Wuchen Li is an Assistant Professor in Mathematics at the University of South Carolina , specializing in Transport information geometry and its applications across Complex Dynamical systems, PDEs, Statistics, Optimization, Control and Games, Mathematical Data science, Graphs and Neural networks , and Scientific Computations . His work bridges theoretical mathematics with practical algorithms for machine learning, Bayesian inference, and optimal transport problems. His research explores geometric frameworks for probability spaces, including Wasserstein-2 metrics , Onsager gradient flows , and primal-dual hybrid gradient algorithms . Recent publications focus on accelerated sampling methods, mean field control systems, and novel applications of optimal transport in high-dimensional settings. 2022 : Air Force Office of Scientific Research YIP award for Transport Information Geometric Computations Key article trends include stochastic differential equations (37%), Wasserstein gradient flows (42%), Markov chain Monte Carlo (28%), and Hamilton-Jacobi-Bellman equations (33%). Subfields span accelerated optimization , nonlinear mobility metrics , generative modeling , and reaction-diffusion systems .
Prof. Alexandros Kalousis holds the position of Ordinarius HES Professor at the Geneva School of Economics and Management (HES-SO). His primary affiliation is within the Department of Management Information Systems under the School of Economics and Services. His research focuses on machine learning, data mining, and their applications in biomedical systems, cybersecurity, and generative modeling. Key projects include SimGait (SNSF-funded), which develops neuromechanical models for pathological gait analysis using machine learning, and RAWFIE (EU-funded), creating a mixed network testbed for autonomous vehicle experimentation. He has also led projects on time series forecasting, olfactory modeling for perfume creation, and metric/kernel learning optimization. Notable contributions span graph generative models like DGAE and GLAD, cybersecurity implications of LLMs, and reproducible indoor positioning systems. His work integrates theory-driven models with deep learning, emphasizing interpretability and extrapolation capabilities. Research collaborations include EPFL's Biorobotics Lab, Geneva University Hospitals, and industry partners like Firmenich. Total funding exceeds CHF 3M across projects like SimGait (CHF 2.1M) and RAWFIE (CHF 623K).
İsa Yıldırım is an Associate Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University (ITU) , Faculty of Electrical and Electronics Engineering. He earned his PhD from the University of Illinois at Chicago in 2009, following MSc and BSc degrees from ITU in 2004 and 2002, respectively. He has been a full-time academic at ITU since 2012, advancing from Assistant to Associate Professor in 2015. Education: PhD, University of Illinois at Chicago, 2009 MSc, Istanbul Technical University, 2004 BSc, Istanbul Technical University, 2002 His research centers on biomedical imaging , signal and image processing , and deep learning , with a focus on medical image reconstruction techniques. His work applies advanced computational methods to improve imaging in digital breast tomosynthesis and low-dose CT. He has led multiple research projects funded by TUBITAK and BAP, focusing on non-convex optimization, total variation regularization, and compressed sensing. His recent publications (2022–2024) demonstrate a strong trend in integrating deep learning with model-based reconstruction , particularly in self-supervised and unsupervised frameworks for low-dose CT. His work also extends into robotics , specifically air-ground robot localization, indicating interdisciplinary collaboration. Scientific Awards: PhD Scholarship, Presidency of the Board of Higher Education, 2004 He actively mentors graduate students, having supervised 29 theses. He serves as Principal Investigator on ongoing projects, including Patient-Adapted Digital Breast Tomosynthesis Design (TUBITAK, 2023–2026). His research combines theoretical innovation with clinical applicability, particularly in reducing radiation exposure while enhancing diagnostic image quality. Labs and Research Teams: While specific lab names are not mentioned, his projects suggest leadership in a research group focused on Medical Image Reconstruction and Signal Processing , likely involving graduate students and collaborators in biomedical engineering and computer science.
Irene Fonseca is the Kavčić-Moura University Professor of Mathematics and Director of the Center for Nonlinear Analysis at Carnegie Mellon University’s Mellon College of Science. Her research focuses on applied mathematics at the interface of physical sciences and engineering, emphasizing variational techniques for materials science (e.g., shape memory alloys, thin films, epitaxy) and computer vision (image segmentation, denoising). She holds leadership roles including past SIAM President (2012) and has been honored with prestigious awards like the European Academy of Sciences Fellowship and knighthood in Portugal’s Military Order of St. James (1997). Education: Ph.D., University of Minnesota, Minneapolis Research Interests: Calculus of variations and nonlinear partial differential equations Mathematical modeling of materials microstructures Anisotropic surface energies and thin film growth Image processing via variational methods Epitaxial thin film growth mechanics Articles Trends: Recent work emphasizes Homogenization theory and multiscale modeling Nonlinear elasticity in thin structures Advanced image denoising algorithms Dislocation dynamics and defect formation Γ-convergence applications in phase transitions Awards: SIAM Fellow American Mathematical Society Fellow 2014 University Professor appointment 2018 Kavčić-Moura Professorship Grants & Labs: Leads the Center for Nonlinear Analysis, managing NSF-funded programs. Active in training next-generation applied mathematicians through interdisciplinary initiatives. Labs/Teams: Core contributor to CMU’s CNA, collaborating on projects bridging mathematics with materials science and imaging challenges.
Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.
Michel Ménard is a Teacher-Researcher at the University of La Rochelle, affiliated with the Mathematics and Computer Science departments. His research focuses on image and signal processing, particularly in cardiovascular imaging, dynamic texture analysis, and UWB radar applications for through-wall imaging. Key projects: ANR DIAMS, FISC consortium, A.Gaugue project Applications: Cardiovascular imaging, environmental monitoring, mobile application programming Research Interests Ménard's work centers on modeling information ambiguity, imprecision, and uncertainty in image analysis, pattern recognition, and information fusion. He has developed generalized fuzzy coalescence methods, non-parametric Bayesian approaches for trajectory analysis, and variational formulations for image filtering inspired by quantum physics. His team focuses on: Dynamic texture modeling via spatio-temporal decomposition Low-level image processing with information theory Through-wall imaging systems using UWB radar Information fusion techniques with minimal a priori assumptions Applications in coastal environment monitoring and biomedical imaging Publications Ménard's publications reflect his expertise in advanced image processing techniques applied to diverse domains. Notable contributions include: Theoretical works on total variation and sublinear functionals Algorithm developments for multistatic radar systems Applications in 3D bee tracking and cardiovascular flow analysis Extensions of Chambolle's algorithm to color images Decomposition methods for dynamic textures Integration of quantum physics concepts in image filtering Collaborations He collaborates with: Laboratoires: L3i, MIA, CLDG/BQR, IRPHE CNRS, ETIS, LASIE Institutions: University Hospitals of Poitiers and Angers, ONERA, LEAT, Tronico Researchers: Abdallah El-Hamidi, Alain Gaugue, Damien Coisne, Gilles Aubert Teaching Ménard teaches across eight departments/programs including: Electronics and Industrial Computing Automation Network Security and Cryptography Video Game Programming Smartphone Programming Digital Media Distribution He has developed new educational initiatives in mobile application programming since 2010.
Alexander Van Slyke serves as an Assistant Professor in the Department of Therapeutic Radiology at Yale School of Medicine, Yale University. His primary appointment focuses on medical physics research within the radiation oncology division, contributing to both clinical and preclinical radiation therapy advancements. His educational background includes: PhD in Biophysics from Cornell University (2019) BA in Physics and Mathematics from SUNY Geneseo (2012) Certificate in Medical Physics from University of Pennsylvania (2020) Medical Physics Residency at University of Maryland (2023) He holds board certification from the American Board of Radiology in Therapeutic Medical Physics (2024). Dr. Van Slyke's research centers on cutting-edge radiation therapy technologies and radiobiological mechanisms. His primary expertise spans FLASH radiotherapy (particularly proton FLASH), real-time electromagnetic surface tracking for motion management, oxygen dynamics monitoring during irradiation, and small animal radiation research. He employs advanced techniques like phosphorescence quenching for ultrafast oxygen sensing and develops optimization algorithms for treatment delivery. His work bridges medical physics innovation with radiobiological investigation to enhance radiation therapy precision. Analysis of his publications reveals a strong trajectory toward understanding ultra-high dose rate radiation effects, with recent work focusing on geometric validation of tracking systems (2025) and oxygen depletion dynamics during proton FLASH delivery. His research consistently integrates preclinical models with clinical translation, emphasizing motion management solutions and biological response characterization. Dr. Van Slyke actively contributes to departmental leadership as Acting Co-Chair of the C-RAD Optimization Committee and Chair of the Lung SBRT Planning Committee. His conference presentations at AAPM meetings demonstrate expertise in electromagnetic tracking feasibility, motion effect simulations for LATTICE therapy, and oxygen monitoring techniques. He operates within Yale's radiation oncology ecosystem, collaborating with medical physics and radiobiology teams to advance therapeutic radiology through technological innovation and radiobiological insight.
Michael S. Zhdanov is a Distinguished Professor in the Department of Geology and Geophysics at the University of Utah, where he has served since 1993. As Director of the Consortium for Electromagnetic Modeling and Inversion (CEMI) since 1995, he leads industry-sponsored research in non-seismic geophysics. He holds a Ph.D. from Moscow State University and previously held prominent roles at the Moscow Academy of Oil and Gas and the Russian Academy of Sciences. Research Focus Dr. Zhdanov pioneered regularized focusing inversion for geological imaging and developed the Gramian-based joint inversion framework for multiphysics data integration. His work bridges geophysics, machine learning (e.g., diffusion models, ResU-Net++), and computational methods to solve inverse problems in mineral exploration, subsurface imaging, and electromagnetic modeling. Key domains include salt dome reconstruction, basin analysis, and airborne EM/IP inversion. Publication Trends Recent work (2023–2025) demonstrates a strong emphasis on AI-driven geophysical inversion , with neural networks (ResU-Net++, EfficientNetV2) applied to gravity/magnetic data for salt dome detection, basement relief mapping, and mineral targeting. Over 50% of publications integrate machine learning with traditional inversion theory. Awards and Honors IEEE Senior Member (2024) SEG Honorary Membership (2013) Gauss Professorship, Gottingen Academy (1990) University of Utah Distinguished Scholarly Award (2009) Full Member, Russian Academy of Natural Sciences (1991) Leadership and Funding As CEMI Director, Zhdanov collaborates with 30+ industry partners (e.g., Shell, ExxonMobil, BHP). Secured grants from NSF, DOE, and industry for projects on 3D EM inversion, mineral exploration, and airborne survey technologies. Actively advises graduate students in inversion theory and EM methods. Facilities Leads the CEMI Consortium, developing advanced computational tools for multiphysics data fusion used globally in resource exploration.
Sylvain Sardy is an Associate Professor in the Department of Mathematics at the University of Geneva, where he conducts research at the intersection of statistics, optimization, and machine learning. He is affiliated with the Analysis, Mathematical Physics and Probability research group and has held significant editorial positions including Associate Editor for Computational Statistics and Data Analysis since 2020. Professor Sardy's research focuses on statistical machine learning, sparsity, and optimization with applications spanning astronomy, chemometrics, finance, and tomography. His work develops innovative methods for high-dimensional data analysis, particularly using wavelet-based approaches and LASSO regularization techniques for feature selection, denoising, and model selection. His publications reveal a consistent focus on finding sparse signals in complex datasets across diverse scientific domains. Professor Sardy has mentored numerous graduate students, currently supervising PhD candidate Maxime van Cutsem and having previously guided Dr. Xiaoyu Ma, Dr. Pascaline Descloux, Prof. Jairo Diaz Rodriguez, and Dr. Caroline Giacobino. His Master's students include Jairo Diaz (now Professor at Universidad del Norte, Colombia), Jean-Luc Baeriswyl, and others who have pursued careers in academia, industry, and education. His academic service includes leadership roles as Swiss representative at the European Regional Committee of the Bernoulli Society (2014-2018), President of the Doctoral School of Applied Statistics and Probability (2010-2013), and Student Advisor for the Mathematics Section (2008-2015). His teaching portfolio includes Optimization with Applications I, Statistical Machine Learning, and Pharmaceutical Statistics and Methodology, reflecting his expertise in statistical methodology and its practical implementation.
Robert Jerrard is a Professor and Chair in the Department of Mathematics at the University of Toronto's Faculty of Arts and Science, where he has been a faculty member since 2002. He completed his undergraduate studies at Deep Springs College and Cornell University (A.B. Physics, 1986), and earned his PhD in Mathematics from the University of California, Berkeley in 1994. His research focuses on nonlinear partial differential equations , calculus of variations , and mathematical physics , with particular emphasis on vortex dynamics, Ginzburg-Landau theory, and geometric analysis. He investigates problems at the intersection of rigorous mathematics and physical phenomena, including superfluidity, superconductivity, and wave propagation dynamics. Analysis of his recent publications reveals consistent themes in geometric analysis and nonlinear PDEs , with significant contributions to vortex dynamics in quantum fluids, Sobolev geometries on diffeomorphism groups, and interface motion in wave equations. His work combines theoretical analysis with applications to condensed matter physics. Scientific Awards & Honors: Fellow of the Royal Society of Canada Fellow of the American Mathematical Society He has held key administrative roles including Associate Chair for Undergraduate Studies (2007-2009) and Associate Chair for Research (2014-2016). He currently supervises graduate students and teaches courses ranging from multivariable calculus to advanced topics in geometric measure theory and fluid dynamics.
Konstantina Trivisa is a Professor of Mathematics at the University of Maryland, holding joint appointments at the Department of Mathematics, the Institute for Physical Science and Technology (IPST), and the Center for Scientific Computation and Mathematical Modeling (CSCAM). She currently serves as Director of IPST and Founding Faculty Director of the Masters of Professional Studies in Quantum Computing. Previously, she was Director of the Applied Mathematics & Statistics, and Scientific Computation Program (AMSC) from 2007-2018. She earned her Ph.D. in Applied Mathematics from Brown University in 1996, with a dissertation titled "A priori estimates on the total variation of solutions to hyperbolic systems of two conservation laws via generalized characteristics." Her undergraduate degree is a B.S. in Mathematics with high distinction from the University of Patras, Greece (1990). Trivisa's research lies at the interface between nonlinear partial differential equations and continuum physics, with applications in fluid dynamics, multiphase flows, continuum mechanics, materials science, and mathematical biology. Her work spans theoretical analysis, computational methods, and applications to real-world problems including tumor growth modeling, quantum computing algorithms, and fluid-particle interactions. She has developed innovative mathematical approaches to problems in superfluidity, polymer dynamics, and ferrofluids. Her recent publications demonstrate a strong trajectory toward interdisciplinary applications, particularly in quantum computing and mathematical biology. The 15 most recent articles show increasing focus on computational methods for complex fluid systems, quantum algorithms for differential equations, and mathematical models of tumor growth. Her work bridges pure mathematical analysis with practical applications across multiple scientific domains. Alfred P. Sloan Research Fellowship Presidential Early Career Award for Scientists and Engineers (PECASE) Simons Foundation Fellowship 2023 AWM Fellow 2023 SIAM Fellow 2018 Outstanding Director of Graduate Studies Award Trivisa has advised numerous Ph.D. students and postdoctoral associates, many of whom have secured prestigious academic positions. Her research has been supported by multiple NSF grants including DMS-2008568 (2020-2023), DMS-1614964 (2016-2021), and earlier PECASE funding. She has also received funding from Northrup Grumman and The World Bank for interdisciplinary research partnerships. As Director of IPST, she leads an internationally recognized center for interdisciplinary research at the boundaries between physical, mathematical and life sciences, and engineering.
Arne Kovac serves as Associate Professor in Statistics within the School of Mathematics at the University of Bristol, where he completed his PhD in 1999 under B. Silverman with the thesis "Wavelet Thresholding for Unequally Time-Spaced Data". His academic profile centers on methodological innovations in nonparametric statistics. His research expertise is defined by eight core areas: Taut String algorithms (100% fingerprint prominence) Confidence Region construction (94% prominence) Regularization techniques Total Variation minimization (68% prominence) Extreme Value theory applications Statistical minimization problems Nonparametric regression frameworks Asymptotic analysis Publication trends from 2009-2014 reveal consistent advancement of smoothing methodologies, particularly through taut string extensions and graph-based regression. These works establish foundational contributions to statistical inference under shape constraints, with notable emphasis on edge preservation in signal processing and confidence band construction. No scientific awards or major honors are documented in the available records. Professionally, Kovac served on the editorial board of Annals of Statistics (2007-2009) and participates in specialized workshops including "Nonparametric statistical inference under shape constraints" (active since 2016). While student supervision and grant funding details remain unspecified, his 20 research outputs—including 14 journal articles with significant Scopus citations (60 for 2009 taut string paper)—demonstrate sustained scholarly impact.
Colin Klaus is a Postdoctoral Research Fellow at The Mathematical Biosciences Institute (MBI) at The Ohio State University, where he applies advanced mathematical techniques to biological problems, particularly in protein dynamics and visual transduction. He received his PhD in Mathematics from Vanderbilt University in 2017 under the supervision of Emmanuele DiBenedetto. His doctoral research focused on interior regularity of parabolic partial differential equations of p-Laplacian type and parabolic variational formulations of the total variation flow. Klaus's research program bridges theoretical mathematics with practical biological applications. His primary interests include: Parabolic partial differential equations and their applications Multi-scale modeling techniques including homogenization and concentrated capacity Finite element methods for biological systems Protein diffusion modeling in cell membranes Visual and hearing transduction mechanisms Bayesian inference and Markov Chain Monte Carlo for uncertainty quantification His recent publications demonstrate a strong interdisciplinary approach, combining rigorous mathematical theory with biological insight. Klaus has developed novel modeling frameworks that significantly reduce computational complexity while maintaining biological fidelity, such as his concentrated capacity model for protein-solvent interactions which transforms a 3D problem into an essentially 1D computational task. His scientific contributions have been recognized with several awards: Invited Feature in SIAM DSWeb (2021) Bjarni Jonsson Prize for Excellence in Research (Vanderbilt Mathematics 2016-2017) BF Bryant Award for Excellence in Teaching (Vanderbilt Mathematics 2015-2016) Klaus actively collaborates with experimental biologists across multiple institutions, including the Vanderbilt Pharmacology Department, Boston University School of Medicine, and the Sotomayor Research Lab at OSU. His work integrates mathematical theory with experimental data through statistical frameworks for global sensitivity analysis and parameter optimization. He is a key contributor to the Sotomayor Research Lab's efforts in hearing transduction, where he develops innovative computational models that bridge molecular dynamics simulations with continuum approaches, enabling new insights into the biophysics of hearing.
Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.
