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Katharina ProkschView profile

Researcher

• Statistics
• Biomedical Imaging
• Data Analysis
• +4 more

Katharina Proksch is a researcher specializing in advanced statistical methodologies and their applications across diverse scientific domains. Her work focuses on regression models, simultaneous inference, and transforms, with notable contributions to nonparametric testing, inverse problems, and quantitative imaging techniques. She explores interdisciplinary applications in biomedical imaging, astrophysics, and biomechanical analysis, emphasizing rigorous statistical frameworks to ensure methodological robustness. Her research interests include developing statistical tools for super-resolution microscopy, fatigue detection through biomechanical data, and analyzing complex datasets from fluorescence microscopy and X-ray astronomy. Proksch's methodologies often address challenges in handling measurement errors, outliers, and multiscale data structures. Key contributions include a two-sample test using Wilcoxon rank sums, quantitative super-resolution microscopy techniques with statistical guarantees, and fatigue detection algorithms leveraging sequential testing. Her work bridges theoretical statistics with practical applications in health monitoring and astrophysical phenomena modeling.

Tim RestonView profile

Professor

• Geophysics
• Tectonics
• Geology
• +3 more

Tim Reston is a Professor of Geology and Head of the Geosystem Research Group at the University of Birmingham's School of Geography, Earth and Environmental Sciences. He serves as Head of School and Natural Sciences Tutor. His research focuses on applying geophysics to tectonic problems, particularly rifted continental margins and slow-spreading mid-ocean ridges, with expertise in seismic imaging, detachment tectonics, and mantle serpentinization. Reston holds a BA in Geology from the University of Cambridge (1983), an MSc in Geophysics from the University of Oxford (1984), and a PhD in Geophysics from the University of London (1988). Education: BA Geology, University of Cambridge (1983) MSc Geophysics, University of Oxford (1984) PhD Geophysics, University of London (1988) Research Interests: Reston’s work emphasizes the integration of geophysical methods to study tectonic processes. His research explores the seismic structure of continental margins, the mechanics of detachment faulting, and the role of mantle serpentinization in shaping crustal architecture. He has contributed to understanding the dynamics of mid-ocean ridges, particularly at ultraslow-spreading segments like the Mid-Atlantic Ridge, and the interplay between magmatism and serpentinization in crustal evolution. Publications: His recent work spans subduction zone dynamics, mid-ocean ridge tectonics, and continental margin evolution. Key themes include the use of high-resolution seismic imaging to map crustal structures and the application of geophysical models to unravel tectonic processes. Labs/Teams: Leads the Geosystem Research Group, which investigates Earth system processes through interdisciplinary geophysical and geological approaches.

Michael OristaglioView profile

Researcher

• Energy Geoscience
• Geophysical Remote Sensing
• Carbon Sequestration
• +3 more

Michael Oristaglio is a Senior Research Scientist/Scholar and Lecturer in Earth & Planetary Sciences at Yale University. He co-founded the Energy Studies Multidisciplinary Academic Program and served as Executive Director of the Yale Climate & Energy Institute (YCEI). His research focuses on geologic carbon sequestration, geophysical remote sensing, and seismic imaging. He leads SEAM (Society of Exploration Geophysicists Applied Multidisciplinary) projects addressing seismic exploration, reservoir management, and AI applications in geophysics. He has managed major initiatives like SEAM Land Seismic Challenges (2011–2016), SEAM Time Lapse (2016), and SEAM Life of Field (2016–2019), collaborating with industry and government agencies. Current projects include SEAM AI, exploring AI in geophysical exploration and environmental monitoring. Oristaglio teaches courses on energy technologies, fossil fuels, renewables, and capstone projects. His work bridges academic research with industrial applications in energy systems and sustainable resource management.

• Statistical Inverse Problems
• Regularization Theory
• Uncertainty Quantification
• +5 more

Frank Werner is a Professor of Mathematics at the Chair of Scientific Computing, University of Würzburg, specializing in statistical inverse problems and regularization theory. His research bridges statistics and inverse problems, with applications in biophysics, particularly fluorescence microscopy and non-Gaussian noise modeling. Education: Diplom in Mathematics (2009) and PhD (2012) from Georg-August-Universität Göttingen. Werner's research focuses on uncertainty quantification via minimax tests, nonlinear inverse problems, and photonic imaging. His recent work includes adaptive regularization methods and multiscale scanning techniques. He leads the Inverse Problems team and collaborates internationally, including at Fudan University, Technical University of Chemnitz, and the Max-Planck-Institut für biophysikalische Chemie (2014–2020). His publications span journals like Inverse Problems , Annals of Statistics , and SIAM Journal on Numerical Analysis , with trends emphasizing Poisson data, impulsive noise, and computational algorithms for microscopy. Scientific Awards: Diplomprüfung with Distinction (2009) Erskine Fellowship (2023) Werner is actively involved in academic service, organizing conferences (e.g., AIP2025 in Rio), and mentoring collaborations. He is married with two sons and maintains affiliations with societies like the EMS-TAG Inverse Problems and the Society for Inverse Problems.

• Data Assimilation
• Particle Filters
• Oceanography
• +3 more

Peter Jan Van Leeuwen is a Professor specializing in data assimilation methodologies with applications across geophysical sciences. His research develops advanced techniques for high-dimensional systems with particular emphasis on particle filtering approaches. His research innovations include: Development of implicit equal-weights particle filters for high-dimensional systems Nonlinear data assimilation frameworks using particle flow filters Ensemble methods for model error estimation Advanced Bayesian inference techniques for geophysical applications Novel approaches for causal discovery in complex systems Recent publications demonstrate wide applications from oceanography and atmospheric science to flood forecasting and astrophysics. His work consistently addresses fundamental challenges in high-dimensional uncertainty quantification and nonlinear system behavior. Research contributions include significant methodological advances in: Particle filter efficiency for ocean and atmospheric models Time-correlated model error estimation Riemannian data assimilation frameworks Causal inference in non-intervenable systems Preconditioning strategies for 4D-Var assimilation Dr. Van Leeuwen has collaborated extensively on projects including the SEASTAR satellite mission concept for ocean submesoscale dynamics and contributed to major data assimilation initiatives like MERCATOR and MERSEA.

Lu ZhangView profile

Assistant Professor

• Numerical Analysis of Partial Differential Equations (PDEs)
• Discontinuous Galerkin Methods
• Computational Mathematics
• +5 more

Lu Zhang is an Assistant Professor in the Department of Computational Applied Mathematics and Operations Research at Rice University. She joined Rice in 2023 after serving as the Ju Tang Chu and Wu Ping Chu Assistant Professor at Columbia University's Department of Applied Physics and Applied Mathematics. Zhang holds a Ph.D. in computational and applied mathematics from Southern Methodist University (2020) and has conducted summer research at Argonne National Laboratory (2018) and Lawrence Livermore National Laboratory (2019). Her research focuses on numerical and theoretical analysis of partial differential equations (PDEs), particularly developing high-order discontinuous Galerkin methods for PDEs with physical and biological applications. Key areas include advective wave equations, seismic imaging, chemotaxis models, population dynamics, and inverse problems. Her work emphasizes energy-stable, computationally efficient algorithms and their theoretical underpinnings. Zhang's recent publications (2021–2024) explore advanced numerical methods for wave equations, electromagnetic systems, and elastic media, with applications in geophysics, structural mechanics, and crime modeling. Her methods address challenges in high-order accuracy, stability, and computational efficiency across diverse scientific domains. No scientific awards or grants are explicitly mentioned in the provided text. She currently advises no listed students but has extensive experience in collaborative research through national laboratory partnerships.

Jakob ZechView profile

Professor

• Uncertainty Quantification
• High-Dimensional Approximation
• Sparse-Grid Techniques
• +4 more

Jakob Zech is a Professor at the Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University since April 2020. Before this, he held postdoctoral positions at MIT (2019-2020) and ETH Zürich (2018-2019). He earned his PhD in Mathematics from ETH Zürich in 2018, focusing on Sparse-Grid Approximation of High-Dimensional Parametric PDEs, followed by a Master’s (2014) and Bachelor’s (2012) in Applied Mathematics from ETH Zürich and TU Wien, respectively. Research Interests : Zech’s work bridges Uncertainty Quantification (UQ), high-dimensional approximation, and computational mathematics. Key areas include sparse-grid techniques, neural networks, transport methods, Bayesian inverse problems, and the theoretical foundations of deep learning. His research emphasizes developing algorithms for stochastic modeling and analyzing their mathematical properties. Teaching : He has taught advanced courses such as High Dimensional Approximation, Theory of Deep Learning, and Numerical Methods for Bayesian Inverse Problems at Heidelberg University. He also served as a teaching assistant for numerous courses at ETH Zürich, covering numerical analysis, partial differential equations, and linear algebra. Publications : His recent work explores quantum computing applications in polynomial chaos expansions, statistical learning theory for neural operators, and multilevel optimization strategies. His articles reflect a strong focus on combining classical numerical methods with modern machine learning techniques. Labs/Teams : While no specific lab is mentioned, his research group is active in computational UQ and deep learning, collaborating internationally with institutions like MIT and ETH Zürich.

• Geometric Analysis
• Riemannian Geometry
• Geometric Flows
• +5 more

Giuseppe Pipoli is an Associate Professor in Geometry at the Department of Engineering and Information Science and Mathematics, University of L'Aquila, Italy. His research focuses on Geometric Analysis and Riemannian Geometry, particularly geometric flows (e.g., mean curvature flow, inverse mean curvature flow), hypersurfaces with prescribed curvature, and the geometry of submanifolds in hyperbolic and symmetric spaces. He earned his Ph.D. in 2014 from Sapienza University of Rome under Carlo Sinestrari, with a thesis on the mean curvature flow of pinched submanifolds. His work explores curvature flows in non-Euclidean settings, including complex and quaternionic hyperbolic spaces, and their asymptotic behaviors. Key research trends include: analysis of translators in geometric flows, classification of isoparametric hypersurfaces in product spaces, and stability properties of minimal hypersurfaces. His studies often reveal how ambient space geometry influences flow dynamics and curvature behavior. His articles address advanced topics like inverse mean curvature flow in non-compact symmetric spaces, invariant translators in sub-Riemannian geometries (e.g., Heisenberg group), and cylindrical estimates in CROSS spaces. These contributions advance understanding of geometric evolution equations and their singularities. Pipoli has held postdoctoral positions at the University of L'Aquila and Institut Fourier, Grenoble. His research also intersects with Riemannian submersions and applications of curvature flow to geometric classification problems.

Mohammad HasanView profile

Senior Lecturer

• Computer Networks
• Cyber Security
• Cloud Computing
• +5 more

Dr. Mohammad Hasan is a Senior Lecturer in Digital, Tech, Innovation & Business at Staffordshire University with extensive expertise in cybersecurity, cloud computing, and network engineering. His educational background includes a PhD in Mobile Ad-hoc Networks, MSc in Computer and Network Engineering, and BSc in Computer Science. His primary research focuses on: Computer networks including wireless systems and IoT Cybersecurity frameworks and threat analysis Cloud computing infrastructure and virtualization Machine learning applications in data analytics Recent publications demonstrate strong trends in machine learning applications for pattern recognition (eSports analytics), network security (XSS prevention), and biomedical signal processing (brain source localization). Dr. Hasan maintains professional certifications including CCSK, CompTIA Security+, and Cisco CyberOps Associate, and holds Senior Fellow status with the Higher Education Academy.

Peter LelievreView profile

Associate Professor

• Geophysical Inversion
• Computational Geophysics
• Mathematical Modeling
• +4 more

Peter Lelievre is an Associate Professor in the Department of Mathematics and Computer Science at Mount Allison University. His research focuses on advancing geophysical inversion technology to improve subsurface imaging for applications in flood risk management, resource exploration, and environmental monitoring. He emphasizes collaborative, interdisciplinary approaches combining mathematics, computer science, and geophysics. His work addresses computational challenges in geophysical inversion, including unstructured mesh modeling, machine learning integration, and uncertainty quantification. He leads a research group prioritizing diversity and innovation, with current projects involving geophysical imaging of agricultural dykes and seafloor massive sulphide deposits. Recent publications span magnetic surface inversion, transient electromagnetic data analysis, and gravity modeling of crustal systems. Lelievre collaborates on initiatives like the AARMS-CRG Numerical Solution of Geophysical Inverse Problems, fostering discussion on optimization methods and joint inversion strategies. His team includes postdoctoral fellows, undergraduate researchers, and technical staff, with notable contributions to software development for geophysical analysis.

Vincent LostanlenView profile

Researcher

• Machine Learning
• Audio Content Analysis
• Computer Music
• +11 more

Vincent Lostanlen is a Researcher (chargé de recherche) at CNRS, affiliated with the LS2N laboratory located at Centrale Nantes engineering school. He also serves as a visiting scholar at New York University. His research focuses on developing artificial intelligence for audio analysis, emphasizing machine listening systems, bioacoustic monitoring, and computer music. Key projects include the BirdVox platform for avian migration monitoring and the Kymatio Python library for wavelet scattering transforms. Education: Ph.D. in Mathematics from École Normale Supérieure (2017), supervised by Stéphane Mallat; MSc in Acoustics & Music Informatics from Université Pierre-et-Marie-Curie (2013); Dipl. Ing. in Signal Processing from Télécom ParisTech (2013). Research interests span audio representation learning, environmental sensor networks, and generative models for sound. Notable contributions include the SONYC-UST urban sound dataset and the PCEN signal frontend for robust acoustic sensing. Scientific awards include the 2019 AFIM Young Researcher Award, 2016 SFA Best Poster Prize, and 2015 IEEE MLSP Best Paper Award. He advises graduate students across computer science and music technology domains, with former advisees working in academia and industry. Current activities include developing green AI for audio processing and collaborating on bioacoustic sensor networks for biodiversity monitoring. His work bridges signal processing theory with practical applications in music, ecology, and biomedical engineering.

Abdul KhaliqView profile

Professor

• Numerical Analysis
• Scientific Computing
• Financial Mathematics
• +10 more

Dr. Abdul Khaliq is a Professor in the Department of Mathematical Sciences at Middle Tennessee State University (MTSU) . With a career spanning decades, he specializes in developing Numerical Methods for complex systems including Partial Differential Equations (PDEs) , Fractional PDEs , and Stochastic Differential Equations . His work bridges applied mathematics and computational science, focusing on applications in Financial Mathematics , Biochemical Systems , and Public Health Modeling . Ph.D. in Mathematics, Brunel University London (1983) M.Sc. in Numerical Analysis, Brunel University London (1981) M.Sc. in Mathematics, Quaid-i-Azam University (1977) Research Interests include: Numerical Analysis of large-scale systems Scientific Computing with deep learning integration Computational Finance for high-dimensional derivatives Fractional Calculus applications in epidemiology High-Performance Computing for financial and physical systems Article Trends reflect his interdisciplinary expertise in fractional PDEs , stochastic modeling , and machine learning algorithms for financial and epidemiological forecasting. Recent works focus on quantum neural networks for time-dependent systems and physics-informed encoder-decoder architectures for parameter learning. Scientific Awards : MTSU Distinguished Research Award (2015-2016, 2007-2008) Excellence in Professional Service, College of Basic and Applied Sciences (2010-2011, 2006-2007) Faculty Excellence in Research, Western Illinois University (2001-2002) Teaching includes courses in Scientific Computing (COMS6500) and Mathematics of Investment (ACSI4200). He leads Ph.D. dissertation research (COMS7060) and Independent Study (MATH7060). Leadership roles include Editor-in-Chief of the International Journal of Computer Mathematics and Associate Editor positions at multiple journals. He has organized numerous SIAM Mini-Symposia on fractional PDEs and computational finance.

Anthony P. AustinView profile

Assistant Professor

• Numerical Linear Algebra
• High-Performance Computing
• Approximation Theory
• +3 more

Anthony P. Austin is an Assistant Professor in the Department of Applied Mathematics at the Naval Postgraduate School . His research focuses on numerical linear algebra, high-performance computing, approximation theory, and GPU-accelerated algorithms. He has held prior positions at Virginia Tech and Argonne National Laboratory as a J. H. Wilkinson Fellow. His work includes contributions to Chebfun, a software system for numerical computation. Education: D.Phil. in Mathematics , University of Oxford Mathematical Institute (2016) Ph.D. studies with active development in the Chebfun project Research Interests : Numerical methods for large-scale problems, high-order methods for PDEs, and computational techniques leveraging GPUs. His work emphasizes algorithmic innovation and stability in numerical simulations. Recent Contributions : Publications include advancements in spectral factorization, adversarial signal perturbations, and parallel algorithms for partial spectral computations. His seminar co-organization highlights collaboration in early-career faculty research initiatives. Professional Activities : Co-organizer of the NPS Junior Faculty Research Seminar (AY2022/Q3), focusing on fostering interdisciplinary collaboration among early-career faculty.

Zev WoodstockView profile

Assistant Professor

• Optimization
• Nonlinear Analysis
• Signal Processing
• +9 more

Zev Woodstock is an Assistant Professor in the Department of Mathematics & Statistics at James Madison University (JMU). His research focuses on nonsmooth optimization, splitting algorithms, machine learning, and monotone operator theory, with applications in data science, signal/image/audio processing, and large-scale optimization. He holds a PhD in Mathematics (2021) and MS in Applied Mathematics (2018) from North Carolina State University, and a BS in Mathematics (2016) from JMU. Previously, he was a postdoctoral researcher at the Interactive Optimization and Learning Lab at TU Berlin and Zuse Institute Berlin. Education PhD in Mathematics, North Carolina State University, 2021 MS in Applied Mathematics, North Carolina State University, 2018 BS in Mathematics, James Madison University, 2016 Research Interests Dr. Woodstock’s work spans optimization theory and applications, including convex/nonconvex optimization, fixed point algorithms, and signal recovery. He develops methods for solving nonlinear inverse problems using firmly nonexpansive operators, with contributions to variational inequality models, projection-based techniques, and block-iterative schemes. His applied work includes audio signal restoration, large-scale optimization, and machine learning infrastructure. Key Research Trends Recent publications emphasize efficient algorithms for linear minimization, asynchronous optimization frameworks, and robust signal recovery from inconsistent nonlinear data. His work bridges theoretical operator theory with practical applications in imaging, audio processing, and distributed computing. Awards and Grants Recipient of National Science Foundation Grants DGE-1746939 and CCF-1715671 for collaborative research in approximation theory and optimization. Labs and Collaborations Current affiliations include the Department of Mathematics & Statistics at JMU. Previously, he collaborated with the Interactive Optimization and Learning Lab at TU Berlin, focusing on convex optimization and algorithmic design.

Jens MunkView profile

Professor

• Electromagnetic modeling
• Inverse problems
• Material measurements
• +3 more

Jens Munk is a Professor and Department Chair in the Department of Electrical Engineering at the University of Alaska Anchorage. He holds a Ph.D. in Geophysics (Ohio State University, 1999), MS (1992), and BS (1990) in Electrical Engineering from the same institution. His research focuses on electromagnetic modeling, inverse problems, ionospheric studies, and remote sensing applications. Key contributions include studies on ionospheric plasma dynamics, evapotranspiration landfill covers, and Greenland ice sheet mapping via spaceborne radar. He has served as faculty advisor for the IEEE Student Branch since 2006 and contributed to multiple professional committees. Notable awards include the 1999 Outstanding PhD Dissertation and 1999 Presidential Fellowship. His teaching spans electromagnetics, circuit theory, antenna theory, and senior design projects. He has collaborated with institutions like the Technical University of Denmark and the Byrd Polar Research Center. Current research emphasizes environmental engineering applications in cold regions and space physics.