Ivan Bratko is a Full Professor at the Faculty of Computer and Information Science, University of Ljubljana, and has held leadership roles including Head of AI Lab (1986–present) and former Head of AI Department at Jožef Stefan Institute (1996–2004). His career spans over four decades, with research contributions in machine learning, qualitative modeling, and artificial intelligence applications. Developed the KARDIO expert system for cardiac arrhythmia diagnosis using deep qualitative knowledge Innovated ABML (Argument-Based Machine Learning) for faster, interpretable learning with expert annotations Created Q 2 learning methods that integrate qualitative constraints with quantitative prediction Contributed to minimax search analysis and decision tree pruning algorithms His work has earned prestigious awards, including the Zois award (2007) and Fellow of ECCAI (2000). He continues part-time research at Jožef Stefan Institute (since 1975) and authored influential textbooks like Prolog Programming for Artificial Intelligence .
Noël Hallemans is a Postdoctoral Research Assistant at the Control Group of the School of Engineering Science, University of Oxford . He earned his MEng (2019) from Vrije Universiteit Brussel and Université Libre de Bruxelles , followed by a PhD (2023) from Vrije Universiteit Brussel and University of Warwick , where he developed frequency domain data-driven modeling for electrochemical impedance spectroscopy in lithium-ion battery analysis. Research Focus: Electrochemical impedance spectroscopy for nonlinear/time-varying systems Battery modeling and real-time process control Nonlinear dynamics in electrochemical systems System identification and fractional order modeling Article Trends: His work spans Li-ion battery diagnostics , electroplating , and industrial process control , with a focus on operando techniques and nonlinear system identification across materials science , electrochemistry , and control theory . Scientific Awards: 2019: Best Master Thesis Prize (Vrije Universiteit Brussel and Université Libre de Bruxelles) Grants & Funding: His PhD research was supported by the Research Foundation Flanders (FWO-Vlaanderen, grant G.0052.18N) and the Flemish Government Methusalem Fund (METH1) . Current Projects: At Oxford, he investigates real-time battery impedance characterization and process control for battery manufacturing , collaborating with the Battery Intelligence Lab and researchers like Professor Stephen Duncan and Professor David Howey .
Ekaterina A Rapinchuk is an Assistant Professor in the Departments of Mathematics and Computational Mathematics, Science and Engineering (CMSE) at Michigan State University. Her research focuses on developing graph-based algorithms and spectral methods for machine learning applications, particularly in small data classification, molecular science, and hyperspectral imaging. She holds a joint appointment across two departments, reflecting her interdisciplinary work at the intersection of mathematics and computational science. Research Interests : Graph algorithms for data classification and clustering Spectral graph theory and Laplacian-based methods Machine learning for molecular and biomedical data Optimization in high-dimensional spaces Applications of topological data analysis Publications Trends : Recent work emphasizes addressing data scarcity through graph-based techniques, integrating transformer networks with spectral methods, and correcting labeling errors in training datasets. Her research bridges theoretical mathematics with practical applications in sensor data analysis, chemical informatics, and biomedical imaging. Grants & Advising : While specific grant details are not listed, her work aligns with NSF-funded research areas in computational mathematics and data science. No advising information is currently available.
Sebastian Schlecht is a Visiting Professor in the Department of Information and Communications Engineering at Aalto University, associated with the Virtual Acoustics research group. He holds a Doctoral degree in Engineering and Technology from Friedrich-Alexander-Universität Erlangen-Nürnberg (2017). His research focuses on acoustics, signal processing, and audio engineering, with a strong emphasis on reverberation, impulse response analysis, and feedback systems. Key research contributions include advancements in room impulse response completion, feedback delay networks, and audio processing libraries. His work has led to multiple awards, including the Best Paper Award at WASPAA 2019 and recognition at DAFx conferences. Schlecht leads the CExAM ARTS-ELEC project (2023–2027), exploring multi-objective optimization in digital signal processing. He has supervised five theses and contributed to datasets like the 'Sauna Impulse Responses' and 'Variable Acoustics Room Arni' collections. His research aligns with UN Sustainable Development Goals related to innovation and infrastructure.
Prasanth B. Nair is a Researcher at the University of Southampton affiliated with the Computational Engineering and Design Group. His work focuses on developing advanced computational methodologies for engineering and physical systems involving stochastic processes and uncertainty. His research spans Computational Engineering, Numerical Methods, Stochastic Partial Differential Equations, and Engineering Simulation. Key contributions include novel numerical schemes for solving stochastic differential equations and partial differential equations on random domains, with applications in physics, chemistry, and aerospace engineering. His methodologies emphasize robustness in modeling systems with inherent randomness. Nair's 2011 publications reveal a consistent focus on bridging theoretical mathematics with practical engineering challenges, particularly in uncertainty quantification for physical systems. His work demonstrates interdisciplinary connections between computational physics, chemical engineering, and numerical analysis. He participated in EPSRC-funded project EP/F006802/1 (now inactive), collaborating within the Computational Engineering and Design Group—a multidisciplinary unit integrating analytical, computational, and experimental techniques for engineering simulation. No information is available regarding student supervision or additional grants. Nair is embedded in the Computational Engineering and Design Group, which operates as a center for advanced engineering simulation combining high-fidelity computational models with experimental validation across physical domains.
Dr. Jonathan Schmidt is a Lecturer at the Department of Materials, ETH Zurich. His research focuses on computational materials science, integrating machine learning and high-throughput methods to predict material properties, design novel materials, and advance electronic structure theory. He specializes in quantum critical phenomena, superconductivity, and the development of open-source tools for materials discovery. Key research areas include symmetry-based material design, density functional theory corrections, and the OPTIMADE API for data exchange. His work bridges machine learning with traditional computational methods to address challenges in phase stability prediction, electronic band structure analysis, and high-throughput screening. Dr. Schmidt collaborates on interdisciplinary projects involving crystallography, thermodynamics, and advanced software frameworks like Atomate2. His contributions aim to accelerate materials innovation through automated workflows and data-driven approaches.
Anuj Srivastava is a Professor in the Department of Statistics at Florida State University. His research focuses on statistical shape analysis, computational geometry, and their applications in medical imaging, computer vision, and machine learning. He develops novel methods for analyzing complex shapes, trajectories, and functional data, with particular emphasis on elastic geometry and manifold-based techniques. Key research themes include shape analysis of 3D objects, brain subcortical structures, and tree-like anatomical networks. His work integrates statistical theory with computational tools to address challenges in biomedical imaging, motion tracking, and data-driven modeling. Notable contributions include frameworks for longitudinal elastic shape analysis (LESA), graph-based mobility modeling, and Bayesian emulation of human motion. Recent projects involve NSF-funded collaborations on stochastic shape processes, pandemic simulation (RAW-ALPS), and statistical analysis of chromosome conformations. His methods are applied to diverse domains such as neuroimaging, cell morphology analysis, and electrical load profiling. The research emphasizes interdisciplinary applications with clinical, environmental, and engineering relevance.
Martin Lanser is a Researcher at the University of Cologne's Department of Mathematics and Computer Science and a Core Scientist at the Center for Data and Simulation Science (CDS). His work focuses on developing efficient numerical methods for computational science and engineering problems, particularly targeting modern many-core architectures with million-way parallelism. His research spans Computational Science and Engineering, Numerical Methods, and Parallel Computing, with specialized expertise in Domain Decomposition Methods, Multigrid Approaches, and Computational Homogenization for heterogeneous solid mechanics. Lanser's theoretical work emphasizes nonlinear solvers for strongly heterogeneous materials, integrating algebraic multigrid techniques to enhance scalability in extreme-scale simulations. Analysis of his publication record reveals consistent innovation in scalable domain decomposition methods since 2014, with recent work targeting exascale computing through the FE2TI software framework. His research demonstrates strong interdisciplinary connections between computational mathematics, materials science, and high-performance computing, particularly in applications for dual-phase steel modeling. No scientific awards are documented in the provided information. Lanser's advising and grant activities are not specified in the source material, though his collaborative publications indicate extensive partnerships with researchers like Axel Klawonn and Oliver Rheinbach on projects including SCALEXA and High-Q club initiatives. As a core developer of the FE2TI software package—a computational homogenization implementation selected for the High-Q club—Lanser contributes to the CDS's research in quantitative modeling of complex physical systems. His work directly supports exascale computing projects focused on parallel domain decomposition methods and is integrated into the university's numerical analysis research infrastructure at numerik.uni-koeln.de.
Giancarlo Sangalli is a Professor in the Department of Mathematics at the University of Pavia. His research focuses on Scientific Computing, particularly Numerical Methods and Applications, with a strong emphasis on Isogeometric Analysis (IGA) for solving Partial Differential Equations (PDEs). He leads the Scientific Computing group and contributes to interdisciplinary fields such as computational mechanics, biomedical engineering, and environmental modeling. His work integrates advanced numerical techniques, including high-order finite element methods, space-time formulations, and matrix-free solvers, to address challenges in computational efficiency and accuracy. Key areas of application include cardiac electrophysiology, wave propagation, and groundwater flow modeling. Sangalli has pioneered low-rank solvers, Tucker tensor-based methods, and immersed boundary techniques to enhance computational scalability. He actively publishes in top journals and conferences, with a focus on advancing IGA theory and its applications to real-world problems. His research also explores uncertainty quantification, Bayesian calibration, and nonlinear dynamics. Despite no explicitly listed awards, his extensive publication record reflects recognition in computational mathematics and engineering. Sangalli collaborates internationally and maintains a research website at https://mate.unipv.it/sangalli . His group's work is supported by projects in computational electromagnetics, structural mechanics, and fluid-structure interaction, demonstrating a commitment to bridging theoretical advancements with practical engineering solutions.
Xiao Fu is an Associate Professor in the Department of Electrical Engineering and Computer Science at Oregon State University's College of Engineering. He holds a B.S. (2005) and M.S. (2010) in Communications and Information Engineering and Signal and Information Systems from the University of Electronic Science and Technology of China (UESTC), and a Ph.D. (2014) in Electronic Engineering from The Chinese University of Hong Kong (CUHK). His research focuses on machine learning, signal processing, and optimization, with applications in nonlinear factor analysis, unsupervised learning, and deep neural networks for signal processing tasks. He has received notable awards including the 2022 NSF CAREER Award and the 2024 OSU Promising Scholar Award. His work emphasizes developing robust algorithms for factor analysis (e.g., tensor and matrix factorization), large-scale optimization in data mining, and deep learning techniques for hyperspectral imaging, radio map estimation, and crowd-sourced label analysis. Key research groups affiliated with him include Data Science and Engineering, Artificial Intelligence and Robotics, and Communications and Signal Processing. His recent projects include advancing unsupervised machine learning to reduce reliance on labeled data in AI systems and exploring applications in environmental sensing and medical imaging. Xiao Fu's publications span topics like radio map estimation via latent-domain denoisers, noisy label learning with crowd wisdom, and identifiability in nonlinear mixture models. His research bridges theoretical advancements in optimization and practical applications in wireless communication, ecological networks, and biomedical imaging. Current efforts aim to enhance unsupervised deep representation learning and develop scalable algorithms for high-dimensional data analysis. Awards: NSF CAREER Award (2022), OSU Promising Scholar Award (2024) Grants: NSF-funded CAREER Award project on nonlinear factor analysis tools Labs/Teams: Affiliated with interdisciplinary teams in signal processing, AI, and ecological systems modeling
Dr. Ivo Dravins is a PostDoc at the Chair of Numerical Analysis within the Faculty of Mathematics at Ruhr-Universität Bochum, working in Prof. Katharina Kormann's research group. He focuses on preconditioning techniques for implicit time-stepping algorithms, particularly within the PDExa project. His research spans numerical linear algebra, implicit Runge-Kutta methods, and PDE-constrained optimization. Research Interests: Preconditioning of large-scale linear systems Implicit time-stepping algorithms for PDEs High-order numerical methods Optimal control problems with constraints Numerical linear algebra applications Key Research Trends: His work emphasizes scalable preconditioning strategies for parallel computing environments, with a focus on achieving high-order accuracy in time integration. Recent efforts have addressed stage-parallel Runge-Kutta implementations and spectral analysis of preconditioned matrices in PDE-constrained optimization contexts. Labs/Teams: Member of Prof. Kormann's Numerical Analysis group, contributing to the PDExa project. Associated with the Kormann Group within the Faculty's Numerics division.
Froilan Cesar Martinez Dopico is a Full Professor of Applied Mathematics in the Department of Mathematics at Charles III University of Madrid, where he has been a faculty member since 1991. He served as Department Chair from 2020-2022 and currently serves as Editor-in-Chief of Linear Algebra and its Applications. His extensive service includes Vice-President of the International Linear Algebra Society and membership on the prestigious Householder Committee. Education: BSc in Theoretical Physics, Universidad Complutense de Madrid (1987) PhD in Atomic, Molecular, and Nuclear Physics, Universidad Complutense de Madrid (1992) Professor Dopico's research focuses on Numerical Linear Algebra and Matrix Theory, with particular emphasis on structured matrices, matrix perturbation theory, and polynomial/rational eigenvalue problems. His work bridges theoretical developments with practical computational algorithms, contributing significantly to understanding accuracy and stability in numerical computations. He has developed novel approaches for matrix pencils, linearizations, and structured matrix polynomials that have advanced the field. His scientific contributions have been recognized through his election as SIAM Fellow in 2019. This prestigious honor acknowledges his outstanding contributions to applied mathematics and computational science, particularly in advancing the theoretical foundations and practical applications of numerical linear algebra. Professor Dopico has secured continuous research funding since 2000 as Principal Investigator on multiple grants from Spanish government agencies. He has led research teams of 8-11 members on projects related to structured numerical linear algebra, matrix polynomials, and matrix equations. His editorial leadership includes serving as Editor-in-Chief for two major journals in the field and associate editor for several others, shaping the direction of research in numerical linear algebra worldwide.
João Narciso is an Invited Assistant Professor at the Department of Mineral and Energy Resources Engineering, Higher Technical Institute. His research focuses on geostatistical inversion techniques applied to geophysical data, with emphasis on electromagnetic and seismic methods for near-surface characterization, landfill modeling, and resource exploration. He teaches Geophysical Interpretation and Inversion and is affiliated with CERENA (www.cerena.pt/user/596). Key research areas include integrating FDEM (frequency-domain electromagnetic) and ERT (electrical resistivity tomography) data for 3D subsurface modeling, developing algorithms for self-updating probability distributions in seismic inversion, and assessing heterogeneous environments through electromagnetic methods. His work addresses practical challenges in environmental monitoring, waste management, and seismic hazard assessment in regions like the Lisbon area and Lower Tagus Valley Fault Zone. Publications emphasize methodological advancements (e.g., randomized tensor decomposition for FDEM inversion) and real-world applications (e.g., landfill characterization and resource deposit modeling). While no awards are listed, his contributions highlight innovative approaches to geophysical data integration and uncertainty quantification. Teaching and advising activities are not explicitly detailed in the provided materials, though his academic role suggests involvement in student mentorship. Collaborations likely extend to interdisciplinary teams addressing geotechnical and environmental challenges in Portugal and beyond.
Natalie Beams is a Research Assistant Professor at the Innovative Computing Laboratory (ICL) within the University of Tennessee's Department of Electrical Engineering and Computer Science (EECS). She holds a PhD in Theoretical and Applied Mechanics from the University of Illinois at Urbana-Champaign (UIUC), an MS from UIUC, and a BS in Mechanical Engineering (summa cum laude) from the University of Oklahoma. Her research focuses on numerical methods for PDEs, high-performance computing (HPC), and GPU-accelerated algorithms, with contributions to projects like the Exascale Computing Project's CEED and CLOVER initiatives. Key research areas include finite element methods , integral equation solvers , and mixed-precision algebraic multigrid techniques . She has developed software tools such as the libCEED library for high-order discretizations and contributed to the Ginkgo and MAGMA libraries. Her work emphasizes exascale computing, GPU optimization, and parallel algorithm design. Education: PhD in Theoretical & Applied Mechanics, UIUC (2017) MS in Theoretical & Applied Mechanics, UIUC (2014) BS in Mechanical Engineering, University of Oklahoma (2010) Awards & Honors: Best Workshops Paper Award, PPAM Conference (2022) 2011/2012 Computational Science & Engineering Fellow 2010 College of Engineering Carver Fellow List of Teachers Ranked as Excellent by Students (UIUC, 2014) Grants & Projects: Active contributor to the Exascale Computing Project (ECP), leading efforts in CEED (libCEED library) and CLOVER (MFEM-Ginkgo interoperability). Collaborates with Rice University and other institutions on HPC and numerical algorithms. Labs & Teams: Core member of the Innovative Computing Laboratory (ICL) at UTK, specializing in exascale software and GPU-accelerated computing.
Xingjie (Helen) Li is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. Her research focuses on numerical analysis, partial differential equations, and mathematical physics with an emphasis on nonlocal models, multiscale methods, and computational mechanics. She develops advanced numerical schemes for stochastic systems and explores applications in materials science, image processing, and epidemiological modeling. Her work bridges theoretical analysis and practical computation, addressing challenges in coarse-graining stochastic dynamics, atomistic-to-continuum coupling, and probabilistic collocation methods for heterogeneous materials. Recent studies include innovative approaches to nonlocal diffusion problems, peridynamic fracture modeling, and data-driven time-stepping algorithms for ergodic systems. She has also applied mathematical techniques to analyze real-world scenarios such as pandemic dynamics in Singapore and Japan. Key contributions include energy-preserving finite difference schemes, feature-oriented imaging compression frameworks, and inference-based adaptive methods. Her research portfolio reflects interdisciplinary collaboration spanning computational physics, applied mathematics, and data science.
