جست‌وجوی استاد و پژوهشگر

Erik Johnsonمشاهده پروفایل

استاد

• Smart Structures
• Structural Vibration Control
• Controllable Damping Devices
• +۳ مورد دیگر

Erik Johnson is a Professor of Civil Engineering at an unspecified university, affiliated with the Sonny Astani Department of Civil and Environmental Engineering. He has held leadership roles such as Associate Chair, Interim Chair, and currently serves as Vice Dean for Academic Programs. His research focuses on smart structures, structural vibration control, and computationally-efficient simulation algorithms for dynamical systems, with applications in controllable damping devices and seismic mitigation. Education: B.S., M.S., Ph.D. in Aeronautical and Astronautical Engineering (University of Illinois at Urbana-Champaign), Graduate Certificate in Biblical Studies (Trinity Evangelical Divinity School) Professional Affiliations: Senior Member of AIAA; Member of ASCE and ASME; Chair of ASCE technical committees; Associate Editor, ASCE Journal of Engineering Mechanics His work spans disciplines including control theory, structural engineering, and computational methods. Articles highlight Bayesian approaches, inverse problems, and sensor placement optimization under uncertainty. Erik contributes to advancing seismic resilience and mechatronic systems for civil infrastructure. Scientific Awards: 2001 NSF CAREER Award, 2005 International Association for Structural Safety and Reliability Medal, 2016 University of Illinois Distinguished AE Alumnus Award

Rafael Sebastianمشاهده پروفایل

استاد

• Cardiac Computational Modeling
• Artificial Intelligence in Medicine
• Medical Imaging
• +۴ مورد دیگر

Rafael Sebastian is a Full Professor at Universitat de Valencia and General Director for Science and Research of the Generalitat Valenciana. He leads the Computational Multiscale Simulation Lab (CoMMLab) and collaborates with institutions like Oxford University and Yale University. Department of Computer Science, Universitat de Valencia CoMMLab Founder Spanish Network of Excellence in Cardiac Modeling His research focuses on multi-scale computational models and artificial intelligence for patient-specific cardiac simulations , aiming to improve arrhythmia risk stratification and therapy planning . Key topics include cardiac conduction system modeling , scar-related ventricular tachycardia , and machine learning pipelines for clinical applications. Recent publications emphasize automata-based simulations for atrial arrhythmias, machine learning in arrhythmia localization, and 3D geometric characterization of aortic diseases. Trends show integration of computational modeling with clinical data and medical imaging . Scientific Awards: Best Poster Award, Functional Imaging and Modeling of the Heart (2021) Cum Laude Award, SPIE Medical Imaging (2009) Student Presentation Award (2011) He has supervised 7 PhD/Master students and led grants exceeding €1 million, including projects like iSARC-GENETICS and iCardioTwins , focusing on digital twin technology and cardiac disease stratification .

• Thermal Energy Storage
• Phase Change Materials
• Supercooling
• +۳ مورد دیگر

Konsta Turunen is a Researcher at the Department of Energy and Mechanical Engineering, Aalto University. His expertise focuses on thermal energy storage, phase change materials, and sustainable energy systems. He holds a Doctoral degree (2023), Master's (2017), and Bachelor's (2015) in Engineering and Technology from Aalto University. Research Interests: Turunen specializes in long-term thermal energy storage solutions, particularly using cold-crystallizing materials like erythritol-polyelectrolyte mixtures and binary sugar alcohol-polymer systems. His work addresses supercooling, crystallization kinetics, and material optimization for renewable energy applications. Key contributions include advancing phase-change material design and scaling up heat storage prototypes. Recent Research Trends: From 2019 to 2025, his publications emphasize optimizing storage efficiency, thermal conductivity, and scalability of materials. Notable studies include phase inversion in liquid-air systems (2025) and methods to stabilize supercooling in heat storage systems (2023). His research aligns with UN Sustainable Development Goals related to climate action (SDG 13) and affordable clean energy (SDG 7). Collaborations: Active collaborations involve researchers like M. Järvinen, A. Seppälä, and V. Mikkola at Aalto University. His work has been published in journals such as Applied Energy and ACS Sustainable Chemistry and Engineering . Labs/Teams: Associated with Aalto’s energy and materials research groups, focusing on experimental and computational studies of novel energy storage materials.

Jonathan E. Martinمشاهده پروفایل

استاد

• Synoptic Meteorology
• Atmospheric Dynamics
• Jet Stream Variability
• +۳ مورد دیگر

Jonathan E. Martin is a Professor in the Department of Atmospheric and Oceanic Sciences at the University of Wisconsin–Madison since 1994. He received his Ph.D. in Atmospheric Sciences from the University of Washington in 1992. Research Focus: Mid-latitude cyclones, jet stream dynamics, and weather-climate interactions Key Methods: Multi-scale observational analysis, high-resolution mesoscale modeling, potential vorticity diagnostics Recent Projects: Jet stream waviness trends, subtropical-midlatitude cyclone transformation, snow cover-cyclone feedback His 2025-2024 publications examine extratropical cyclone precipitation distribution, Pacific basin rapid cyclogenesis, and Earth system model validation for cyclone occlusion. Earlier work (2023-2019) addresses: Jet superposition climatology Self-organizing map analysis of jet variability Cyclone-induced Southern Hemisphere waviness Nonlinear jet retraction mechanisms Tornado outbreak jet influences Historical cold pool contraction trends Awarded the 2021 Atmospheric Sciences Librarians International Choice Runner Up for his historical meteorology work, he also authored the textbook Mid-Latitude Atmospheric Dynamics: A First Course (2013). His research spans cyclogenesis, frontogenesis, and climate change impacts on jet streams, with applications in extreme weather forecasting and climate modeling validation.

Andrew Luhmannمشاهده پروفایل

استادیار

• Hydrogeology
• Geochemistry
• Karst Landscapes
• +۶ مورد دیگر

Andrew Luhmann is a faculty member in the Department of Earth and Environmental Science at Wheaton College. Previously, he served as Assistant Professor of Hydrology at New Mexico Institute of Mining and Technology and as a Postdoctoral Researcher at the University of Minnesota. His research spans hydrogeology, geochemistry, and environmental processes. University of Minnesota, Twin Cities – Ph.D., Geology (2011) Wheaton College – B.S., Geology and Biblical & Theological Studies (2006) Research focuses on karst hydrogeology, fluid-rock interactions, and geologic carbon sequestration. Fieldwork includes studies in caves, Yellowstone National Park, and deep-sea environments. Experimental and modeling work examines CO2 injection effects on rock permeability and mechanical properties, as well as seismic monitoring of karst aquifers. Recent publications highlight CO2 sequestration mechanisms, seismic noise tomography for aquifer imaging, and experimental analyses of fluid-rock interactions. Keywords span geochemistry, carbon capture, and subsurface hydrology. NSF and DOE grant support Associate Editor, Hydrogeology Journal Primary Representative, GSA Karst Division JTPC (2018–2020) GSA Karst Division Awards Committee (2022–present) Luhmann teaches courses like Hydrogeology, Environmental Geochemistry, and Senior Capstone Seminar. His lab integrates fieldwork, lab experiments, and numerical modeling to address water resource challenges and carbon sequestration.

• Structural Dynamics
• Machine Learning Applications
• Non-Linear Systems
• +۳ مورد دیگر

Professor Keith Worden is a Professor of Mechanical Engineering at the University of Sheffield's School of Mechanical, Aerospace and Civil Engineering. His research focuses on applications of advanced signal processing and machine learning to structural dynamics, particularly in aerospace systems. He has held this position since 1995 and has contributed significantly to the field of structural health monitoring (SHM), including work on nonlinear system analysis and damage detection. His work emphasizes pragmatic engineering solutions and collaboration with industries like aerospace and offshore sectors. Education: Holds a degree from York University and a PhD in Mechanical Engineering from Heriot-Watt University. Career highlights include research at Manchester University before joining Sheffield. Research Interests: Specializes in structural dynamics, SHM using machine learning, nonlinear systems, and vibration analysis. Key themes include population-based SHM frameworks, damage prognosis, and environmental adaptation in monitoring systems. His group develops algorithms for automated inspection and diagnosis, leveraging neural networks, genetic algorithms, and other biological-inspired methods. Articles Trends: Recent publications focus on population-based SHM methodologies, transfer learning applications, and algorithm development for novelty detection, damage localization, and risk-informed decision frameworks. His work bridges theoretical advancements with practical engineering challenges. Grants & Advising: Extensive grants and collaborations in SHM, wind turbine monitoring, and aerospace structures. Advises on projects involving machine learning in structural dynamics and probabilistic modeling. Labs & Teams: Leads research groups exploring computational tools for SHM, including the application of Gaussian processes, Bayesian methods, and data-driven models. Collaborates internationally on projects such as the RAPTOR telescope system and offshore wind farm monitoring.

Andrei L. Badescuمشاهده پروفایل

استاد

• Risk and Ruin Theory
• Matrix Analytic Methods
• Stochastic Claim Reserving
• +۶ مورد دیگر

Andrei L. Badescu is a Professor of Actuarial Science and Director of the Master of Financial Insurance in the Department of Statistical Sciences at the University of Toronto. His academic leadership spans editorial roles at Insurance: Mathematics and Economics and program direction for graduate actuarial programs. His educational foundation includes: BSc in Mathematics and Economics from Bucharest University of Economic Studies (1998) MSc in Mathematics and Economics from Bucharest University of Economic Studies (2000) PhD in Actuarial Science from Western University (2004) He completed postdoctoral training at the University of Waterloo (2006) before joining the University of Toronto faculty in 2006. Research interests evolved from foundational work in Risk and Ruin Theory using Matrix Analytic Methods to contemporary applications in Stochastic Claim Reserving, Dependence Modelling, and Predictive Analytics. Current emphases include Telematics risk assessment and Insurance Data Science, leveraging advanced statistical techniques for real-world insurance challenges. Recent publications (2021-2025) reveal a strategic shift toward data-driven insurance solutions. Key trends include micro-level claim reserving via inverse probability weighting, telematics-based driving risk modeling using unsupervised learning, and mixture-of-experts frameworks for portfolio ratemaking. These works bridge traditional actuarial science with machine learning, particularly in handling censored data and operational risk. Professor Badescu mentors five doctoral students (Spark Tseung, Sebastian Calcetero, Ian Weng, Sophia Chan, Hassan Abdelrahman) and two master's students (Kaihua Sun, Yifeng Ge). His administrative leadership includes directing the Master of Financial Insurance program and previously overseeing the Data Science concentration in the Master of Applied Computing. His research group develops practical tools like the LRMoE.jl software package for actuarial loss modeling, while future work targets telematics integration and insurance-specific artificial intelligence applications.

Frederic Friscourtمشاهده پروفایل

دانشیار

• Chemical Biology
• Glycobiology
• Bioorthogonal Chemistry
• +۳ مورد دیگر

Frederic Friscourt is an Associate Professor in Chemical Biology at the University of Bordeaux, France, with co-affiliations at the European Institute of Chemistry and Biology (IECB) and the Institute of Molecular Sciences (ISM, CNRS UMR 5255). He leads the Friscourt Lab within the "Molecular Photonics, Chemical Biology and Imaging" (PMCI) division at ISM. His educational background includes: M.Sc. in Chemistry & Chemical Engineering Diploma (2004), University of Clermont-Ferrand PhD in Chemistry, University of Glasgow (supervised by Prof. Pavel Kočovský) Postdoctoral Fellowship (2009-2014), Complex Carbohydrate Research Center (GA, USA) under Prof. Geert-Jan Boons Friscourt's research focuses on unravelling functional roles of mammalian complex glycans through innovative chemical tool development. His lab integrates organic chemistry, enzymatic synthesis, biochemical assays, and cell imaging to advance glycomics technologies, with emphasis on bioorthogonal chemistry for selective glycan labeling in living systems. Key methodologies include strain-promoted cycloadditions and sydnone-based probes enabling real-time visualization of dynamic glycan functions. Analysis of his 15 most recent publications (2018-2023) reveals a dominant trend in developing bioorthogonal reaction platforms (particularly sydnone-cyclooctyne systems) for glycan imaging, spanning fundamental organic chemistry to biological applications in DNA labeling, sialome tagging, and neural injury studies. This work bridges chemical innovation with critical biological questions in reproductive biology, neurobiology, and host-pathogen interactions. Notable awards include: CNRS ATIP-Avenir Award for outstanding young principal investigator (2017) IdEx Bordeaux Junior Chair of Excellence in Chemical Biology Award (2014) ACS Outstanding Postdoctoral Research Chemist Award (2012) Friscourt actively mentors a growing research group, evidenced by onboarding of new members (Aïnhoa Lafourcade, Lucile Jeanneret, Audrey Baeyens, Ludovic Hirsoux in 2024-2025), and has secured competitive funding from multiple agencies. His leadership extends to organizing major scientific events including the 58th International Conference on Medicinal Chemistry (RICT 2024) and the 4th Scientific Days of the GDR ChemBio at IECB. The Friscourt Lab operates at the chemical biology frontier within ISM's PMCI division, maintaining strong collaborations and contributing to Bordeaux's prominence in glycan research through its unique integration of synthetic chemistry and live-cell imaging technologies.

Petros Dellaportasمشاهده پروفایل

استاد

• Bayesian Statistics
• Machine Learning
• Financial Econometrics
• +۷ مورد دیگر

Petros Dellaportas holds dual appointments as a Professor of Statistical Science at University College London (UCL) and a Professor of Statistics at the Athens University of Economics and Business (AUEB). His research focuses on Bayesian statistics, machine learning, financial econometrics, and dynamic pricing. He leads projects on topics such as Poisson processes for cybersecurity, reservoir computing for macroeconomic forecasting, and probabilistic fault detection in wind parks. His recent publications emphasize advancements in Bayesian methods, variational autoencoders, and spatio-temporal point processes. Dellaportas has supervised over 20 PhD students, contributing to areas like stochastic volatility models and inverse reinforcement learning. He co-founded Thales and Friends, an organization bridging mathematics and cultural activities, and organizes the Greek Stochastics workshop series on topics ranging from causal learning to computational statistics. Key projects include anomaly detection in VAT networks and scalable Gaussian process models. His work often integrates statistical theory with applications in finance, sports analytics, and environmental science. Dellaportas maintains active collaborations with institutions globally, advancing interdisciplinary research and methodological innovations in statistical science.

Kristin O'Gradyمشاهده پروفایل

استادیار

• Quantitative MRI Methodologies
• Neurological Diseases (Multiple Sclerosis)
• Spinal Cord Imaging
• +۳ مورد دیگر

Dr. Kristin O'Grady is an Assistant Professor in the Department of Biomedical Engineering and Department of Radiology & Radiological Sciences at Vanderbilt University's School of Engineering. Her research focuses on developing quantitative MRI methodologies for the brain and spinal cord, particularly improving spinal cord MRI for neurological diseases like multiple sclerosis. She specializes in diffusion tensor imaging, functional connectivity analysis, and high-field MRI applications. Her work spans advanced imaging techniques including MP2RAGE, susceptibility-weighted MRI, and phase imaging, with a focus on clinical feasibility and disease markers. She has contributed to studies on spinal cord morphometry, paramagnetic rim lesions, and biological interactions affecting CNS structure. No scientific awards or grants are explicitly listed in the provided materials. Dr. O'Grady collaborates across interdisciplinary teams within the School of Engineering, focusing on translational research in neuroimaging technologies.

W. Clem Karlمشاهده پروفایل

استاد

• Computational Imaging
• Statistical Signal Processing
• Inverse Problems
• +۴ مورد دیگر

W. Clem Karl is Professor and Chair of the Electrical and Computer Engineering Department at Boston University's College of Engineering, with additional appointments in Biomedical Engineering and Systems Engineering. Education: PhD in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 1991 Research Interests: Professor Karl's work focuses on computational imaging, statistical signal processing, inverse problems, and biomedical applications. His research develops advanced algorithms for image reconstruction in medical imaging, synthetic aperture radar, and remote sensing, emphasizing statistical methods for signal estimation and detection. Scientific Awards: Fellow of IEEE Fellow of AIMBE Fellow of BMES IEEE Vice President of Publication Services and Products (2025) Inaugural Editor-in-Chief of IEEE Transactions on Computational Imaging (2014-2017) Editor-in-Chief of IEEE Transactions on Image Processing (2012-2014) ECE Award for Excellence in Teaching (2000) Teaching: EC 401 Signals and Systems EC 416 Introduction to Digital Signal Processing EC 505 Stochastic Processes EC 717 Image Reconstruction and Restoration Advising: Due to high email volume, Professor Karl does not respond to individual student inquiries; prospective students must apply directly through College of Engineering degree programs. Labs and Affiliations: Director of the Information & Data Sciences Laboratory, with affiliations in the Center for Information and Systems Engineering, Center for Space Physics, Neurophotonics Center, Center for Nanoscience and Nanobiotechnology, and Division of Systems Engineering.

Melvin Wongمشاهده پروفایل

استادیار

• Transportation Engineering
• Machine Learning
• Urban Mobility
• +۲ مورد دیگر

Melvin Wong is an Assistant Professor in the Department of Urban Planning and Transportation within the Built Environment school at Eindhoven University of Technology. His research focuses on transportation engineering, machine learning applications in urban mobility, reinforcement learning for traffic systems, and sustainable transportation solutions. He utilizes advanced computational methods including graph neural networks, generative AI, and physics-informed models to address challenges in traffic prediction, electric vehicle infrastructure, and urban design. His research interests encompass transportation optimization, spatiotemporal modeling, generative design methods, and behavioral analysis in urban systems. Recent publications demonstrate a strong focus on AI-driven solutions for traffic management, battery-swapping systems, and multimodal design optimization. Dr. Wong has received recognition including the Best Research Paper Award (2024) and Swiss Government Excellence Scholarship (2020). He contributes to academic activities through conference presentations, peer reviews, and course development in urban mobility and big data analytics.

Michiel E. Hochstenbachمشاهده پروفایل

دانشیار

• Numerical Linear Algebra
• Ill-posed and Inverse Problems
• Numerical Analysis
• +۳ مورد دیگر

Michiel E. Hochstenbach is an Associate Professor at Eindhoven University of Technology (TU/e), affiliated with the EAISI High Tech Systems and EAISI Foundational initiatives. He holds positions in the Department of Mathematics within the School of Mathematics and Computer Science. His research focuses on numerical linear algebra, ill-posed problems, and computational methods. Hochstenbach earned his PhD from Utrecht University (2003) and held roles at Düsseldorf University and Case Western Reserve University before joining TU/e in 2006. His work spans numerical analysis, scientific computing, and software development for numerical algorithms. He has secured grants including an NSF (2004–2007) and NWO Vidi (2012–2017). His research group advises PhD students who have also received awards for their contributions. Key research areas include matrix computations, inverse problems, and optimization techniques. Recent publications address subspace methods, gradient algorithms, and matrix factorization innovations. He is an editorial board member of several journals and actively contributes to interdisciplinary computational science projects. Notable awards include the NWO Vidi Award (2011) recognizing his rapid advancements in matrix methods. His teaching includes courses on calculus, linear algebra, and mathematics fundamentals. Hochstenbach collaborates internationally, contributing to computational mathematics and sustainable development through algorithmic efficiency.

Piya Palمشاهده پروفایل

استاد

• High Dimensional Statistical Signal Processing
• Compressive Sensing
• Tensor Methods
• +۴ مورد دیگر

Prof. Piya Pal is a Professor in the Department of Electrical and Computer Engineering at the University of California, San Diego. Her research focuses on high-dimensional statistical signal processing, energy-efficient sampling techniques, and covariance-driven inference. She previously held an Assistant Professor position at the University of Maryland, College Park, and was affiliated with the Institute for Systems Research. Education: Ph.D. in Electrical Engineering from California Institute of Technology (2013). Notable achievements include the NSF CAREER Award (2016) and the 2014 Charles and Ellen Wilts Prize for her thesis on sparse sampling and estimation. Her work emphasizes structured sampling and robust algorithms for undersampled data analysis, with applications in sensor arrays, compressive sensing, and optical imaging. Research Interests: Energy-efficient sparse array design Correlation-aware sparse estimation Covariance compression and statistical inference Tensor methods in machine learning High-resolution imaging systems Publications highlight advancements in sparse array geometries (nested/coprime samplers), Cramér-Rao bound analysis, and hybrid beamforming. Recent work explores super-resolution imaging and millimeter-wave channel sensing with learned empirical priors. Her contributions address fundamental trade-offs between sample size, resolution, and domain knowledge integration. Scientific Awards: NSF CAREER Award (2016) 2014 Charles and Ellen Wilts Prize (Caltech) Advising & Grants: Current research is supported by NSF CAREER funding. Her lab focuses on interdisciplinary projects combining signal processing with medical imaging and wireless communication challenges.

Jun Zhangمشاهده پروفایل

استادیار

• Control Systems
• Robotics
• Smart Materials
• +۳ مورد دیگر

Jun Zhang is an Assistant Professor in the Mechanical Engineering Department at the University of Nevada, Reno. His research focuses on control systems, robotics, smart materials, and artificial muscles, with a particular emphasis on biomimetic, soft, and assistive robotics applications. He leads the Smart Robotics Lab, which develops technologies like twisted string actuators for advanced robotic systems. Dr. Zhang teaches courses including ME410 (Introduction to System Control) and ME422/622 (Introduction to Robotics). While specific educational details are not provided in the text, his work indicates expertise in mechanical engineering and robotics. His research spans from fundamental actuator modeling to practical applications in soft robotics and haptic systems. Over 30 articles from 2012–2025 highlight his contributions to smart materials, hysteresis compensation, and robotic actuation technologies. Current projects emphasize interdisciplinary approaches to enable more capable and adaptive robotic systems. Prospective students are encouraged to contact him regarding research assistantships requiring 12+ weekly hours. No scientific awards are explicitly listed. The Smart Robotics Lab’s work integrates innovation with real-world applications, such as assistive devices and advanced manufacturing techniques.