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Haina WangView profile

Research Fellow

• Biophysics
• Statistical Mechanics
• Chemical Physics
• +3 more

Haina Wang is a Postdoctoral Researcher in the Department of Physics and Astronomy at the University of Pennsylvania's School of Arts & Sciences. She works with Professors Andrea Liu and John Crocker on modeling the actin cortex as a dynamic learning system, which she describes as the "civil engineering" of living cells. Her educational background includes: B.S. in Chemistry and Mathematics from National University of Singapore (2018) Ph.D. in Theoretical Chemistry from Princeton University (2024) Dr. Wang's research focuses on the intersection of physics, chemistry, and biology. Her primary areas of interest include Biophysics, Statistical Mechanics, and Chemical Physics, with particular emphasis on disordered active systems in biological contexts. During her doctoral studies at Princeton under Prof. Salvatore Torquato, she investigated extracting microscopic forces from correlation functions of fluids and the inverse design of disordered hyperuniform systems—exotic states of matter that exist between typical liquids and crystals. This work on order metrics in chemical physics sparked her current interest in applying similar principles to biological systems. Wang's publication record demonstrates a strong focus on hyperuniformity, pair statistics, and many-body systems across ten publications in high-impact journals. Her research trajectory shows progression from fundamental theoretical work on crystal structures and hard-sphere systems toward increasingly biological applications. Recent publications examine hole statistics in crystalline structures, designer pair statistics for disordered systems, and the dynamic measurement of hyperuniformity in heterogeneous media, culminating in her current work on cellular mechanics. As a researcher at the University of Pennsylvania, Dr. Wang is actively contributing to interdisciplinary collaborations that bridge physics, chemistry, and biology. Her current work on modeling the actin cortex as a dynamic learning system represents an innovative approach to understanding cellular mechanics through engineering principles, potentially opening new avenues for research in cellular biophysics and soft matter physics.

• Computerized Tomography
• Radon Transform
• Mathematical Imaging
• +4 more

Matthias Beckmann is an active researcher specializing in mathematical imaging, with a strong focus on the Radon Transform and its applications in computerized tomography and image processing. His work spans theoretical developments, algorithm design, and practical implementations in medical and computational imaging. He has established significant collaborations with researchers across multiple institutions, particularly with Ayush Bhandari, Felix Krahmer, and Robert Beinert. Beckmann's research primarily centers on the Modulo Radon Transform, a specialized area within mathematical imaging that addresses challenges in high-dynamic-range tomography and limited data scenarios. His work bridges theoretical mathematics with practical computational methods, developing novel inversion formulas, reconstruction algorithms, and classification techniques. He has made significant contributions to understanding the properties of the Modulo Radon Transform, developing Fourier-domain inversion methods, and creating normalized transforms that provide invariance and robustness in image classification tasks. Analysis of Beckmann's publication trends reveals a consistent progression from theoretical foundations to practical applications. His early work established the mathematical framework for the Modulo Radon Transform, while more recent publications demonstrate applications in machine learning, optimal transport theory, and specialized imaging scenarios like thermogram analysis of watermarks in manuscripts. His research shows increasing integration of neural network approaches with traditional mathematical imaging techniques, particularly in addressing indirect measurement problems. Beckmann has contributed to multiple prestigious journals including SIAM Journal on Imaging Sciences, SIAM Journal on Mathematics of Data Science, and IEEE Transactions on Computational Imaging, demonstrating the significance and recognition of his work within the mathematical imaging community. His publications span conference proceedings, journal articles, and technical reports, showing versatility in communicating research findings to different academic audiences. Through his collaborations and publication record, Beckmann appears to be actively involved in research teams focused on computational imaging and mathematical methods for image processing. His work on lecture notes suggests involvement in educational activities related to computerized tomography, though specific teaching roles or institutional affiliations are not explicitly documented in the available publication records.

Tomohiro IView profile

Associate Professor

• Algorithms on Strings
• String Pattern Matching
• Indexing
• +9 more

Tomohiro I is an Associate Professor in the Department of Artificial Intelligence at Kyushu Institute of Technology, Japan. He has been in this position since January 2019, following a research associate role at the same institution from 2015 to 2018. Prior to that, he held postdoctoral positions at Kyushu University and TU Dortmund, Germany. His academic foundation includes a Ph.D. in Science from Kyushu University, awarded in 2012. His research primarily centers on string algorithms , with a strong emphasis on compressed data structures , pattern matching , indexing , and algorithmic efficiency . Key interests include Lyndon factorization, Lempel-Ziv compression, palindrome matching, and reverse engineering of string data structures. He frequently collaborates with prominent researchers like Hideo Bannai and Shunsuke Inenaga, producing high-impact work in theoretical computer science. His recent publications demonstrate a consistent focus on improving algorithms for string processing in compressed formats. Work on Re-Pair , RLBWT , and SLP encoding highlights his expertise in space-efficient computation. The 2022 Best Paper Award at IWOCA for work on Lyndon subsequences underscores the quality and recognition of his contributions. His research bridges theoretical analysis with practical algorithm design. Best Paper Award, International Workshop on Combinatorial Algorithms (IWOCA) 2022 Tomohiro I advises graduate students in his laboratory, although he currently notes that the lab is not accepting new research students. His work involves significant algorithmic research, often supported by theoretical grants or institutional funding, leading to numerous publications in peer-reviewed journals and conferences. He has also presented his work in invited talks, such as at CompressedAI2025 and WCTA 2024, indicating active engagement with the research community. He leads a research laboratory at Kyushu Institute of Technology, focused on advanced string processing and compressed data structures. His team collaborates extensively on algorithm design and analysis, contributing to the broader field of combinatorial pattern matching.

Michael VogeliusView profile

Professor

• Partial Differential Equations
• Numerical Analysis
• Mathematical Analysis
• +4 more

Michael Vogelius serves as Board of Governors Professor in the Department of Mathematics at Rutgers University, New Brunswick. He joined the faculty in 1989 after seven years at the University of Maryland, College Park, and has held visiting appointments at Stanford University, École Polytechnique Fédérale de Lausanne, and the University of Copenhagen. His current teaching includes Math 550: Linear Algebra and Applications, and he maintains active research leadership in mathematical analysis. Education: PhD from University of Maryland under advisor Ivo Babuška Postdoctoral training at New York University with George Papanicolaou Research Interests: Professor Vogelius specializes in Partial Differential Equations and Numerical Analysis, with core contributions to blow-up phenomena in nonlinear boundary conditions, scattering effects of small inhomogeneities, metamaterials theory, and non-scattering wave characterization. His work bridges rigorous mathematical analysis with applications in electromagnetic imaging, cloaking technologies, and inverse problems. Recent projects emphasize finiteness of non-scattering wave numbers, crack reconstruction via energy methods, and asymptotic models for thin inhomogeneities in conductive media. Publication Trends: His 2020-2025 publications demonstrate sustained focus on inverse problems and wave scattering in complex media. Key patterns include developing rigorous frameworks for crack reconstruction in Calderón's problem, establishing finiteness results for non-scattering phenomena, and advancing cloaking techniques for broadband and anisotropic settings. These works consistently employ sophisticated asymptotic analysis and error estimation for thin or small-scale defects, reflecting his expertise in connecting theoretical PDE analysis with practical electromagnetic applications. Collaborations and Funding: Vogelius maintains extensive collaborations including Robert V. Kohn (10 publications, 1984-2010), Fadil Santosa (9 publications, 1990-2000), and recent partnerships with Henrik Garde and Matti Lassas (2024). His research is supported by the National Science Foundation through the Division of Mathematical Sciences (DMS), as evidenced by his participation in DMS updates and conference organization. The Mathematics Department website references student advisement and postdoc mentorship, though specific trainee names aren't detailed in the provided text.

Vladimir GoncharoffView profile

Lecturer

• Signal Processing
• Radio Communication
• Speech Analysis
• +2 more

Vladimir Goncharoff is a Lecturer in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago (UIC). His educational background includes a Ph.D. and M.Sc. in Electrical Engineering from Northwestern University (1983, 1980), a B.Sc. in Electrical Engineering from UIC (1979), and an Associate of Science from William Rainey Harper College (1976). His research focuses on: Analog/Digital Signal Processing : Speech enhancement, spectral analysis, pitch detection, and watermark recovery. Radio Frequency Circuits : Design principles, educational methodologies, and practical applications. Acoustic Systems : Biomedical acoustics and voice communication technologies. Goncharoff's publications emphasize speech algorithms, RF circuit education, and signal reconstruction. Recent works transition toward pedagogical materials, including textbooks on radio frequency circuits (2015–2016) and signal processing (2018). Awards & Honors: UIC Silver Circle Award for Excellence in Teaching (1988, 2000, 2003, 2006, 2009) UIC College of Engineering Harold Simon Award (1985) No information is available regarding grants, student advising, or laboratory affiliations.

Francesco Di RenzoView profile

Associate Professor

• Theoretical Physics
• Quantum Field Theory
• Lattice Gauge Theory
• +6 more

Francesco Di Renzo is Associate Professor of Theoretical Physics at the University of Parma's Department of Mathematical, Physical and Computer Sciences. Appointed faculty in 2001 and promoted to associate professor in 2016, he holds concurrent research appointments with INFN and coordinates the €4M EuroPLEx H2020 network. His academic background includes: Laurea (Master's equivalent) in Physics with honors, University of Parma PhD in Physics, University of Parma Di Renzo's research centers on lattice formulations of quantum field theories with emphasis on fundamental interactions. He pioneers computational approaches to critical phenomena using Lee-Yang zero analysis and thimble regularization to address sign problems in QCD. His work integrates high-performance computing architectures with emerging AI techniques for non-perturbative physics simulations. Analysis of his 2022-2025 publications reveals three dominant trends: (1) Application of multipoint Padé approximants to locate QCD critical points through Lee-Yang edge singularities, (2) Development of Numerical Stochastic Perturbation Theory (NSPT) for O(N) sigma models at large N, and (3) Cross-disciplinary fusion of machine learning with lattice field theory for phase transition analysis. He currently supervises three PhD students and has served on the Doctoral Studies Committee since 2000. His grant leadership includes EuroPLEx (H2020 scientific coordinator), STRONGnet (FP7 Parma unit lead), and multiple PRIN projects where he coordinated research units. As an INFN-appointed researcher, he directs the QCDLAT initiative. Di Renzo actively contributes to international infrastructure through the International Lattice Data Grid (where he serves as Italian representative and former chairman) and the INFN SUMA supercomputing project. He previously led the AuroScience initiative and currently shapes UNIPR's high-performance computing strategy as committee member.

Ville KolehmainenView profile

Professor

• Computational Inverse Problems
• Uncertainty Quantification
• Medical Imaging
• +6 more

Ville Kolehmainen is a Professor at the Department of Technical Physics within the Faculty of Science, Forestry and Technology at the University of Eastern Finland (UEF). His research focuses extensively on computational inverse problems and uncertainty quantification , with applications in advanced medical imaging techniques such as X-ray tomography , diffuse tomography , and MRI . He actively collaborates with clinical experts and engineering specialists to develop novel imaging modalities for 3D/4D imaging with sparse data Electrical impedance tomography (EIT) Multi-energy CT reconstruction Thermal tomography Ville Kolehmainen leads the Inverse Problems research group (active since 2010) and contributes to the Neuro-Innovation project (2021-2026). His work integrates Bayesian statistical inversion , parallel level sets , and nonlinear reconstruction methods to address challenges in Medical tomography Optical diffusion tomography Thermal conductivity estimation Atmospheric remote sensing Recent publications highlight his contributions to electrical impedance tomography for hemorrhagic stroke monitoring, dual-modality imaging of two-phase flows, and deep learning post-process corrections in remote sensing. His methodological innovations include model error compensation , sparsity-promoting inversion , and anatomical prior integration in image reconstruction. Collaborations span University of Eastern Finland , University College London , and Harvard Medical School researchers.

Axel KrönerView profile

Professor

• Optimal Control
• Optimization with partial differential equations
• Numerical analysis
• +3 more

Prof. Dr. Axel Kröner is a Professor of Optimization at the Institute of Mathematics, Martin-Luther-Universität Halle-Wittenberg, within the Faculty of Natural Sciences II. His research focuses on optimal control and numerical methods for partial differential equations (PDEs), stochastic control, and feedback mechanisms in complex systems. Academic Background: Ph.D. in Mathematics, Technische Universität München (2011) Diploma in Mathematics, Ruprecht-Karls-Universität Heidelberg (2007) Axel Kröner's research spans PDE-constrained optimization, numerical analysis, and control theory. Key areas include feedback control for hyperbolic and parabolic equations, stochastic optimal control for infinite horizon problems, and adaptive finite element methods for PDE discretization. His work bridges theoretical and computational aspects, with applications in elasticity, fluid dynamics, and quantum mechanics. Recent publications highlight advancements in Hamilton-Jacobi-Bellman equations for feedback control, bilevel optimization for parameter learning, and Runge-Kutta-based neural networks . Methodologies often integrate dynamic programming , Galerkin approximations , and error estimation for efficient solutions. Advising and Projects: He has supervised numerous Master's and Bachelor's theses on topics like wave equation control, inverse problems, and neural network optimization. Funding includes support from the German Academic Exchange Service (DAAD) and the Gaspard Monge Program for Optimization and Operations Research (2015-2018). Professional Activities: Kröner has organized major conferences such as the International Workshop on PDE Constrained Optimization and contributed to teaching at institutions including Humboldt-Universität zu Berlin and University Paris-Saclay.

• Machine Learning
• Large-Scale Data Analysis
• Bioinformatics
• +3 more

Inderjit Dhillon , Professor at the University of Texas at Austin , is a leading researcher in machine learning and big data analytics. As Director of the Center for Big Data Analytics, his work focuses on scalable algorithms for high-dimensional data, social network analysis, and gene-disease association prediction. ACM, IEEE, SIAM, and AAAS Fellow Gottesman Family Centennial Professor (2014) ICES Distinguished Research Award (2013) Research Interests: His contributions span machine learning , bioinformatics , and scientific computing , with recent work emphasizing deep learning for time series, adversarial robustness, and distributed optimization. Publications in top venues like NeurIPS and ICML highlight his expertise in inductive matrix completion and non-convex optimization . Scientific Awards: 2016: AAAS Fellow 2014: ACM, SIAM, IEEE Fellow 2013: ICES Distinguished Research Award 2011: SIAM Outstanding Paper Prize His students and collaborators frequently publish in areas like extreme classification , kernel methods , and multi-scale spectral decomposition . Labs and teams under his leadership have advanced nomadic computing and asynchronous optimization for massive datasets.

Fotis KopsaftopoulosView profile

Assistant Professor

• Structural Health Monitoring
• Aerospace Engineering
• Machine Learning
• +7 more

Fotis Kopsaftopoulos is an Assistant Professor at the Department of Mechanical, Aerospace, and Nuclear Engineering (MANE) at Rensselaer Polytechnic Institute (RPI) , where he has been since 2017. His research focuses on intelligent aerospace and mechanical systems , particularly structural health monitoring (SHM) , stochastic modeling , and machine learning applications for damage detection and fault diagnosis. Education: Ph.D. in Mechanical Engineering & Aeronautics (2012), University of Patras Diploma in Mechanical Engineering & Aeronautics (2004), University of Patras Research Interests: Kopsaftopoulos develops data-driven SHM frameworks using stochastic time series models , Gaussian process regression , and guided wave propagation for aerospace structures. His work addresses damage detection under varying loads , rotor fault diagnosis in multicopters, and health monitoring of batteries and adhesively bonded joints . Scientific Awards: 2021 Class of 1951 Outstanding Teaching Award at RPI 2015 Most Practical SHM Solution for Aerospace Award (sponsored by Airbus) 1998–1999 Academic Scholarship of Excellence from the State Scholarship Foundation of Greece Teaching & Leadership: He has taught courses such as System Identification , Aerospace Structures and Materials , and Engineering Dynamics at RPI. He served as co-Editor for Structural Health Monitoring proceedings, Associate Editor for the Structural Health Monitoring Journal , and Topic Editor for Sensors Journal . He has advised 6 PhD, 14 MEng/MS, and 46 BS projects.

Fehmi CirakView profile

Professor

• Computational Mechanics
• Finite Element Analysis
• Structural Engineering
• +5 more

Professor Fehmi Cirak is a faculty member at the University of Cambridge, Department of Engineering, specializing in Computational Mechanics. He joined Cambridge in 2006 after five years as a Senior Scientist at the California Institute of Technology (Caltech). His research integrates advanced computational methods with structural and materials analysis, emphasizing innovation in finite element techniques and data-informed modeling.

• Cognitive Modeling
• Machine Learning
• Control Theory
• +3 more

Dr. Clemens Bombach is a postdoctoral researcher at the Professorship of Predictive Analytics within the School of Human Sciences and Technology (HSW) at Chemnitz University of Technology (TU Chemnitz). His work bridges cognitive modeling, machine learning, and mathematical control theory to predict individual human behavior and analyze complex systems. Education: PhD in Mathematics (Dr. rer. nat.) from TU Chemnitz, 2024 Studies in Mathematics at TU Chemnitz, 2009–2016 Research Interests: Dr. Bombach focuses on cognitive modeling and machine learning for behavioral prediction, alongside mathematical control theory and inverse problems. His current work emphasizes observability estimates for parabolic and non-autonomous systems, leveraging L p -spaces and functional analysis. Publication Trends: His publications primarily address control theory, inverse problems, and differential equations, with recent contributions to observability theorems, singular potentials, and applications of harmonic analysis. Collaborations with colleagues like M. Tautenhahn and F. Gabel highlight his interdisciplinary approach. Teaching: He contributes to exercises in mathematics, including courses on partial differential equations, complex analysis, and higher mathematics for engineering, computer science, and physics students. Projects: Currently leads the DfG-Project "PREDIR" (2025–2027), developing systems, algorithms, and cognitive models to predict human inference processes.

• Computer Vision
• 3D Shape Reconstruction
• Biomedical Imaging
• +4 more

Dr. Hector Andrade Loarca is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology and Department of Informatics 9 (Computer Vision Group), working under Prof. Daniel Cremers on 3D deep learning and shape reconstruction. His research spans multiple disciplines at the intersection of computer vision and applied mathematics: 3D Shape Reconstruction Biomedical Imaging Inverse Problems Machine Learning Applications Inverse Rendering Techniques Applied Harmonic Analysis Dr. Andrade Loarca maintains active industry engagement as Head of AI at AngioWave Imaging (leading biomedical imaging AI as a founding team member) and Head of AI at Nazca VC (developing AI tools for venture capital analysis in Latin America). His academic background includes a PhD from TU Berlin in the Applied Functional Analysis Group, with postdoctoral work at LMU Munich's Mathematical Foundations of AI Group under Prof. Gitta Kutyniok, focusing on inverse problems in medical imaging and graphics. He has developed notable software projects including Shearlab.jl (a Julia library for multidimensional signal processing) and tfShearlab (TensorFlow interface for Shearlab-style transforms), and has presented his work at conferences including JuliaCon and PyData.

• Applied Electromagnetics
• Antennas and Propagation
• Microwave Imaging
• +3 more

David Orlando Rodriguez Duarte is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin (Politecnico di Torino), Italy. He is a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab and actively contributes to the applied electromagnetics research group. His academic work bridges electromagnetic theory with practical medical applications, particularly in developing innovative imaging technologies for healthcare. Dr. Rodriguez Duarte is completing his Ph.D. degree with the Department of Electronics and Telecommunications at Politecnico di Torino while holding a prestigious Marie Sklodowska-Curie Fellowship. He is deeply involved in the EMERALD project, which focuses on modeling and designing microwave imaging systems for cerebrovascular diseases. The department's research group has over 10 years of experience advancing both theoretical and applied electromagnetism, with significant contributions in electromagnetic theory, wave propagation, and applications in communication systems and radar technology. His research spans multiple interconnected domains in applied electromagnetics. He specializes in antenna design specifically tailored for medical applications, with particular expertise in microwave imaging systems for neurological conditions. His work in electromagnetic field theory focuses on biomedical contexts, especially stroke monitoring and detection. He develops practical implementations that bridge theoretical concepts with clinical applications, addressing critical healthcare challenges. His research directly supports SDG Goal 3: Good health and well-being through the development of accessible diagnostic technologies. Dr. Rodriguez Duarte's scholarly output demonstrates a clear progression toward developing practical, portable microwave imaging solutions using off-the-shelf components. His recent work increasingly integrates machine learning techniques with traditional microwave sensing approaches, enhancing diagnostic accuracy for neurological conditions. There's a strong emphasis on creating real-time imaging solutions that can be deployed at the bedside for immediate patient care, particularly for time-sensitive conditions like stroke. Marie Sklodowska-Curie Fellowship for research on microwave imaging systems for cerebrovascular diseases Dr. Rodriguez Duarte serves as primary supervisor for two doctoral candidates: Alex Ramiro Masaquiza Caiza (40th cycle, 2024-present) and Martina Gugliermino (39th cycle, 2023-ongoing). Their research focuses on 'EM field study in medicine: characterization of materials for phantoms and stroke monitoring algorithms.' He has secured research funding through the EMERALD European project (2018-2021) and continues to develop grant-supported work in biomedical microwave imaging. His ERC sector alignments include PE7_6 (Communication technology), PE7_11 (Medical applications), and PE7_3 (Simulation engineering). He is an integral member of the PolitoBIOMed Lab - Biomedical Engineering Lab, where his team develops microwave imaging technologies for brain stroke monitoring. The lab focuses on creating practical implementations of electromagnetic theory for medical diagnostics, with emphasis on point-of-care applications. Their work addresses a critical healthcare need as stroke affects approximately one in six people worldwide and requires prompt diagnosis for effective treatment, making their research vital for improving patient outcomes through early detection technologies.

Boyan KostadinovView profile

Associate Professor

• Computational Thinking
• Data Science Education
• Interactive Visualizations
• +3 more

Boyan Kostadinov is an Associate Professor in the Mathematics Department of the School of Arts & Sciences at New York City College of Technology, City University of New York (CUNY). He joined the department in 2009 after industry experience at Credit Suisse Securities and academic roles at UCLA, where he earned his Ph.D. in Mathematics in 2005. His educational background includes: Ph.D. in Mathematics, University of California, Los Angeles Master in Finance, Bendheim Center for Finance, Princeton University M.A. in Mathematics, University of California, Los Angeles M.S. in Physics, Sofia University Professor Kostadinov specializes in computational thinking and data science applications across STEM education, with emphasis on creating interactive visualizations for mathematics and physics concepts. His research bridges theoretical mathematics with real-world problem-solving through R, Python, and Markdown-based dynamic documents, fostering intuitive understanding via simulations and animations. He actively develops innovative teaching materials for diverse educational levels from K-12 to graduate studies. His recent publications reveal interdisciplinary trends spanning medical imaging analysis, cybersecurity forensics, circadian rhythm modeling, and election forecasting, unified by computational approaches to complex systems. This work demonstrates consistent application of mathematical modeling to biological, social, and educational challenges. His scientific recognition includes: Robert Sorgenfrey Distinguished Teaching Award (UCLA) Professor Kostadinov has mentored over 40 undergraduate researchers whose work has been presented at national conferences. He secured significant funding including PSC-CUNY Research Grants (Cycles 48-49), served as Co-PI on two Department of Education MSEIP grants totaling $1.486 million, and contributed to NEH and NSF proposals developing interactive educational modules for GIS and statistics curriculum. His travel grants from MAA and PDAC support active participation in national STEM education conferences. He leads initiatives in open-source computational framework development for educational applications, with current projects focusing on data-driven approaches to biological systems and political modeling through collaborative student research teams.