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Waheed U. BajwaView profile

Professor

• High-dimensional inference
• Inverse problems
• Compressed sensing
• +6 more

Waheed U. Bajwa is a Professor in the Department of Electrical & Computer Engineering at Rutgers, The State University of New Jersey, based in the Computing Research & Education Building (CoRE) in Piscataway, NJ. His research spans high-dimensional inference, inverse problems, compressed sensing, and statistical signal processing with critical applications in biological sciences, wireless communications, complex networked systems, and radar/image processing. His educational background includes a PhD (2009) and MS (2005) in Electrical Engineering from the University of Wisconsin-Madison, and a BE (2001) from Pakistan's National University of Sciences & Technology. Bajwa's work focuses on developing theoretical frameworks for extracting meaningful information from high-dimensional data, with direct impact on wireless communications infrastructure and biomedical imaging. His methodologies bridge statistical theory with real-world engineering challenges, particularly in resource-constrained sensing environments. He has received major recognition including the NSF CAREER Award (2015), Army Research Office YIP Award (2014), and Rutgers University Presidential Merit Award (2016). His students have earned best paper awards at IEEE conferences, reflecting his mentorship excellence. NSF CAREER Award (2015) Army Research Office YIP Award (2014) Rutgers Presidential Merit Award (2016) ECE Professor of the Year (2016) IEEE Senior Member (2013) Packard Fellowship Nominee (2012) Bajwa has advised multiple graduate students whose research has been recognized with competitive paper awards. His work is supported by significant federal grants, including the NSF CAREER award and ARO funding, enabling cutting-edge research in signal processing theory and applications. He directs the INSPIRE Lab (Information Processing, Statistical Inference, and Related Engineering) at Rutgers, which develops novel algorithms for modern sensing challenges across biomedical, communications, and defense domains.

Thanh Trung NguyenView profile

Associate Professor

• Inverse Problems
• Numerical Analysis
• Optimization
• +6 more

Thanh Trung Nguyen is an Associate Professor and Graduate Program Coordinator in the Department of Mathematics at Rowan University's College of Science & Mathematics. He holds a PhD in Engineering Mathematics from Vrije Universiteit Brussel and completed postdoctoral research at the Austrian Academy of Sciences, UNC Charlotte, and Iowa State University. Dr. Nguyen's research focuses on inverse problems for partial differential equations with applications in subsurface detection, through-wall imaging, water quality modeling, and smart farming. His work combines mathematical theory with computational methods to solve practical engineering challenges, including infrared thermography for buried object detection, electromagnetic induction for subsurface imaging, and AI-driven agricultural monitoring. Dr. Nguyen has secured research funding from NSF, US Army Corps of Engineers, and NJ Department of Agriculture, and serves as a member of the Society for Industrial and Applied Mathematics. Nguyen's publication record demonstrates consistent contributions to inverse problem methodologies applied to environmental monitoring, nondestructive testing, and advanced material analysis. His current projects include developing globally convergent methods for coefficient identification and applying artificial intelligence to agricultural challenges.

• Inverse Problems
• Computational Algorithms
• Medical Imaging
• +3 more

Rosemary Renaut is a Professor and Associate Director for Faculty Development at the School of Mathematical and Statistical Sciences, Arizona State University. Her research focuses on computational algorithms for solving inverse problems in medical and geophysical applications, emphasizing regularization techniques and numerical linear algebra. She holds a Ph.D. in Applied Mathematics from the University of Cambridge (1985). Education: Ph.D. in Applied Mathematics, University of Cambridge, UK (1985) Research Interests: Dr. Renaut specializes in developing algorithms for solving ill-posed inverse problems, particularly in medical imaging (e.g., PET, MRI) and geophysics (e.g., gravity/magnetic data inversion). Her work addresses challenges like data noise, model ill-posedness, and parameter regularization. She has contributed to methods like total variation regularization, χ²-based parameter estimation, and iterative solvers (e.g., LSQR). Articles Trends: Recent publications (2020–2025) emphasize large-scale inversion techniques, sparsity-driven reconstruction, and efficient regularization parameter estimation. Key applications include geophysical exploration (e.g., iron oxide deposits, kimberlites) and medical imaging (e.g., EEG source localization). Her work often combines numerical methods with domain-specific challenges, such as joint inversion of multimodal geophysical data. Awards: SIAM Fellow (2022) Technische Universitaet Muenchen Ambassador (2013) J.T. Knight Prize in Mathematics (1982) Grants & Service: Dr. Renaut has led numerous grants (e.g., NSF, DOD) on inverse problems and computational methods. She serves on editorial boards (BIT, SIAM News) and has held roles like Program Director at the NSF. Her service also includes mentoring junior faculty and advancing computational biosciences education. Labs/Teams: Collaborates with interdisciplinary teams in geophysics (e.g., gravity/magnetic data inversion) and biomedical imaging (e.g., PET parametric imaging). Active in conferences like International Conference on Spectral and High Order Methods.

• Computational Electromagnetics
• Finite Element Methods
• Inverse Problems
• +3 more

Dr Paul Ledger is an Honorary Research Fellow at Swansea University's Faculty of Science and Engineering and a Professor of Computational Mathematics at Keele University. His expertise spans computational electromagnetics, finite element methods, and inverse problems. He specializes in high-order finite element simulations for Maxwell's equations, preconditioning techniques, and coupled magneto-mechanical systems in MRI scanner design. Research interests include hp-finite element software development, error estimation, reduced order modeling, and applications in biomedical imaging and metal detection. He has supervised PhD research on MRI scanner vibrations and contributed to industrial projects on electromagnetic field modeling. His work bridges applied mathematics, engineering, and data science, with recent focus on probabilistic methods for inverse problems. Publications emphasize magnetic polarizability tensors, eddy current simulations, and MRI system optimization. He maintains active collaborations in multi-physics computational modeling and has presented widely at international conferences such as ICOSAHOM and MAFELAP. Teaching roles include modules in problem-solving, data science statistics, and computational engineering methods.

Longxiu HuangView profile

Assistant Professor

• Data/Signal Processing
• Applied Harmonic Analysis
• Machine Learning
• +4 more

Longxiu Huang is an Assistant Professor at Michigan State University (MSU), holding dual appointments in the Department of Computational Mathematics, Science and Engineering (CMSE) and the Department of Mathematics. Prior to this, she was an Assistant Adjunct Professor at UCLA's Department of Mathematics. Her research focuses on Data/Signal Processing, Applied Harmonic Analysis, Machine Learning, Random Matrix/Tensor Analysis, Dynamical Sampling, and Optimization. Education : Ph.D. in Mathematics, Vanderbilt University (2014-2019) M.S. in Mathematics, Fudan University (2012-2014) B.S. in Mathematics, Sun Yat-Sen University (2009-2012) B.S. in Software Engineering, Sun Yat-Sen University (2008-2009) Research Interests : Huang's work bridges theoretical mathematics and applied computational methods. She explores signal reconstruction in dynamical systems, tensor decompositions for high-dimensional data, robust matrix/tensor completion, and optimization algorithms for ill-conditioned problems. Her contributions include CUR-based methods for low-rank approximations and sampling strategies for sparse signals on graphs. Professional Activities : She co-organizes the One World MINDS Seminar, a global initiative in mathematical data science. Her research has been published in top journals like SIAM Journal on Imaging Sciences and Applied and Computational Harmonic Analysis, with a focus on theoretical guarantees and practical applications in imaging and machine learning. Lab/Teams : Active collaborator in interdisciplinary projects at MSU, particularly in computational mathematics and machine learning applications.

• Geophysics
• Seismology
• Wave Propagation
• +3 more

Dr. Dirk-Jan van Manen is a Lecturer at the Department of Earth and Planetary Sciences at ETH Zürich, located in Zurich, Switzerland. His research focuses on geophysics, seismology, and wave propagation with a strong emphasis on metamaterials and experimental wave physics. He is affiliated with the Institut für Geophysik and leads projects in elastic wavefield analysis, acoustic metamaterials, and non-destructive evaluation techniques. His recent work explores topics such as passive speech classification using mechanical neural networks, acoustic cloning, and the design of self-inverting space-time media. His research bridges theoretical models with practical applications in fields like seismic data processing, glaciology, and materials science. Dr. van Manen’s contributions include pioneering immersive wave experimentation setups and innovative methods for signal interpolation and scattering analysis in challenging environments.

Joe LoVetriView profile

Professor

• Microwave Imaging
• Computational Electromagnetics
• Inverse Problems
• +3 more

Joe LoVetri, Ph.D., P.Eng., is a Professor and Head of the Department of Electrical and Computer Engineering at the University of Manitoba's Faculty of Engineering. His research focuses on computational electromagnetics, microwave imaging, and inverse problems, with applications in biomedical and agricultural domains. He leads the Electromagnetic Imaging Laboratory (EIL), which develops advanced imaging systems for applications like grain monitoring and breast cancer detection. LoVetri teaches graduate and undergraduate courses including ECE 8200 (Advanced Engineering Electromagnetics) and ECE 7810 (Computational Electromagnetics). Education: M.A. Phil. (2006), University of Manitoba Ph.D. E.E. (1991), University of Ottawa M.Sc. E.E. (1987), University of Manitoba B.Sc. E.E. (1984, with distinction), University of Manitoba Research Interests: LoVetri's work spans microwave tomography, electromagnetic imaging techniques, optimization methods, and sensor systems for biomedical and agricultural safety. His lab's projects include the Electromagnetic Imaging Group Online Repository (EIGOR) for experimental scattering data and the development of multimodal imaging systems combining ultrasound and microwave technologies. Key areas include grain bin monitoring, breast cancer imaging, and the use of machine learning for image reconstruction. Facilities & Equipment: The EIL is equipped with advanced instrumentation such as network analyzers, signal generators, and a resonant chamber system for high-precision measurements. Collaborations include funding from NSERC and Western Economic Diversification Canada. Grants & Collaborations: Supported by grants from NSERC and industry partners, LoVetri's research bridges academia and practical applications, particularly in healthcare and agriculture. His lab's innovations include novel calibration methods and prior-information integration for imaging algorithms.

Catherine HighamView profile

Research Fellow

• Machine Learning
• Quantum Optics
• Systems Biology
• +4 more

Dr. Catherine Higham is a Research Fellow in the School of Computing Science at the University of Glasgow, affiliated with Prof. Rod Murray-Smith's Inference, Dynamics and Interaction group. Her work focuses on applying machine learning and statistical methods to inverse problems in quantum optics and systems biology. Previously, she held roles as a Research Associate in the EU-funded TiMet project (2012–2015) and completed a Daphne Jackson Trust Fellowship (2006–2008) followed by a Lord Kelvin/Adam Smith PhD Scholarship (2008–2012) at Glasgow. Education includes a Mathematics degree from the University of Oxford, an MBA from City University London (sponsored by Novaction), and a PhD in computational biology from the University of Glasgow under Prof. Darren Monckton. Her interdisciplinary research spans machine learning, statistical inference, high-performance computing, and collaborative work with experimental scientists. Research interests emphasize developing algorithms for data-driven scientific understanding, particularly in quantum technologies and biomedical applications. Key contributions include Bayesian deep inversion, quantum annealing for neural networks, and parameter estimation in biological systems. She has also explored applications in single-pixel imaging and LiDAR optimization. Scientific awards include the Daphne Jackson Trust Fellowship and Lord Kelvin/Adam Smith PhD Scholarship. Grants include EPSRC UK Quantum Technology Programme funding. Catherine's work bridges computational methods with real-world challenges in physics, biology, and linguistics, supported by collaborations across disciplines.

Melanie BirkeView profile

Professor

• Nonparametric Statistics
• Functional Data Analysis
• Inverse Problems
• +3 more

Prof. Dr. Melanie Birke is a faculty member at the University of Bayreuth , holding the Professorship for Mathematical Statistics within the Faculty of Mathematics, Physics and Computer Science . Her research spans multiple areas of statistics, including nonparametric methods, functional data analysis, inverse problems, goodness-of-fit tests, and random matrices. Women's Representative of the Mathematical Institute DAV Correspondent for actuarial training exemptions Research Interests are focused on nonparametric statistics, functional data, and inverse problems. She develops asymptotic theory for estimation and testing procedures, particularly in high-dimensional or infinite-dimensional spaces, with applications to real-world issues like image distortion and data collection errors. Her work also includes constructing goodness-of-fit tests for regression models and functional data, ensuring robust statistical analysis. Publications highlight her contributions to quantile regression, symmetry testing in inverse problems, and shape-constrained density estimation. A recurring theme involves improving statistical consistency by addressing model misspecification through advanced nonparametric techniques. Statistical Consulting is offered to both internal and external stakeholders, emphasizing methodological guidance during experimental design to prevent data inconsistencies. She also supervises Bachelor's and Master's theses in nonparametric statistics and financial mathematics.

Yassine MHIRIView profile

Associate Professor

• Statistical Signal Processing
• Computational Imaging
• Radio-Interferometry
• +7 more

Yassine MHIRI is an Associate Professor at the LISTIC laboratory of Université Savoie Mont Blanc. His research focuses on statistical signal and image processing, computational imaging, and their applications in radio-interferometry, hyperspectral imaging, and sensor arrays. Key contributions include methodologies for robust statistical modeling, unrolled algorithms, and plug-and-play neural networks. He holds a Ph.D. from Université Paris-Saclay and conducted postdoctoral research at Université Paris Cité on hyperspectral imaging within the ANR project Fu-MultiSpoc. Education: Ph.D. in Statistical Signal Processing (2023), Université Paris-Saclay M.S. in Electronics and Signal Processing (2018), ENSEEIHT, Toulouse Professional Experience: Associate Professor (2024–present), LISTIC, Université Savoie Mont Blanc Postdoctoral Researcher (2023–2024), MAP5 Lab, Université Paris Cité PhD Research (2020–2023), SATIE Lab, Université Paris-Saclay Research Engineer (2019–2020), Phasics (wavefront sensor algorithms) His research integrates statistical learning, inverse problems, and neural networks to address challenges in radio interferometry and hyperspectral imaging. Notable work includes developing the pfb-clean library for MeerKAT telescope imaging and advancing robust EM algorithms for outlier-resistant imaging. His recent focus includes hyperspectral denoising and Fourier-based reconstruction techniques. Grants & Projects: ANR Fu-MultiSpoc (hyperspectral imager development), contributions to the Africanus imaging suite. Labs & Collaborations: LISTIC (current), SATIE (PhD), MAP5 Lab (postdoc), and Gipsa-Lab (collaboration).

Yajing YANView profile

Lecturer

• Remote Sensing
• Geophysics
• Data Assimilation
• +5 more

Yajing YAN is a Lecturer (Habilitation à Diriger des Recherches) at Université Savoie Mont Blanc, affiliated with the LISTIC laboratory. She specializes in remote sensing, geophysics, and data assimilation with particular focus on Earth deformation monitoring and natural hazard prediction. Her research integrates multi-temporal SAR interferometry, statistical methods, and machine learning to analyze displacement data for applications in volcanology, glaciology, and mountain environment monitoring. Education and Professional Path: PhD in Remote Sensing (2008-2011, LISTIC), followed by post-doctoral positions at the University of Liège (2012-2014) and University of Rennes 1 (2012). Since 2014, she has held her current position at Université Savoie Mont Blanc. Research Themes: Multi-temporal SAR interferometry, data fusion/assimilation, statistical methods, machine learning applications in geoscience. Active in projects like CNES MagmaTrack, ANR REPED-SARIX, and MIAI, focusing on volcanic deformation, glacier dynamics, and environmental imagery analysis. Teaching: Polytech Annecy-Chambéry, covering algorithmic, databases, embedded systems, signal/image processing, and Problem-Based Learning (PBL) in Environmental Imagery. Leadership Roles: Member of CNU 61, responsible for the 'Systèmes Numériques et Instrumentation' specialty, and organizes conferences on statistical learning and télédétection. Co-editor of 'Inversion & Assimilation de données de télédétection' and leads the 'Télédétection & IA' initiative under GdR ISIS.

Vladimir PuzyrevView profile

Research Fellow

• Deep Learning
• Geophysics
• Numerical Modeling
• +3 more

Vladimir Puzyrev is an Adjunct Research Fellow at Curtin University’s School of Earth and Planetary Sciences (EPS) and a Senior Research Scientist at Schlumberger-Doll Research. His work bridges geophysics, numerical modeling, and machine learning. He specializes in deep learning applications for geophysical inversion, electromagnetic exploration, and seismic facies classification. Research interests include: deep learning for geophysical data analysis, 3D modeling and inversion, isogeometric and finite element methods, and sparse linear solvers. His interdisciplinary approach integrates computational geosciences with advanced numerical techniques. Publications focus on applying neural networks to mineralization prediction, seismic facies classification, and electromagnetic inverse problems. Key contributions include frameworks for generative adversarial networks in geophysical modeling and Monte Carlo dropout for uncertainty quantification in geochemical data. Affiliations: Curtin University (Adjunct Research Fellow), Schlumberger Limited (Senior Research Scientist). Active in professional societies like SEG and EAGE. His work spans academic and industrial sectors, emphasizing real-world applications of computational geoscience.

• Inverse Problems
• Variational Methods
• Total Variation Regularization
• +7 more

Jose A. Iglesias Martínez is an Assistant Professor in the Department of Applied Mathematics at the University of Twente, affiliated with the Mathematics of Imaging and AI (MIA) group within the Faculty of Electrical Engineering, Mathematics and Computer Science. He conducts research at the intersection of variational analysis, inverse problems, shape analysis, and machine learning, with a geometric perspective on regularization and optimization. University: University of Twente School: Faculty of Electrical Engineering, Mathematics and Computer Science Department: Department of Applied Mathematics Research Group: Mathematics of Imaging & AI (MIA) His research focuses on analytic and variational methods for inverse problems in imaging, particularly involving total variation and related regularization models. The geometric structure of function domains and convex geometry in minimization problems plays a central role in his work. He applies these methods to imaging, shape analysis, and machine learning, with strong mathematical foundations in PDEs, functional analysis, and optimization. The recent publications reflect a consistent focus on variational regularization, extremal point analysis, sparse optimization, and PDE-constrained problems. Key themes include total variation, TGV, nonlocal perimeters, and shape optimization, with applications in image denoising, optical flow, and fluid dynamics. His work spans both theoretical convergence analysis and algorithmic development, often with computational implementations. Scientific Awards and Recognition: Member of the Editorial Board, Numerical Functional Analysis and Optimization (since August 2023) He actively teaches in applied mathematics, including courses such as Analysis 3, Mathematics behind Data-Driven Methods, and interdisciplinary topics like Art, Mathematics and Technology. He has supervised tutorials and projects in calculus, linear algebra, and modeling. His academic training includes dual MSc degrees in Mathematics and Telecommunication Engineering from Madrid, a PhD (2015) and habilitation (2021) in Mathematics from the University of Vienna, followed by postdoctoral work at the Radon Institute in Linz. He has no listed students yet, but is actively publishing and contributing to the mathematical imaging community. He is involved in editorial work and maintains a strong publication record in top-tier applied mathematics journals.

Matti StenroosView profile

Senior Lecturer

• Biomedical Engineering
• Applied Physics
• Computational Neuroscience
• +5 more

Matti Stenroos is a Senior Lecturer at Aalto University's Department of Neuroscience and Biomedical Engineering , where he also serves as Vice Head of the Department . His research focuses on biomedical engineering, neuroimaging, and electromagnetic field applications. Doctoral degree in Engineering and Technology (2008), Helsinki University of Technology Licentiate degree in Engineering and Technology (2005), Helsinki University of Technology Master's degree in Engineering and Technology (2002), Helsinki University of Technology Stenroos specializes in Transcranial Magnetic Stimulation (TMS) , Magnetoencephalography (MEG) , and Electromagnetic Field Modeling . His work improves brain stimulation devices , neural source localization , and biomedical signal analysis , contributing to depression treatment and pain management. Recent articles focus on Pulse-Width Modulation and multi-locus stimulation systems . Stenroos received the IFMBE Young Investigator Competition award in 2005. He has organized international conferences like the Science Factory: TMS-EEG Summer School and participates in editorial activities. As principal investigator for the Device-Independent Real-Time MEG/EEG Source Localization project (2015-2016), he pioneered adaptive neuroimaging techniques. His laboratory collaborates globally on biomedical electromagnetic modeling and clinical neuroengineering applications .

• Computer Vision
• Digital Image Processing
• Mathematical Analysis
• +3 more

Dr. Onofre Martorell Nadal is an Assistant Professor in the Department of Applied Mathematics at the School of Mathematical Sciences and Computer Science, University of the Balearic Islands (UIB). He earned his PhD in 2022 from UIB's Information and Communication Technologies program, supported by an FPI-CAIB predoctoral fellowship. His research focuses on computer vision, digital image processing, and mathematical analysis, particularly in detecting geometric structures in images and image registration techniques. Education: BSc in Mathematics (2016), University of the Balearic Islands MSc in Computer Vision (2017), Autonomous University of Barcelona Research Interests: He specializes in image reconstruction for inverse problems and is a member of UIB's Image Processing and Mathematical Analysis (TAMI) research group. His work includes collaborations with the Computer Vision group at the University of Siegen (Germany). Teaching: Currently teaches Mathematics II - Calculus (Computer Science degree) and Software Laboratory and Problems I (Mathematics degree). Previously taught Linear Algebra II and optimization techniques for deep learning at the master's level. Scientific Awards: FPI-CAIB Predoctoral Fellowship for PhD research Labs & Teams: Active in MIA (Mathematics, Images and Learning) research group and TAMI (Image Processing and Mathematical Analysis) at UIB, focusing on mathematical models for image restoration.