integrating PDE modeling, machine learning, and topology analysis for applications in MRI analysis and renewable energy systems, demonstrating strong leadership in collaborative mathematical research.

Ardeshir Ebtehajمشاهده پروفایل

دانشیار

• Hydrology
• Remote Sensing
• Environmental Science
• +۵ مورد دیگر

Ardeshir Ebtehaj is an Associate Professor at the University of Minnesota's College of Science & Engineering, Department of Civil, Environmental and Geo-Engineering. He leads the Hydrologic Sciences and Remote Sensing (HydSens) Laboratory at the Saint Anthony Falls Laboratory (SAFL) and serves as Editor of the Journal of Hydrometeorology. His research integrates physical models with data science to address sustainable water, food, and energy systems. Education: Postdoctoral Research, Georgia Institute of Technology Ph.D., University of Minnesota, Water Resources and Hydrology M.Sc., University of Minnesota, Mathematics M.Sc., Iran University of Science and Technology, Environmental Engineering M.Sc., Iran University of Science and Technology, Structural Engineering B.Sc., Iran University of Science and Technology, Civil Engineering Research Interests: Electromagnetic hydrology, satellite hydrometeorology, microwave remote sensing, inverse problems, land-atmosphere interactions, climate intelligence, and machine learning applications for water and environmental monitoring. Awards & Grants: Recent funding from NASA (Arctic ecohydrology modeling, SMAP program) and USACE (Harmful Algal Blooms detection). Collaborations: HydSens team at SAFL, NASA Goddard Space Flight Center, University of Alaska.

Tomislav Plesaمشاهده پروفایل

مدرس

• Synthetic Biology
• Mathematical Biology
• Biochemical Systems
• +۴ مورد دیگر

Dr. Tomislav Plesa is a Lecturer and Fellow at Peterhouse, University of Cambridge. His research bridges mathematics and biology, focusing on synthetic biology and the construction of deterministic/stochastic dynamical systems for biological substrates. He completed his PhD at the University of Oxford (2018) and postdoctoral work at Imperial College London (2021). Education: Doctoral Research, Mathematical Institute, University of Oxford (2018) Postdoctoral Research, Department of Bioengineering, Imperial College London (2021) Research Interests: Dr. Plesa investigates design principles of living systems and challenges in biochemical system design. His work includes integral feedback mechanisms, stochastic approximations, noise utilization in molecular computing, and chemical reaction network dynamics. He explores both theoretical and applied aspects of synthetic biology, with a focus on deterministic/stochastic modeling and mathematical approximations. Publication Trends: Recent works (2023–2024) emphasize chemical reaction networks, integral feedback, and stochastic control. Earlier publications (2016–2021) address noise dynamics, bistability, and homoclinic bifurcations. His research spans nonlinear dynamics, biomathematics, and computational biology, often integrating synthetic biology with theoretical frameworks.

• Artificial Intelligence
• Machine Learning
• Inverse Reinforcement Learning
• +۵ مورد دیگر

Aaron Snoswell is a Research Fellow at Queensland University of Technology (QUT), affiliated with the Faculty of Creative Industries, Education & Social Justice and the School of Communication. His work bridges technical AI development with social implications, focusing on ethical implementation of AI systems across multiple domains including healthcare, legal systems, and public policy. Snoswell's research interests span artificial intelligence, machine learning (particularly inverse reinforcement learning), AI ethics, automated decision-making systems, and the societal implications of AI technologies. His work demonstrates a consistent focus on making AI systems more transparent, accountable, and beneficial for society, with particular attention to healthcare applications, legal systems, and public understanding of AI technologies. Analysis of his most recent publications reveals a clear trajectory toward responsible AI deployment, with increasing focus on policy frameworks, public education about AI capabilities and limitations, and practical applications that address real-world problems in healthcare and justice systems. His work often combines technical AI development with critical social perspectives, reflecting an interdisciplinary approach that connects computer science with social sciences. Snoswell has been actively involved in policy submissions to governmental bodies including the Australian Senate Select Committee on Adopting Artificial Intelligence and responses to the G7 Guiding Principles for organizations developing advanced AI systems. His work with the ARC Centre of Excellence for Automated Decision-Making and Society (ADM+S) demonstrates his commitment to shaping responsible AI governance frameworks. He has established productive collaborations across multiple institutions and disciplines, working with researchers in communication studies, computer science, healthcare, and legal fields. His recent work on the GenAI Arcade project represents an innovative approach to public engagement with AI technologies through interactive learning environments.

Ai Taniguchiمشاهده پروفایل

استادیار

• Formal Semantics
• Formal Pragmatics
• Public Linguistics
• +۸ مورد دیگر

Ai Taniguchi is an Assistant Professor (Teaching Stream) in the Department of Language Studies at the University of Toronto Mississauga, specializing in English Language Linguistics and Online Teaching. Her academic journey includes a PhD in Linguistics from Michigan State University, where she worked under Marcin Morzycki, an MA in Linguistics from the same institution, and a BA (Highest Honours) in Linguistics and French from the University of Georgia. Her research expertise spans formal semantics and pragmatics, with particular focus on non-at-issue meaning, social meaning, and dynamic semantics. She explores what kinds of meaning exist in language beyond literal, at-issue meanings and how they can be formally modeled. Her work includes investigations of Japanese linguistic phenomena such as sentence-final particles, role language ( yakuwarigo ), and pejorative language use. Taniguchi's pedagogical work is equally innovative, combining linguistics with visual arts to create accessible educational content. She uses comics and other visual media to communicate complex linguistic concepts to broader audiences through her LingComics projects. Her teaching interests include equity, diversity, inclusion, and decolonization in linguistics education, public outreach, and innovative online teaching methods for large introductory courses. Her publications and research demonstrate a consistent pattern connecting formal linguistic theory with practical applications in education and public communication. She has co-authored Essentials of Linguistics (2nd edition), an open educational resource that makes linguistics accessible to wider audiences, reflecting her commitment to public linguistics and educational equity. LSA 2019: 5-Minute Linguist (winner) University of Toronto International Student Experience Fund (2022-2023) eCampusOntario Virtual Learning Strategy grant (2021-2022) As a teaching-focused faculty member, Taniguchi has developed numerous courses including LIN204 (English Grammar I), LIN205 (English Grammar II), LIN337 (Lexical Semantics), LIN338 (Pragmatics), and LIN452 (Communicating English Language Linguistics). Her pedagogical approach emphasizes authentic assessment, justice-centered curriculum development, and the integration of visual arts into linguistic instruction. Taniguchi maintains an active presence in both academic and public spheres, with media appearances, public lectures, and high school outreach programs in the Greater Toronto Area. Her work bridges the gap between specialized linguistic research and public understanding through creative communication methods.

Aamna AlShehhiمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Big Data Analytics
• +۷ مورد دیگر

Dr. Aamna AlShehhi serves as an Assistant Professor in the Department of Biomedical Engineering and Biotechnology at Khalifa University, where she has pioneered AI-driven healthcare solutions since joining in 2020. Her interdisciplinary expertise bridges electrical engineering, computer science, and clinical medicine through innovative research collaborations with MIT, Imperial College London, and Novartis. Her educational foundation includes: Bachelor's in Software Engineering, United Arab Emirates University (2009) Master's in Computing and Information Science, Masdar Institute (MIT collaboration) (2013) PhD in Interdisciplinary Engineering, Masdar Institute (MIT collaboration) (2017) Dr. AlShehhi's research centers on artificial intelligence for healthcare transformation , with emphasis on neurodegenerative disease prediction, rare disease diagnostics, and wearable-based gait analysis. She develops constrained deep learning models for Alzheimer's biomarker discovery, self-supervised frameworks for emotion recognition using smartphone data, and physics-informed neural networks for biomechanical analysis. Her work integrates multimodal data including fMRI, genomic sequences, and real-world behavioral metrics to address critical gaps in dementia screening, cancer detection, and mental health monitoring. Analysis of her 2023-2025 publications reveals three dominant trends: (1) Explainable AI architectures for clinical decision support, particularly constrained graph neural networks analyzing comorbidity networks in Alzheimer's; (2) Multimodal sensor fusion systems combining wearables, keystroke dynamics, and fMRI for neurological assessment; (3) Self-supervised learning approaches enabling robust performance with limited medical data. Her work consistently targets real-world clinical deployment through portable systems like smart gait analyzers and remote depression screening tools. Dr. AlShehhi actively mentors the next generation of biomedical engineers: Ferial J. Abuhantash (Ph.D. candidate) Khalid Ahmed Abdulla (Master's student) Her research is supported through strategic partnerships with Novartis Institute for Biomedical Research and collaborative grants enabling AI-driven drug repurposing for dementia and genomic cancer detection. She leads projects analyzing skin lesions for melanoma screening and developing deep learning models for early-stage cancer detection using genomic data. As a core member of Khalifa University's Healthcare Engineering Innovation Group , she directs a multidisciplinary team including Postdoctoral Fellow Dr. Sherlyn Jemimah and Research Assistant Chahd Maher Musthafa Chabib. Current initiatives focus on creating deployable AI systems for clinical settings, with ongoing work on portable gait assessment tools for rehabilitation centers and real-time emotion recognition platforms for mental health monitoring.

• Wave Propagation
• Mathematical Simulation
• Aeroacoustics
• +۷ مورد دیگر

Éric Lunéville is a Researcher in Applied Mathematics at ENSTA Paris, where he is affiliated with the Applied Mathematics Unit (UMA) and the Wave Propagation, Mathematical Study and Simulation (POEMS) research group. His work focuses on mathematical modeling and numerical simulation of wave phenomena, particularly in the context of acoustics and waveguides. Dr. Lunéville's research interests span wave propagation theory, mathematical simulation techniques, aeroacoustics, waveguide analysis, acoustic multicasting, optimization methods, inverse problems, and numerical approaches for high-frequency diffraction. His work demonstrates a strong connection between theoretical mathematics and practical engineering applications, particularly in the field of acoustics. Analysis of his publication record reveals a consistent focus on waveguide theory and numerical simulation methods. His research shows progression from fundamental mathematical modeling of wave phenomena to practical software implementations like the XLiFE++ library. A significant portion of his work addresses inverse problems in wave propagation, particularly related to crack detection and non-scattering phenomena in waveguides. His research also demonstrates expertise in developing transparent boundary conditions and multimodal approaches for complex waveguide configurations. Knight of the Order of Academic Palms Dr. Lunéville has made substantial collaborative contributions to wave propagation research, working with prominent researchers including Anne-Sophie Bonnet-Ben Dhia, Laurent Bourgeois, and Jean-François Mercier. His development of the XLiFE++ software library represents a significant bridge between theoretical mathematics and practical computational tools for engineers and scientists working in wave propagation and related fields. As part of the POEMS research group at ENSTA Paris, Dr. Lunéville contributes to a collaborative environment focused on mathematical wave propagation studies with applications across multiple disciplines including acoustics, structural mechanics, and non-destructive testing. His work supports broader institutional research goals in sustainable energy, transportation, and defense sectors through advanced mathematical modeling and simulation techniques.

• Inverse Problems
• Seismic Imaging
• Full Waveform Inversion
• +۳ مورد دیگر

Mohamed Aziz Boukraa is a postdoctoral researcher at the Applied Mathematics Unit (UMA) of Institut Polytechnique de Paris , working with the IDEFIX team on collaborative projects with EDF . His work focuses on interface imaging problems in electricity production sites, particularly the dam-rock foundation interface using non-destructive seismic waves and Full Waveform Inversion . His research spans inverse problems in applied mathematics, with a focus on: Biharmonic equation regularization techniques Cauchy problem solutions for thin plate theory Finite element method implementations Seismic wave propagation modeling Shape optimization for imaging Key publications demonstrate: Novel fading regularization algorithms DKQ plate elements for structural analysis High-resolution dam-rock interface imaging Noise-robust inversion methods Applications in hydroelectric infrastructure diagnostics Advancements in metric resolution imaging

• Applied Geophysics
• Environmental Geophysics
• Seismic Monitoring
• +۴ مورد دیگر

Diego AROSIO is an Associate Professor at the Department of Chemical and Geological Sciences, University of Modena and Reggio Emilia. His academic focus is in Applied Geophysics (GEOS-04/B), where he conducts research and teaches courses related to geophysical investigations, seismic monitoring, and environmental applications of geophysical methods. Dr. AROSIO teaches several advanced courses including: In situ Geognostic and Geophysical Investigations for the Master's Degree in Geosciences, Georisks and Georesources Seismic Sources and Microzonation for the Master's Degree in Geosciences, Georisks and Georesources Geophysics with elements of seismology for the Degree Course in Geological Sciences His teaching emphasizes practical applications of geophysical methods for environmental monitoring, landslide risk assessment, and cultural heritage preservation. His research interests span multiple areas of applied geophysics: Geophysical monitoring of landfills and waste containment systems Ground Penetrating Radar applications for structural assessment and cultural heritage conservation Seismic and microseismic monitoring of unstable rock slopes and landslides Development and validation of novel geophysical instrumentation and methodologies Application of optical fiber sensors for geohazard monitoring Dr. AROSIO's work demonstrates a strong focus on developing practical, field-deployable geophysical solutions for environmental and engineering challenges. His recent research output shows consistent trends toward developing and validating geophysical methods for environmental monitoring applications, particularly in landfill integrity assessment, landslide monitoring, and cultural heritage preservation. He frequently employs mixed-method approaches combining laboratory testing, field validation, and numerical modeling to advance geophysical techniques. Dr. AROSIO collaborates extensively with researchers across multiple institutions and disciplines, as evidenced by his publication record which includes collaborations with experts in hydrogeology, structural engineering, and environmental science. His laboratory and field research focuses on: Development of monitoring protocols for landfill integrity using geoelectrical methods Application of Ground Penetrating Radar for quality control in marble conservation Microseismic monitoring networks for rock slope stability assessment Optical fiber sensor applications for early detection of landslide precursors These research activities position Dr. AROSIO at the intersection of geophysics, environmental engineering, and geohazard mitigation.

Michael Hintermüllerمشاهده پروفایل

استاد

• Applied Mathematics
• Optimization and Control
• Partial Differential Equations
• +۴ مورد دیگر

Prof. Michael Hintermüller is the Director of the Weierstrass Institute (WIAS) and holds a professorship in Applied Mathematics at Humboldt-Universität zu Berlin. He serves as Founding Coordinator of BR50, Spokesperson of the Mathematical Research Data Initiative (MaRDI), and Board Member of the MATH+ Cluster of Excellence. His research focuses on nonsmooth optimization, PDE-constrained control, mathematical image processing, and quasi-variational inequalities. Leadership Roles: Director of WIAS; Founding Coordinator of BR50; Spokesperson of MaRDI; Board Member of MATH+ Key Research Areas: Mathematical image processing, optimization under uncertainty, PDE-constrained optimization, shape/topology optimization, learning-informed constraints Recent Applications: Image deblurring/denoising/demodulation, energy network modeling, gas dynamics on pipeline networks, thermoforming simulations, strained photonic device design. His work combines analytical rigor with numerical methods for inverse problems, including adaptive regularization and physics-informed neural networks. Scientific Leadership: Active in mathematical modeling for biomedical imaging (e.g., quantitative MRI) and industrial applications (e.g., semiconductor design, gas flow optimization). Develops novel algorithms for nonsmooth PDE systems and contributes to the theoretical foundations of quasi-variational inequalities and generalized Nash equilibrium problems.

• Numerical Analysis
• Inverse Problems
• Computational Mathematics
• +۳ مورد دیگر

Dr. Malena Sabate Landman is a Research Fellow at the Mathematical Institute of the University of Oxford, where she is a member of the Numerical Analysis research group. Her office is located in the Andrew Wiles Building at the Radcliffe Observatory Quarter in Oxford. Her primary research areas are: Numerical Analysis Inverse Problems Bayesian Methods Optimization Computational Mathematics Medical Imaging Recent publications in 2025 highlight her contributions to the development of flexible Krylov subspace methods for Bayesian inverse problems, inner-product free iterative methods for large-scale inverse problems, and robust optimization techniques. She has also developed the TIGRE v3 toolbox for computed tomography reconstruction. As a member of the Numerical Analysis group at the Mathematical Institute, she collaborates on advancing computational methods for scientific and medical applications.