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Ralf HielscherView profile

Professor

• Mathematical Imaging
• Crystallography
• Texture Analysis
• +7 more

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.

Melina FreitagView profile

Professor

• Numerical Linear Algebra
• Data Assimilation
• Inverse Problems
• +6 more

Melina Freitag is Professor for Data Assimilation at the Institute for Mathematics, University of Potsdam. Her research spans numerical linear algebra, inverse problems, and model order reduction, with applications in geophysics, image processing, and machine learning. She leads the Data Assimilation Group and contributes to the SFB 1294 Collaborative Research Center. Education : Prof. Freitag earned her Diplom in Mathematics at TU Chemnitz (2004) and PhD in Mathematical Sciences from University of Bath (2007). Research Themes : Her work focuses on Krylov subspace methods, low-rank approximations, and preconditioning for large-scale systems. She bridges classical numerical analysis with modern data assimilation, addressing challenges in: Bayesian inverse problems with unstable systems Optimized spectral sampling in X-ray imaging Physics-informed neural networks for Navier-Stokes inversion Parameter-dependent eigenvalue analysis Collaborations & Grants : She collaborates with institutions like KTH Stockholm and Arizona State University. Her group secures funding through SFB 1294 and participates in INI Cambridge networks. Leadership : Co-Chair of GAMM Activity Group on Applied Numerical Linear Algebra SIAM Activity Group on Linear Algebra Chair (2022) Teaching : Delivers courses on numerical optimization, matrix methods in data science, and inverse problems, integrating computational theory with real-world applications.

Tamir BendoryView profile

Associate Professor

• Mathematics of data science
• signal processing
• optimization
• +4 more

Prof. Tamir Bendory serves as an Associate Professor in the Department of Electrical Engineering within Tel Aviv University's School of Electrical and Computer Engineering. His research bridges mathematical theory with practical signal processing applications, primarily focused on computational imaging challenges. His core research areas include Mathematics of data science, signal processing, optimization, statistics, cryo-electron microscopy, phase retrieval, and super-resolution. This interdisciplinary work develops theoretical frameworks for reconstructing biological structures from noisy imaging data, with particular emphasis on algorithmic solutions for cryo-EM and phase retrieval problems. Analysis of his 2017-2023 publications reveals consistent progression in mathematical cryo-EM methodologies, evolving from fundamental uniqueness theorems toward sparsity-constrained algorithms with improved sample complexity. His work increasingly integrates algebraic geometry with statistical signal processing to address computational bottlenecks in structural biology. Prof. Bendory actively recruits research students and acknowledges funding support from major international sources including ISF, NSF, BSF, and TAD. His laboratory focuses on developing mathematical tools for next-generation imaging technologies in structural biology.

Haim AvronView profile

Professor

• Numerical Computing
• High Performance Computing
• Machine Learning
• +3 more

Haim Avron is a Professor in the Department of Applied Mathematics at Tel Aviv University's School of Mathematical Sciences, where he has been employed since 2015. His research focuses on numerical computing, high-performance computing, and their applications in scientific computing and machine learning. Education: Completed PhD in Computer Science at Tel Aviv University under Prof. Sivan Toledo, followed by postdoctoral research at IBM T.J. Watson Research Center. Research interests: Foundations of numerical linear algebra and randomized algorithms Tensor-tensor algebra for multiway data representation High-performance computational methods for machine learning Optimization techniques for large-scale systems Recent publications demonstrate strong focus on tensor algebra, randomized numerical methods, and machine learning optimization, with applications ranging from quantum computing to deep learning architectures. Awards: SIAM Activity Group on Computational Science and Engineering Best Paper Prize 2025 Software contributions include development of numerical libraries such as libSkylark for matrix sketching and Blendenpik for least-squares problems.

• Gravitational Wave Astronomy
• General Relativity
• Black Hole Physics
• +2 more

Prof. Dr. Badri Krishnan is a long-term visitor at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Hannover, Germany, affiliated with the Observational Relativity and Cosmology department. His research centers on gravitational wave astronomy and theoretical general relativity, with a focus on compact binary systems and black hole dynamics. His primary research interests include black hole spectroscopy, ringdown physics, quasi-local horizon dynamics, and gravitational wave data analysis. He has pioneered work in black hole tomography and the detection of sub-dominant quasi-normal modes, contributing significantly to LIGO/Virgo data interpretation and tests of general relativity through gravitational wave observations. Analysis of his 2023-2025 publications reveals intense focus on binary black hole merger ringdown phases, with recurring themes of horizon dynamics, tidal effects in neutron star systems, and mathematical frameworks for gravitational waveforms. His work bridges numerical relativity, observational data, and theoretical predictions to probe black hole properties. Scientific Awards: No specific awards or fellowships were documented in the provided materials. Advising and Grants: The source text contains no information regarding student supervision, doctoral advisees, or research funding sources. Labs and Teams: Krishnan actively participates in Einstein@Home (distributed computing for gravitational wave/pulsar searches), Pulsar Timing Arrays, and the Atlas computing cluster at AEI, leveraging these infrastructures for large-scale gravitational wave data analysis.

Felix AbramovichView profile

Professor

• Statistical Learning
• High-dimensional Inference
• Sparsity
• +5 more

Felix Abramovich is a Professor in the Department of Statistics and Operations Research at Tel Aviv University. His research focuses on theoretical foundations of statistical and machine learning, specializing in high-dimensional inference, sparsity, model selection, and nonparametric estimation techniques. Professor Abramovich's work spans statistical learning theory, classification methods, wavelet-based estimation, and inverse problems. His research develops minimax-optimal procedures for sparse high-dimensional data and explores the interplay between Bayesian methods and frequentist optimality. Key themes include adaptive estimation under sparsity constraints, theoretical analysis of classification algorithms, and wavelet applications in nonparametric statistics. Analysis of Abramovich's recent publications reveals a strong emphasis on classification methodologies, high-dimensional regression, and deep learning theory. His work consistently develops minimax-optimal procedures, examines sparsity patterns in complex data structures, and bridges Bayesian and frequentist frameworks. The research demonstrates increasing focus on modern machine learning challenges including neural network theory and multiclass classification. Professor Abramovich actively recruits students and post-doctoral researchers with strong mathematical statistics backgrounds to work on fundamental problems in statistical learning theory and high-dimensional inference.

• Computational Statistical Mechanics
• Soft Matter Physics
• Computer Simulation Methods
• +3 more

Burkhard Dünweg is a theoretical physicist and group leader at the Max Planck Institute for Polymer Research in Mainz, Germany. He also holds an Associate Professor position in the Department of Chemical Engineering at Monash University, Australia, where he has maintained a long-standing research collaboration since 2010. Dünweg serves as Director of the Statistical Mechanics and Soft Matter (SMSM) CECAM Node and acts as the institute's ombudsperson since 2013. Dünweg received his diploma in theoretical physics in 1987, his doctorate in 1991 (with Kurt Binder at Mainz University), and completed his habilitation in 2000 at the University of Mainz, where he was appointed associate professor in 2008. His academic journey includes a postdoctoral period from 1991 to 1993 at the Center for Simulational Physics in Athens, Georgia, USA (funded by the Humboldt Foundation), and visiting professorships at Saarbrücken University (2005) and Darmstadt University (2014-15). Professor Dünweg's research focuses on computational statistical mechanics and soft matter physics. He has made pioneering contributions to simulation methods, particularly in developing the lattice Boltzmann method for studying hydrodynamic interactions in soft matter systems. His key innovations include inventing a dissipative coupling scheme for particle systems interacting with lattice Boltzmann backgrounds, advancing understanding of thermal fluctuations in lattice Boltzmann simulations, and constructing field-based algorithms for the nonlinear Poisson-Boltzmann equation. His work spans polymer physics, colloidal systems, and electrokinetics, with particular emphasis on semi-dilute polymer solutions and charged colloidal dispersions driven by fields or flows. Dünweg's recent publications reveal a consistent trajectory toward increasingly sophisticated computational approaches for soft matter systems. His work demonstrates strong integration of theoretical development with practical applications, addressing challenges in multiscale modeling, hydrodynamic interactions, and phase transitions. The research shows particular strength in bridging molecular-scale phenomena with macroscopic material properties, with growing interest in biological applications like chromatin structure. From 2004 to 2013, Dünweg served as Associate Editor for Physical Review E (Polymer Physics, Computational Physics), contributing significantly to the scholarly communication in his field. His research has been supported through extensive international collaborations, most notably with J. Ravi Prakash's group at Monash University, with whom he has co-authored numerous publications spanning polymer dynamics and electrokinetics. Dünweg leads a research group within the Biomolecular Mechanics Group at the Max Planck Institute for Polymer Research. The group specializes in developing and applying advanced simulation techniques to understand complex soft matter behavior. They maintain strong international connections, particularly with Australian institutions, and contribute to advancing computational methodologies that bridge molecular dynamics with continuum hydrodynamics.

Axel MunkView profile

Professor

• Mathematical Statistics
• Biosciences
• Biophysics
• +10 more

Prof. Axel Munk is a University Professor at the Institute for Mathematical Stochastics , Georg-August-University of Goettingen, and serves as the director of the Felix Bernstein Institute for Mathematical Statistics in the Life Sciences (FBMS) within the Faculty of Mathematics and Computer Science. His work bridges mathematical statistics with biosciences and biophysics, focusing on advanced statistical methodologies for complex data analysis. Research Interests: Shape analysis, stochastic models in biology, ion channel dynamics, optimal transport theory, and super-resolution microscopy. Awards: Institute of Mathematical Statistics (IMS) Fellow (2016). Collaborations: Involved in DFG Collaborative Research Centers (e.g., SFB 755, SFB 860) and Priority Programs (e.g., SPP 1590). Software Contributions: Developed packages like stepR , J-SMURF , and icmcstat for ion channel analysis and multiscale methods.

Felix KastnerView profile

Researcher

• Stochastic Processes
• Mathematical Statistics
• Numerical Simulation
• +3 more

Dr. Felix Kastner is a Researcher in the Department of Mathematics at the University of Hamburg, affiliated with the ST – Mathematical Statistics and Stochastic Processes group within the Faculty of Mathematics, Informatics and Natural Sciences. His research focuses on stochastic processes, numerical simulation methods, approximation algorithms, and their applications in fields like stochastic differential equations and image reconstruction. His work bridges theoretical developments with computational implementations, evidenced by contributions to Julia/Matlab simulation toolboxes and numerical schemes for SPDEs. He holds an ORCID ID (0000-0003-0504-6211) and maintains a Google Scholar profile (krCP4ykAAAAJ). No formal awards or grants are explicitly listed in the provided materials, but his research demonstrates sustained engagement with cutting-edge problems in stochastic analysis and applied mathematics.

Kostas KokkotasView profile

Professor

• Gravitational Wave Astronomy
• Neutron Stars
• Black Holes
• +4 more

Kostas Kokkotas is a Professor of Theoretical Astrophysics at the University of Tübingen, Germany, leading the Theoretical Astrophysics (TAT) group. He holds an Honorary Professorship at Aristotle University of Thessaloniki. His academic journey includes degrees from the University of Thessaloniki (BSc, PhD) and Cardiff University (MSc). Primary affiliation: Eberhard Karls University of Tübingen (since 2007) Adjunct Professor at Georgia Tech (2010) Former position at University of Thessaloniki (1990–2007) Research focuses on gravitational waves, neutron stars, black holes, and relativistic astrophysics. Notable contributions include studies on quasinormal modes, compact object dynamics, and asteroseismology. Editor of Handbook of Gravitational Wave Astronomy (Springer, 2022). Teaching includes courses on General Relativity, Numerical Methods, and Relativistic Astrophysics. Active in international collaborations like the Einstein Telescope and Laser Interferometer Space Antenna (LISA) projects. Over 200 refereed publications, with recent work on premerger neutron star phenomena and gravitational wave echoes.

• Optimal Control
• Partial Differential Equations
• Numerical Analysis
• +3 more

Prof. Michael Hintermüller is a Professor at Humboldt University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics, specializing in Applied Mathematics. He also holds an affiliation with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin. His research focuses on optimal control of partial differential equations (PDEs), variational inequalities, numerical analysis, and their applications in imaging and energy systems. Notable contributions include work on machine learning-informed PDEs, stochastic optimization, and mathematical modeling of coupled systems like hydrogen-electric markets. His recent publications emphasize data-driven methods in imaging, regularization techniques, and optimization algorithms for complex systems. He explores interdisciplinary topics such as quantum systems simulation, gas network control, and energy market modeling. His work often bridges theoretical analysis and computational implementation, with applications in engineering and medicine. Prof. Hintermüller’s research aligns with initiatives like MaRDI, aiming to build infrastructure for mathematical data sciences. His academic profile reflects a strong commitment to advancing applied mathematics through rigorous analysis and innovative numerical methods.

• Bayesian Data Analysis
• Artificial Intelligence
• Machine Learning
• +5 more

Dr. Udo von Toussaint is a Senior Lecturer at Graz University of Technology (TU Graz) and leads the working group 'Bayesian Data Analysis and AL/AI Methods' (DAAL) at the Max Planck Institute for Plasma Physics (IPP) in Garching. His work bridges theoretical physics and modern data science, focusing on the application of Bayesian inference and artificial intelligence to complex physical systems, particularly in plasma and nonequilibrium processes. Institution: Graz University of Technology (TU Graz) Research Affiliation: Max Planck Institute for Plasma Physics (IPP), Garching Working Group: Bayesian Data Analysis and AL/AI Methods (DAAL) His research interests are centered on developing advanced AI-driven Bayesian methods for solving inverse physical problems, especially in the context of fusion and plasma diagnostics. These methods integrate probabilistic modeling with machine learning to extract meaningful insights from noisy or incomplete experimental data. The absence of publication data in the provided text prevents a trend analysis of recent articles. However, based on his working group's focus, his scholarly output likely spans interdisciplinary topics combining physics, statistics, and artificial intelligence. Scientific Focus: Bayesian inference, AI/ML in physical sciences, inverse problems Research Impact: Development of robust data analysis frameworks for plasma physics and fusion energy research While no formal advisees are listed in the provided material, Dr. von Toussaint is actively involved in teaching, offering a course on 'Inverse Physical Problems and Bayes Probability Theory' at TU Graz. This indicates an ongoing role in mentoring and educating graduate students in advanced statistical and computational methods. There is no mention of external grants, but his leadership of a dedicated working group at IPP suggests involvement in funded research initiatives. He leads the 'Bayesian Data Analysis and AL/AI Methods' (DAAL) working group at IPP, which is dedicated to innovating data analysis techniques for plasma and nonequilibrium systems. This group serves as a nexus for interdisciplinary research, combining expertise in physics, computer science, and applied mathematics to tackle challenges in experimental data interpretation.

Tatiana A. BubbaView profile

Assistant Professor

• Computational Inverse Problems
• Medical Imaging
• Spent Nuclear Fuel Imaging
• +2 more

Tatiana A. Bubba is an Assistant Professor in Numerical Analysis at the Department of Mathematics and Computer Science, University of Ferrara (Italy), and a Docent in Inverse Problems at the Department of Mathematics and Statistics, University of Helsinki (Finland). Her research bridges computational inverse problems with practical applications in medical imaging and nuclear fuel diagnostics.

Jan-Frederik PietschmannView profile

Professor

• Nonlinear Partial Differential Equations
• Gradient Flows
• Parameter Identification in PDEs
• +3 more

Jan-Frederik Pietschmann is a Professor for Inverse Problems at the University of Augsburg's Faculty of Mathematics, Natural Sciences and Technology. He previously held the same position at TU Chemnitz (2018–2023) and served as a Research Associate at institutions including the University of Münster and TU Darmstadt. University of Cambridge (PhD, Mathematics, 2008–2011) University of Münster (Diploma, Mathematics and Physics, 2008) Habilitation in Mathematics, University of Münster, 2018 His research focuses on the analysis of nonlinear PDEs , gradient flows , and parameter identification in PDEs . Recent work explores applications to cell invasion , pedestrian dynamics , neurite growth , and organic semiconductors . Publications highlight his expertise in optimal transport , cross-diffusion systems , and uncertainty quantification . Notable trends in his 15 most recent articles include Nonlinear PDEs for biological and physical systems Optimal transport on networks and metric graphs Computational methods for inverse problems His current team includes Gianna Götzmann, Carmen Tretmans, and Team Assistant Juliane Krautz. Contact details: jan-f.pietschmann@uni-a.de , +49 821 598-3926.

Oleh MelnykView profile

Visiting Professor

• Mathematical Imaging
• Phase Retrieval
• Numerical Analysis
• +4 more

Oleh Melnyk is a Substitute Professor at the Bavarian AI Chair for Mathematical Foundation of Artificial Intelligence at Ludwig-Maximilians-Universität München (LMU Munich), currently on leave from TU Berlin. His academic journey includes a Ph.D. in Mathematics (2023) from Technical University of Munich/Helmholtz Center Munich, an M.Sc. in Mathematics in Data Science (2018) from TU Munich, and a B.Sc. in Statistics (2016) from Taras Shevchenko National University of Kyiv. His research focuses on Mathematical Imaging , Phase Retrieval , Numerical Analysis , Optimization , and Compressed Sensing , with applications spanning ptychography, optical flow, sparse regression, and inverse problems. Recent work emphasizes algorithm convergence, noise-robust recovery, and high-dimensional data decomposition. Melnyk's publications demonstrate a strong focus on inverse problems and computational mathematics , with recurring themes in ptychographic imaging, phase retrieval algorithms, and sparse modeling. His articles frequently address theoretical convergence guarantees and practical applications in medical imaging and electron microscopy. He collaborates with research groups at LMU Munich, TU Berlin, and Helmholtz Munich, focusing on mathematical foundations of imaging and AI. No students, awards, or grants are mentioned in the source materials.