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 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.
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.
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 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 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 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.
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 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 .
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.
Dr. Joan Duran Grimalt is an Associate Professor of Applied Mathematics in the Department of Mathematics and Computer Science at the University of the Balearic Islands (UIB). He serves as a member of the Mathematical Image Processing (TAMI) research group and the Institute of Applied Computing and Community Code (IAC3). From July 2021 to June 2024, he held the position of deputy director of the Higher Polytechnic School and head of studies for the Degree in Mathematics program. His academic career at UIB began in 2015 following the completion of his PhD. Dr. Duran Grimalt earned his academic credentials at prestigious institutions: BSc in Mathematics (2010) from the University of the Balearic Islands (UIB) MSc in Advanced Mathematics and Mathematical Engineering (2011) from the Polytechnic University of Catalonia PhD in Mathematics (2016) from UIB with thesis on variational models for ill-posed inverse problems in digital imaging His research spans the intersection of mathematical theory and practical applications in imaging science. Dr. Duran Grimalt specializes in nonlinear analysis, calculus of variations, partial differential equations, and deep unfolding architectures. His work focuses on developing mathematical frameworks that bridge traditional variational methods with modern deep learning approaches, particularly for image processing and computer vision applications. This hybrid methodology allows for both the interpretability of model-based approaches and the performance benefits of data-driven techniques. Analysis of his recent publications reveals a clear research trajectory toward integrating classical mathematical models with deep learning architectures, particularly through the technique of deep unfolding. His work consistently addresses challenging problems in satellite image processing, pansharpening, hypersharpening, and low-light image enhancement. The publications demonstrate a progression from purely variational approaches to increasingly sophisticated hybrid models that incorporate attention mechanisms, nonlocal operations, and specialized network architectures designed specifically for imaging problems. His notable scientific recognition includes: Fellowship from the Govern de les Illes Balears for PhD research (2011-2015) Dr. Duran Grimalt has secured research funding for multiple projects, leading two major initiatives. He has established significant international collaborations with the National Centre for Space Studies (CNES) in France, where he contributed to the image restoration chain for Earth observation satellites, and with the Oceanographic Centre of the Balearic Islands, focusing on deep unfolding architectures for remote sensing data fusion and marine object detection. His academic mentorship includes supervising PhD candidates M. Francesc Alcover (working on nonlocal theory for variational problems) and Daniel Torres (researching the combination of variational models and deep learning for image processing). He has also been a visiting researcher at leading institutions including the Technical University of Munich, ENS Paris-Saclay, and New York University. His research is conducted through the Mathematics, Imaging and Learning (MIA) Consolidated R+D+I Group, where he is an active member, and leverages resources from the Institute of Applied Computing and Community Code (IAC3). These research structures provide the computational infrastructure and collaborative environment necessary for his work on advanced image processing algorithms and their applications in satellite imaging and computer vision.
Ralf Hielscher is a Professor at the Institute of Applied Analysis within the Faculty of Mathematics and Computer Science at the Technical University of Freiberg, Germany. His research lies at the intersection of applied mathematics, materials science, and imaging, with a strong focus on crystallographic texture analysis and electron backscatter diffraction (EBSD). He is a core developer and leading figure behind MTEX, a widely used open-source MATLAB toolbox for texture and orientation data analysis. Research Interests: His work centers on mathematical methods for analyzing crystallographic orientations, including spherical harmonic transforms, kernel density estimation on rotation groups, manifold-valued data processing, and inverse problems in tomography and texture reconstruction. He develops algorithms for parent grain reconstruction, orientation mapping, denoising, and visualization of microstructures. The recent publications reveal a consistent trend in advancing computational techniques for EBSD and texture analysis, particularly through the MTEX platform. His work bridges theoretical mathematics with practical materials characterization, enabling more accurate and efficient analysis of polycrystalline materials across geology, metallurgy, and engineering. Email: ralf.hielscher@math.tu-freiberg.de Scientific Contributions: While no formal awards are listed, his extensive publication record in high-impact journals such as SIAM Journal on Imaging Sciences , Journal of Applied Crystallography , and Inverse Problems underscores his significant contributions to the field. He has developed foundational algorithms now embedded in MTEX, which is used globally by researchers in materials science and geology. Teaching and Advising: He teaches courses such as Function Theory, Analysis 3, and Mathematics for Engineers. Although specific students are not mentioned, his leadership in MTEX and numerous collaborative publications suggest he mentors researchers and contributes to training the next generation of scientists in computational materials analysis. Labs and Teams: He is part of the team at the Institute of Applied Analysis and leads research efforts related to signal and image processing in crystallography. The MTEX project serves as a virtual research platform involving international collaborators in Germany, France, the UK, and beyond, facilitating open science in texture analysis.
Susanta Ghosh is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Michigan Technological University, where he is also a faculty member of the Center for Data Sciences at the Institute of Computing and Cybersystems. His research integrates Bayesian Machine Learning , Scientific Machine Learning , and Computational Mechanics to address uncertainty quantification in materials design, fracture modeling, and biomedical imaging. Education : Ph.D. and M.S. from Indian Institute of Science (IISc), Bangalore; B.S. from Indian Institute of Engineering Science and Technology, Shibpur. Research Expertise : Bayesian Neural Networks, Uncertainty Quantification, Computational Inverse Problems, Electronic Structure Prediction, and Nonlocal Elasticity. Funding : NSF CAREER Award (2025-2030, $669,490), DOE grants for chiral nanomaterials (2022-2025, $321,941; 2025-2028, $396,528), and NSF EAGER Award (2019-2022, $170,604). Teaching : Undergraduate and graduate courses in mechanics, finite element methods, and applied machine learning. Selected Awards : National Science Foundation (NSF) CAREER Award (2025)
Anjith George is a researcher at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, working within the Biometrics Security and Privacy Laboratory. His research focuses on advancing face recognition systems with particular emphasis on security, efficiency, and cross-domain applications. He maintains a strong collaborative relationship with Professor Sébastien Marcel's research group at EPFL. George's research interests span multiple critical areas in modern biometrics including face recognition systems, face anti-spoofing techniques, heterogeneous face recognition across different modalities (such as visible to infrared), and efficient model deployment for edge devices. His work addresses fundamental challenges in biometric security by developing robust systems that can withstand presentation attacks while maintaining high accuracy across diverse conditions. He has made significant contributions to the field of synthetic data generation and utilization for improving face recognition systems, exploring how knowledge can be effectively transferred from synthetic to real-world domains. Analysis of George's recent publications reveals a clear research trajectory focused on solving practical challenges in face recognition. His work has evolved from fundamental eye tracking and gaze direction research in the early 2010s toward increasingly sophisticated face recognition systems addressing security vulnerabilities, efficiency constraints, and domain adaptation problems. A significant portion of his recent work explores the potential of synthetic data to overcome limitations in real-world training data, while also investigating how to bridge the gap between different face recognition modalities. His research demonstrates a strong emphasis on practical applications, particularly in resource-constrained environments where edge deployment is necessary. George has been actively involved in major biometrics competitions and challenges, including the FRCSyn Challenge and EFaR (Efficient Face Recognition) competition, contributing to community benchmarking efforts and advancing state-of-the-art solutions. His collaborative work spans multiple institutions globally, reflecting the international nature of biometrics research.
Michael Möller is a Professor for Computer Vision at the University of Siegen, Germany. His research focuses on integrating model-based and learning-based techniques in imaging and vision, with an emphasis on efficient optimization algorithms for solving high-dimensional minimization problems. Research Interests: Combining classical model-based methods with deep learning Optimization algorithms for inverse problems Applications in CT reconstruction, image segmentation, and quantum computing Development of energy-dissipating neural networks and non-smooth regularization techniques Scientific Awards: Best Paper Award at ISWC 2021 Best Paper Honorable Mention at GCPR 2020 Best Paper Award at VMV 2015 Collaborations and Grants: Active collaborations with institutions like IEEE, Springer, and universities across Europe. Publications in top-tier venues such as NeurIPS, CVPR, ICCV, and ICLR. His work spans theoretical frameworks (e.g., convex relaxations, spectral methods) and practical applications (e.g., medical imaging, THz defect detection).
