جست‌وجوی استاد و پژوهشگر

• Ultrasonics
• Guided Waves
• Time-Reversal Acoustics
• +۵ مورد دیگر

Claire Prada is a CNRS Research Director at the Institut Langevin , specializing in laser ultrasound , guided wave propagation , and time-reversal acoustics . Her work bridges fundamental wave physics and applied nondestructive testing. Research Pillars : Zero-group-velocity (ZGV) Lamb modes for material characterization Anisotropic wave propagation and negative refraction phenomena Time-reversal operator decomposition for structural monitoring Passive acoustic defect localization with ambient noise Technological Innovations : Fourier-domain reconstruction algorithms for 3D imaging Single-pixel photoacoustic microscopy Adaptive projection methods for rib-cage ultrasound focusing Wave Physics Discoveries : Documentation of power flux skewing in anisotropic plates Identification of beating resonance patterns in elastic media Experimental validation of negative reflection in chaotic waveguides Medical & Industrial Applications : Quantitative elastography for tissue stiffness measurement Jet engine blade damage detection Cortical bone femoral neck assessment Thin layer thickness measurement via ZGV resonance shifts

Andrew Jirasekمشاهده پروفایل

استاد

• Medical Physics
• Radiation Oncology Physics
• Raman Spectroscopy
• +۳ مورد دیگر

Andrew Jirasek is a Professor at the Irving K. Barber Faculty of Science , University of British Columbia Okanagan , and serves as the Associate Dean for Graduate and Postdoctoral Training . He leads the Analytics in Medical Sciences (AiMS) Institute with a focus on Medical Physics and Radiation Oncology Physics , particularly utilizing Raman spectroscopy and 3D radiation dosimetry for cancer treatment verification. PhD from University of British Columbia His research involves developing polymer gel dosimeters for 3D radiation dose verification in complex therapies like volumetric modulated arc therapy (VMAT) , collaborating across physics, oncology, and engineering . He also investigates optical technologies to monitor biological responses during radiotherapy using Raman spectroscopy with machine learning for data analysis. Recent publications highlight advancements in 3D gel dosimetry , Raman spectroscopy for metabolic profiling , and iterative image reconstruction algorithms . His work spans dosimeter technology development , radiation therapy quality assurance , and clinical applicability studies for novel treatment verification methods.

Zehang Richard Liمشاهده پروفایل

استادیار

• Statistical Methods
• Demography
• Epidemiology
• +۷ مورد دیگر

Zehang Richard Li is an Assistant Professor in the Department of Statistics at the University of California Santa Cruz. His research focuses on statistical methods for demography, epidemiology, and global health, with expertise in latent variable modeling, space-time models, survey sampling, data integration, and weakly supervised learning. He develops workflows and pipelines for complex statistical analysis and real-world data-driven decision-making. Ph.D. in Statistics from the University of Washington (advisor: Tyler McCormick) Postdoctoral Researcher at Yale School of Public Health (Department of Biostatistics, advisor: Forrest Crawford) His work emphasizes high-dimensional data and uncertainty quantification in prevalence mapping. He leads the development of the SUMMER R package and SAE4Health platform, including ShinyApps for subnational health indicator analysis. Current projects address farmworker health under climate change through a UCOP grant and Bayesian frameworks for integrating verbal autopsy data via NIH funding. Recent publications highlight methodological advances in small area estimation, verbal autopsy analysis, and pandemic modeling. Key article themes include: Bayesian hierarchical modeling and tensor decomposition (2025) Domain adaptation for cross-population cause assignment (2024) Verbal autopsy validation and diagnostic accuracy (2023) Bayesian multilevel poststratification for disease prevalence (2022) Respiratory virus transmission dynamics and immune mechanisms (2021) Grants supporting his research include: National Institutes of Health Melinda and Bill Gates Foundation Vital Strategies Hellman Fellows Program University of California Office of President (UCOP) UCSC Committee on Research He advises Ph.D. students Yu (Zoey) Zhu, Qianyu Dong, Sho Kawano, and Toshiya Yoshida, all of whom have received academic recognition for their work on Bayesian models, small area estimation, and prevalence mapping.

• Computational Mathematics
• Applied Mathematics
• Partial Differential Equations
• +۷ مورد دیگر

Frédéric Valentin is a Senior Researcher at the National Laboratory for Scientific Computing (LNCC) in Brazil, where he led the Department of Computational and Mathematical Methods from 2015 to 2021. He holds an INRIA International Chair (2018–2023) and served on the applied mathematics board of Brazil's National Science Foundation (CNPq) from 2017 to 2020. He currently leads the IPES Research Group and acts as the Brazilian Scientific Director for the Inria-Brasil partnership. His work focuses on computational and applied mathematics, particularly in developing innovative numerical methods for multiscale phenomena in engineering and life sciences. National Laboratory for Scientific Computing - LNCC (2015–2021) INRIA International Chair (2018–2023) Inria-Brasil Partnership (Scientific Director) Valentin's research involves partial differential equations, finite element methods, domain decomposition, and numerical analysis. He explores the integration of numerical algorithms with machine learning and high-performance computing, including applications to supercomputing projects like the Brazil-European Community's HPC4E initiative. His publications span over 60 high-impact journal articles and book chapters, primarily in computational mathematics and numerical analysis. He has co-advised over a dozen PhD students and postdoctoral researchers, contributing to international collaborations and advancing multiscale modeling techniques. Notably, he played a pivotal role in developing the Santos Dumont petaflop supercomputer at LNCC, the most advanced in Latin America.

• Intelligent Systems and Control
• Cognitive Robotics
• Nano-robotics
• +۹ مورد دیگر

Laxmidhar Behera is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur, specializing in Intelligent Systems and Control. With over two decades of academic experience at IIT Kanpur and international research experience at institutions including Fraunhofer Institute of Autonomous Intelligent Systems in Germany, ETH Zurich, and University of Ulster, he has established himself as a leading researcher in cognitive robotics and intelligent control systems. Dr. Behera's research spans multiple cutting-edge domains including Cognitive Robotics, Nano-robotics, Vision based Control, Soft Computing, Information Retrieval in music and language, Semantic Information Processing, Physics of Complex Systems, Cyber Physical Systems, Formation Control of UAVs, Brain-Computer Interface (BCI), and Sanskrit Computational Linguistics. His interdisciplinary approach bridges traditional control theory with modern computational intelligence techniques, creating innovative solutions for complex real-world problems. His extensive publication record in top-tier journals like IEEE Transactions demonstrates his leadership in areas such as brain-computer interfaces, visual servoing, multi-robot systems, and music information retrieval. Notably, his work on quantum neural networks for EEG filtering and multisatellite formation control has received significant attention in the research community. UKIERI Standard Research Award 2008 Best Paper at International Conf. on Intelligent Sensors and Information Processing (ICISIP-2004) Best Paper at WoSco,02, Int. Conf. High-Performance Computing (HiPC, 2002) AICTE career award for young teacher (1997) Senior Member IEEE Multiple IEEE top accessed articles (2009-2010) As an Associate Editor for Autosoft Journal and Technical Committee Member for Intelligent Control at IEEE Control System Society, Dr. Behera actively contributes to the academic community. His laboratory in the Western Lab - 212A of the Department of Electrical Engineering serves as a hub for research in intelligent systems, where he mentors students and collaborates with researchers worldwide on cutting-edge projects in robotics, control systems, and computational intelligence.

Patricia Martinkovaمشاهده پروفایل

دانشیار

• Psychometrics
• Statistical Modeling
• Educational Measurement
• +۶ مورد دیگر

Patricia Martinkova is an Associate Professor at the Faculty of Education, Charles University , a Senior Researcher leading the Department of Statistical Modelling at the Institute of Computer Science, Czech Academy of Sciences , and an Affiliate Associate Professor at the University of Washington (Statistics and Social Sciences). She is also the founder of the Computational Psychometrics Group and the Center for Educational Measurement and Psychometrics at Charles University. Her research focuses on advanced psychometric models and estimators for granular insights in education, psychology, and health, with emphasis on inter-rater reliability , differential item functioning (DIF) , and reproducible research via tools like ShinyItemAnalysis . She has developed software packages ( difNLR , SIAmodules , SIAtools ) and authored the book Computational Aspects of Psychometric Methods. With R (2023). Recent projects include the 2025–2027 EduCoDe (Czech Science Foundation) and 2024–2028 Digital Technologies and Wellbeing (EU-funded). She has received recognition as a Fulbright Alumna (2013–2015) and organized the IMPS 2024 conference (570+ participants). Teaching includes courses on Statistical Methods in Psychometrics and Item Response Theory , incorporating active learning and R-based tools.

Giorgio Scorzelliمشاهده پروفایل

پژوهشگر

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +۶ مورد دیگر

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

• Biostatistics
• Neuroscience
• Cancer Research
• +۳ مورد دیگر

Shengxian Ding is a Postdoctoral Associate at the Yale School of Public Health , focusing on Biostatistics, Neuroscience, and Public Health . Her research develops advanced statistical models for biomedical data analysis. Her work includes 2025: Subgroup Mediation Analysis , 2024: Shape Mediation in Alzheimer’s Disease , and 2023: Tumor Growth Quantification via MRI , reflecting expertise in Regression Models, Neuroimaging, and Computational Biology . Contact: naomi.ding@yale.edu

Andrea Cristofaroمشاهده پروفایل

دانشیار

• Robotic Systems
• Multi-Agent Systems
• Hybrid Systems
• +۴ مورد دیگر

Andrea Cristofaro is an Associate Professor at the Department of Computer, Control and Management Engineering, Sapienza University of Rome. He holds an M.Sc. in Mathematics (2005) from Sapienza University of Rome and a Ph.D. in Information Science and Complex Systems (2010) from the University of Camerino. His academic trajectory includes postdoctoral positions at INRIA Rhône-Alpes (France) and the Norwegian University of Science and Technology, followed by faculty appointments at the University of Camerino and University of Oslo before joining Sapienza in 2019. His research spans: Robotic systems : Fault-tolerant control, multi-agent coordination, and hybrid dynamics Distributed parameter systems : Stability analysis, observer design, and optimal control of PDEs Hybrid systems : Control synthesis for systems with state-driven jumps and switching logic Recent publications (2024-2025) predominantly explore fault-tolerant control architectures for multi-robot systems, decentralized task allocation, adaptive observer designs for biological and thermal systems, and optimal control of reaction-diffusion equations. This reflects a consistent focus on robustness in complex dynamical systems with applications ranging from industrial robotics to biomedical engineering. He serves as Associate Editor for Automatica and IEEE Transactions on Control Systems Technology , and is a member of the IEEE CSS Conference Editorial Board.

Vinh Nguyenمشاهده پروفایل

استادیار

• Advanced Manufacturing
• Industry 4.0
• Human-Robot-Machine Interaction
• +۵ مورد دیگر

Vinh Nguyen is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he directs the Michigan Tech Center for AI and coordinates the NIST-PREP program. His research focuses on advanced manufacturing through Industry 4.0, human-robot-machine interaction, and physics-based/data-driven modeling. He has developed solutions for machining, additive manufacturing, metal forming, and robotic assembly to promote smart and sustainable manufacturing. Prior to joining Michigan Tech in 2022, he was a National Research Council Postdoctoral Fellow at NIST (2020–2022). Dr. Nguyen earned his PhD (2020), MS in Mechanical Engineering (2017), and MS in Electrical & Computer Engineering (2017) from Georgia Institute of Technology. He received dual bachelor’s degrees in Electrical and Mechanical Engineering from Rensselaer Polytechnic Institute (2014). His research portfolio spans Advanced Manufacturing Industry 4.0 and 5.0 Human-Robot Interaction Physics-Based/Data-Driven Modeling Industrial Automation based on his lab’s interdisciplinary focus on human-centric, resilient solutions. His recent publications address trends in Machine Learning for Manufacturing Autonomous Vehicle Sensors Hybrid Additive/Subtractive Manufacturing Augmented/Mixed Reality Interfaces Industrial Robot Diagnostics Material-Specific Machining with keywords spanning Robotics, Data Science, and Industrial Engineering.

• Partial Differential Equations
• High Performance Computing
• Discontinuous Galerkin Methods
• +۳ مورد دیگر

Maurice S. Fabien is an Assistant Professor in the Department of Mathematics at the University of Wisconsin-Madison and a SIAM-MGB Early Career Fellow. In 2025, he will join MIT's Schwarzman College of Computing as an MLK Assistant Professor while on leave from UW-Madison. His research focuses on computational mathematics with specialization in partial differential equations, high-performance computing, and numerical methods including discontinuous Galerkin formulations and multigrid solvers. Research interests span: Development of structure-preserving discretizations for hyperbolic systems GPU-accelerated computational algorithms Hybridizable discontinuous Galerkin (HDG) frameworks Multiscale modeling in porous media and biomechanics Numerical analysis of nonlinear PDEs Publications demonstrate strong focus on: High-order methods for conservation laws Efficient solvers for elliptic/parabolic systems Applications in fluid dynamics and materials science GPU-based performance optimization Error analysis of energy-stable schemes Awards & Honors: SIAM-MGB Early Career Fellowship (2025) Research Team: Austin Anyanwu (Undergraduate) - Finite precision arithmetic Alexis Liu (Alumni) - GPU-accelerated elliptic solvers Patrick Li (Undergraduate) - GPU-based mesh refinement Neer Mehta (Alumni) - Adaptive mesh refinement algorithms Significant involvement since 2007 in STEM diversity initiatives focused on recruitment/retention of underrepresented groups in academia.

• Complex Systems
• Computational Modeling
• Data Science
• +۶ مورد دیگر

Bryan Daniels is an Assistant Professor at Arizona State University's School of Complex Adaptive Systems . He is affiliated with the ASU-SFI Center for Biosocial Complex Systems , Biosocial Complexity Initiative , and holds the title of Senior Global Futures Scientist . Educational Background: Ph.D. in Theoretical Physics from Cornell University. Research Interests: Daniels focuses on predictive modeling of collective behavior in biological systems, exploring how functional aggregates emerge from heterogeneous networks. His work spans computational biology, network science, and dynamical systems. Key Article Trends: Recent publications emphasize collective behavior in biological systems, network dynamics, criticality, and control strategies. Topics range from neural networks and Boolean models to animal group behavior and conflict analysis. Teaching: He teaches courses like Fundamentals of CAS Science and Applied Complex Adaptive Systems at the graduate level. Labs & Affiliations: Leads the Collective Logic Lab and contributes to interdisciplinary research through multiple ASU-SFI collaborations.

Kaiming Biمشاهده پروفایل

استادیار

• Infectious Disease Modeling
• Epidemic Forecasting
• Public Health Decision-Making
• +۱۲ مورد دیگر

Kaiming Bi, Ph.D., is an Assistant Professor in the Department of Management, Policy & Community Health at the University of Texas Health Science Center at Houston (UTHealth Houston) School of Public Health . As an affiliated member of the Center for Health Care Data , his research bridges quantitative methods and public health, focusing on infectious disease modeling , epidemic forecasting , and data-driven health policy . B.S. in Mathematics from Northeastern University (2015) Ph.D. in Industrial Engineering from Kansas State University (2020) Postdoctoral training at University of California San Diego School of Medicine (2020-2021) and University of Texas at Austin (2021-2024) His methodological expertise spans mathematical modeling , machine learning , and optimization , applied to diverse public health challenges including respiratory infections , vector-borne diseases , STIs , and the opioid epidemic . Recent work includes modeling SARS-CoV-2 Omicron subvariants , population immunity dynamics , and multi-pathogen burden projections for the 2023-2024 US winter season. Dr. Bi has received prestigious accolades such as the Pencis Best Researcher Award (2021) and IISE Best Paper (2018). He previously taught graduate courses in Integer Programming , Information Systems , and Industrial Simulation at Kansas State University. Currently leading the Big-data and Infectious Disease Modeling Lab (BI Lab) , he seeks STEM-motivated PhD students to develop computational solutions for epidemic control.

Mine Doganمشاهده پروفایل

استادیار

• Environmental Geophysics
• Groundwater Modeling
• Stochastic Hydrology
• +۳ مورد دیگر

Dr. Mine Dogan serves as Assistant Professor of Environmental Geophysics in the Department of Geological and Environmental Sciences at Western Michigan University, with her office located in 1121 Rood Hall (Kalamazoo, MI). She holds a Ph.D. from Michigan State University (2013) and previously held research positions at Clemson University's Department of Environmental Engineering and Earth Sciences. Education: Ph.D., Michigan State University, 2013 Her research integrates geophysics, hydrology, and environmental engineering to investigate subsurface processes using advanced methodologies including drone-based electromagnetic surveys, time-lapse monitoring, and 4D X-ray computed tomography. Key focus areas include tree root hydrology, contaminant transport in groundwater, permafrost characterization, and macropore flow dynamics in heterogeneous soils. Analysis of her recent publications (2018-2024) reveals strong emphasis on unmanned aerial systems for geophysical data acquisition, visualization of fluid transport mechanisms in porous media, and forensic/environmental applications of electromagnetic methods. Recurring themes include the role of biological structures in hydrological processes and innovative approaches to subsurface imaging. Scientific Awards: No awards documented in source material While her advising activities and grant funding remain unspecified in available information, her publication record demonstrates active collaboration across geophysics, hydrology, and environmental engineering disciplines. Laboratory facilities and research team structures are not detailed in the provided texts.

Pablo G Cámaraمشاهده پروفایل

دانشیار

• Computational Biology
• Genetics
• Biomedical Informatics
• +۶ مورد دیگر

Pablo G Cámara is an Associate Professor of Genetics at the University of Pennsylvania's Perelman School of Medicine. He serves as Senior Fellow at the Institute for Biomedical Informatics and is affiliated with the Center for AI and Data Science in Integrated Diagnostics, the Statistical Center for Single-Cell and Spatial Genomics, and the Department of Genetics. His research focuses on computational approaches to cellular heterogeneity in diseases, particularly brain tumors, integrating topology, geometry, statistics, physics, and computer science. BS, Theoretical Physics, Universidad Autonoma de Madrid (2002) PhD, Theoretical Physics, Universidad Autonoma de Madrid (2006) His work combines single-cell technologies with mathematical frameworks to decode tumor ecosystems and signaling networks. Key themes include metric geometry applications, tumor-genome interactions, and interdisciplinary collaborations in neurodevelopment and cancer research. Recent publications highlight his methodological innovations in single-cell data analysis, tumor biology, CAR T-cell therapy, and quantum computing. Collaborations span immunology, neuro-oncology, and developmental biology domains. The Camara Lab at Penn Medicine develops computational tools for integrating morphometric, transcriptomic, and physiological data, aiming to advance precision oncology and developmental neuroscience through geometric and topological approaches.