Jubran Akram is an Adjunct Assistant Professor in the Department of Earth, Energy, and Environment within the Faculty of Science at the University of Calgary. His research focuses on advanced geophysical signal processing techniques. Primary research areas include: Microseismic data processing and event detection Wavefield separation and noise suppression methods Machine learning applications in seismic analysis Velocity modeling and hypocenter localization His publications (2018-2024) demonstrate consistent focus on developing computational methods for seismic data enhancement, including machine learning approaches for phase picking, noise reduction, velocity calibration, and event location.
Weiran Sun is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. His research focuses on partial differential equations (PDEs), kinetic theory, and applied analysis, with recent interests expanding into AI applications in mathematics. He holds a Ph.D. in Applied Mathematics and Scientific Computation from the University of Maryland (2008). Research interests include: Analysis and numerical methods for kinetic equations Inverse problems related to kinetic theory Mathematical collaboration with computer scientists (e.g., DeLTA Lab at SFU for LLMs in applied math) Recent work highlights: Development of PDE-Controller using LLMs for formalization/reasoning of PDEs Studies on crossover transport phenomena and diffusion limits Contributions to numerical analysis of nonlocal equations (e.g., Lévy-Fokker-Planck) Upcoming activities include organizing the 5th SIAM Pacific Northwest Section Meeting (2025) and participating in kinetic theory programs at SLMath (Berkeley). His research spans interdisciplinary collaborations, blending traditional applied mathematics with emerging AI tools.
Francis J. Narcowich is a Professor of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from Princeton University (1972) and has been a faculty member since his appointment. His research focuses on approximation theory, mathematical physics, and numerical methods, with notable contributions to radial basis functions, wavelets, and spherical approximation. Education: Ph.D. in Mathematics, Princeton University, 1972 M.A. in Mathematics, Princeton University, 1970 B.S. in Mathematics, DePaul University, 1968 Teaching: Current courses (Spring 2025): Math 414 (Fourier Series and Wavelets), Math 601 (Methods of Applied Mathematics) Past courses include Analysis for Applications, Complex Variables, and Special Functions Research Interests: Dr. Narcowich's work bridges approximation theory and applied mathematics, emphasizing kernel-based methods, scattered data approximation, and numerical solutions of PDEs on spheres. His contributions include advancements in radial basis functions, localized bases, and spectral theory for operators. Publications: Over 100 peer-reviewed articles, including seminal works on spherical interpolation, meshless methods, and inverse theorems in approximation theory. Notable books include *A First Course in Wavelets and Fourier Analysis* (co-authored with Albert Boggess). Grants and Service: Extensive funding history in applied mathematics and computational science. Active in academic service, including curriculum development and graduate student mentorship.
Dr. Matt Sutton is a Lecturer in Statistical Inference for Complex Models at the School of Mathematical Sciences. He earned his PhD in 2019, focusing on developing statistical methods for high-dimensional data in clinical health and biological contexts. Previously, he worked as a postdoc at Lancaster University under the Bayes4Health grant. His research emphasizes Monte Carlo methods, Bayesian methodology, and high-dimensional statistics, with a current focus on continuous-time Monte Carlo techniques to accelerate Bayesian inference. Sutton actively contributes to the Models and Algorithms research program at the Centre for Data Science. His research interests include computational statistics, specifically advancements in PDMP samplers, control variates, and scalable Bayesian methods. Notable work spans applications in genomics, geophysics, and healthcare data analysis. Sutton’s methodologies aim to enhance computational efficiency in complex statistical inference tasks. His articles reflect a trend toward optimizing sampling algorithms and addressing challenges in high-dimensional Bayesian problems. Key areas include PDMP-based sampling, debiasing techniques, and federated learning applications. Sutton has not yet reported formal scientific awards or listed advisees in the provided text. His involvement in collaborative initiatives like the Centre for Data Science underscores his commitment to interdisciplinary research.
Alen Alexanderian is an Associate Professor in the Department of Mathematics at North Carolina State University (NC State), serving since 2022. Previously, he was an Assistant Professor at NC State (2015-2022), a Research Associate and Postdoctoral Fellow at The University of Texas at Austin’s ICES (2012-2015), and a Postdoctoral Fellow at Johns Hopkins University (2010-2012). He earned his PhD in Applied Mathematics from the University of Maryland, Baltimore County (2010), with prior degrees including an MS (2007) and BA in Mathematics (2005) from Hood College. His research focuses on numerical analysis, scientific computing, and uncertainty quantification. Key areas include numerical methods for inverse problems, optimization under uncertainty, Bayesian inversion, and sensitivity analysis. He develops algorithms for optimal experimental design and applies these to large-scale inverse problems in engineering and science. His recent work emphasizes hyper-differential sensitivity analysis, robust optimal design under model uncertainty, and applications in PDE-constrained optimization. His studies often bridge theoretical developments with practical implementations in fields like porous media flow, biotransport, and epidemiological models. Notable contributions include frameworks for Bayesian optimal sensor placement, variance-based sensitivity analysis techniques, and scalable computational methods for high-dimensional inverse problems. His research integrates mathematical rigor with computational efficiency, addressing challenges in data assimilation, model reduction, and risk-aware decision-making.
Dr. Alessio Alexiadis is a Reader in Chemical Engineering at the University of Birmingham, specializing in mathematical modeling and physics-informed machine learning. He holds a PhD from Politecnico di Torino (2001) and has held academic positions across 11 institutions in 8 countries. His research focuses on particle methods (MD/DEM/SPH), multiphase flows, and interdisciplinary applications in materials science, biomedical engineering, and environmental modeling. Education: PhD in Chemical Engineering, Politecnico di Torino, 2001 BSc in Chemical Engineering, 1998 Research Interests: Development of Discrete Multiphysics frameworks for complex systems AI-driven simulations and physics-informed machine learning Modeling fluid-structure interactions, colloids, and biophysical systems Applications in energy storage, climate modeling, and biomedical devices Grants & Funding: EPSRC CDT in Formulation Engineering (PI, £multi-million) Leverhulme Trust (PI, 2019–2023) US Office of Naval Research (PI, 2018–2022) Awards & Editorial Roles: Marie Curie Fellowship (2008) Editorial Board member: PLOS ONE, Scientific Reports (Nature), and ChemEngineering Labs & Collaborations: Modelling Concepts and Tools (MCT) group at Birmingham International collaborations spanning 4 continents
Lachlan Grose is a Research Fellow at Monash University's School of Earth Atmosphere and Environment, specializing in advanced geological and geophysical modeling. His work focuses on integrating machine learning, 3D structural analysis, and geophysical inversion techniques to address challenges in subsurface resource management and tectonic understanding. Key projects include the Three-dimensional Bayesian Modelling of Geological and Geophysical data (ARC-funded) and development of the LoopStructural software, a time-aware 3D geological modeling platform. Grose also contributes to industry collaborations like the AMIRA P1249 project on ore body knowledge modeling. Research interests span structural geology, igneous intrusion modeling, FAIR data principles, and geophysical inversion methods. He has led efforts to improve usability of Loop3D and pioneered machine learning applications for geological structure imaging. Leading developer of LoopStructural (open-source 3D modeling framework) Primary investigator on usability improvements for geoscientific workflows Recipient of ARC and industry grants totaling over AUD 3M Supervises PhD candidates focusing on interdisciplinary geoscience problems Recent publications emphasize transdimensional inversion methods, FAIR data integration, and application of machine learning to geophysical datasets. His work bridges academic research with industrial subsurface resource challenges.
Jayathi Y. Murthy is a Professor in the Department of Mechanical and Aerospace Engineering at the UCLA Henry Samueli School of Engineering and Applied Science. She served as the Ronald and Valerie Sugar Dean of the school from January 1, 2016, to July 31, 2022, becoming the first woman to hold this position. Her leadership emphasized transformative growth in areas such as engineering in medicine, artificial intelligence, sustainable urban systems, and advanced materials. Research Interests: Murthy's research spans nanoscale heat transfer , computational fluid dynamics (CFD) , and multiscale multi-physics simulations of micro- and nano-electromechanical systems (MEMS/NEMS). She also investigates uncertainty quantification in simulations, particularly in sub-micron thermal transport. Her work bridges fundamental thermal sciences with industrial applications, including energy systems and microelectronics. The 15 most recent articles reflect a strong focus on thermal modeling at micro- and nanoscales , advanced numerical methods in CFD , and uncertainty-aware simulations . Keywords include nanotechnology, heat transfer, computational engineering, and materials science. Subfields span phonon transport, adaptive mesh refinement, hybrid numerical schemes, and inverse methods, showing a consistent trajectory toward high-fidelity, predictive modeling in thermal systems. Scientific Awards and Honors: National Academy of Engineering (NAE) Member Foreign Fellow, Indian National Academy of Engineering (INAE) Fellow, American Society of Mechanical Engineers (ASME) ASME Heat Transfer Memorial Award (2016) ASME Electronics and Photonics Packaging Division Clock Award Advising and Grants: While no specific students are listed, Murthy has led major research centers, including the $21 million NNSA-supported PRISM Center at UT Austin. She has authored over 330 publications and edited the second edition of the Handbook of Numerical Heat Transfer . Her editorial roles include service on Numerical Heat Transfer and the International Journal of Thermal Sciences . Labs and Research Teams: Murthy led the Center for Prediction of Reliability, Integrity and Survivability of Microsystems (PRISM), a multidisciplinary research hub. Her work involves collaborations across mechanical engineering, materials science, and computational modeling, often integrating industrial and academic partners.
Rida Benhaddou is an Associate Professor in the Department of Mathematics at the College of Arts and Sciences, Ohio University. She is based in Morton Hall 563 and contributes to the academic and research missions of the department. Education: Ph.D., University of Central Florida Her research focuses on advanced statistical methodologies, particularly in nonparametric inference and inverse problems. She specializes in empirical Bayes methods, which bridge Bayesian and frequentist approaches in data analysis. These areas are critical in modern statistical theory and applications where distributional assumptions are weak or unknown. While no recent publications are listed in the provided text, her research interests suggest a strong theoretical foundation in mathematical statistics and statistical learning. Scientific Awards: No awards listed. She has not been mentioned as advising any students in the provided content. There is no mention of grants, research labs, or collaborative teams. Her work appears to be centered in theoretical statistics with potential applications in data science and inference under uncertainty.
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.
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 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 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 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 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.
