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Martin SiebenbornView profile

Professor

• Shape Optimization
• Computational Mathematics
• Fluid Dynamics
• +3 more

Martin Siebenborn is a Junior Professor (W1) for Optimization and Approximation at the Department of Mathematics, University of Hamburg. His research focuses on shape optimization with applications in fluid dynamics, structural mechanics, and partial differential equations (PDEs), emphasizing scalable high-performance algorithms and multigrid methods. Education: PhD in Mathematics (2014, Universität Trier), Diploma in Mathematics (2010, Universität Trier). His research integrates algorithmic scalability for PDE-constrained optimization, leveraging multigrid preconditioners and nonlinear extension operators. Recent projects include simulation-based design optimization under uncertainties and scalable shape optimization for fluid dynamics. Onyshkevych, Pinzon Escobar, and Wyschka are part of his research team. He has developed open-source software tools like MinFEM, MGSS, and MultigridShapeOpt for teaching and high-dimensional data analysis.

• Numerical Analysis
• Sparse Grids
• Machine Learning
• +3 more

Prof. Dr. Michael Griebel is a leading academic at the Institute for Numerical Simulation (INS) at the University of Bonn , Germany. His work focuses on numerical methods for partial differential equations (PDEs), sparse grid techniques, and applications in computational science, machine learning, and materials modeling. Research Interests : Numerical analysis, sparse grids, high-dimensional approximation, tensor product methods, stochastic processes, and multiscale simulations. Collaborations : Co-author of over 50 publications with researchers in computational mathematics, fluid dynamics, and quantum chemistry. Prominent research trends include advancements in sparse grid methods for uncertainty quantification, kernel-based regression, and efficient solvers for ill-posed problems. His work on space-filling curves and domain decomposition enables scalable parallel algorithms for complex simulations. Scientific Contributions : Editor of 10+ volumes in Lecture Notes in Computational Science and Engineering . Co-developer of the EXAHD exascale sparse grid framework for plasma physics simulations. Foundational work on tensor product spaces and their applications to electronic structure theory. Innovations in adaptive wavelet solvers and parallel multigrid methods. Software and Projects : Key developer of sparse grid solvers and adaptive algorithms. His INS Preprint Series archives over 200 technical reports, reflecting his group’s impact on computational methods.

Anqi LiuView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Distributionally Robust Learning
• +5 more

Anqi Liu (Angie) is an Assistant Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University. She is affiliated with the Data Science and AI Institute, Mathematical Institute for Data Science (MINDS), and Institute for Assured Autonomy (IAA). Her research focuses on developing principled machine learning algorithms for reliable, trustworthy, and human-compatible AI systems in high-stakes applications. PhD in Computer Science from University of Illinois Chicago Postdoctoral Research at Caltech's Department of Computing and Mathematical Sciences Her work emphasizes robustness to changing data environments, uncertainty quantification, and human-AI interaction. Key methodologies include distributionally robust learning, active learning, safe exploration, fair machine learning, and conformal prediction. Applications span healthcare (NIA/NIH-funded), robotics, and computational social science. Amazon Research Award Johns Hopkins + Amazon Initiative for AI Faculty Research Johns Hopkins Discovery Award Institute for Assured Autonomy Challenge Grant She advises PhD candidates in AI safety and fairness, with students co-advised by faculty in Human-Robot Interaction and Computational Linguistics. Collaborations include Center for Language and Speech Processing (CLSP) and Laboratory for Computational Sensing and Robotics (LCSR).

• Graph Machine Learning
• Signal Processing
• Higher-Order Networks
• +5 more

Elvin Isufi is an Associate Professor at the Delft University of Technology (TU Delft) , where he co-founded and co-directs AIdroLab , one of the 24 TU Delft AI Labs. His research focuses on fundamental and applied graph-based data processing with applications to water networks, flood modeling, infrastructure systems, and recommender systems. Elvin obtained his Ph.D. from TU Delft in graph signal processing and completed his master's and bachelor's studies at the University of Perugia in Italy. He has been mentored by renowned academics including Prof. Alejandro Ribeiro (postdoc), Prof. Geert Leus (Ph.D.), and Prof. Paolo Banelli (master's/bachelor's). His research integrates signal processing , machine learning , and mathematical modeling to develop techniques for graph signal processing, graph neural networks (GNNs), and higher-order network analysis. Key application domains include water distribution networks , flood modeling , and recommender systems . His work addresses critical challenges such as stability analysis of GNNs, dynamic graph processing , and physics-informed machine learning . Recent publications highlight advancements in multi-scale hydraulic GNNs for flood prediction, carbon footprint-aware recommender systems , and simplicial vector autoregressive models for edge flow analysis. His research group includes Ph.D. students like Bishwadeep Das and M.Sc. students exploring topics such as online edge flow prediction and topological signal processing . 2021: Audience Choice Award, IEEE Data Science and Learning Workshop 2022: Best Student Paper Award, IEEE DSLW 2023: Top 3% Recognition Award, ICASSP Elvin actively supervises students in graph machine learning projects, emphasizing requirements like Python and PyTorch/TensorFlow expertise . He provides structured thesis project themes covering dynamic graph analysis , physics-informed GNNs , and self-supervised learning for networked data.

• Machine Learning
• Economics/Blockchain
• Mathematical Physics
• +8 more

Franck Gabriel is an Associate Professor at University Claude Bernard Lyon 1 , affiliated with the Institut de Science Financière et d'Assurances (ISFA) . His research bridges Machine Learning , Economics/Blockchain , Mathematical Physics , and Random Matrices , with notable work on neural tangent kernels, DeFi protocols, and asymptotic matrix theory. Research Focus : Machine Learning: Theoretical analysis of neural networks, kernel methods, and generalization bounds. Blockchain Economics: Decentralized finance, staking mechanisms, and smart contract design. Mathematical Physics: Holonomy fields, Yang-Mills theory, and random matrix asymptotics. Recent Publications highlight trends in denoising diffusion models, free probability in matrix theory, and DeFi credit systems. His work often integrates cross-disciplinary approaches, merging deep learning with financial technology and quantum field theory. Scientific Awards : 2025 AI 2000 Most Influential Scholar Award in Theory 2024 AI 2000 Most Influential Scholar Award in Theory 2023 AI 2000 Most Influential Scholar Award in Theory As an organizer of the ISFA Seminar , he fosters interdisciplinary discussions in insurance, economics, and machine learning. His collaborations span institutions like Ecole Polytechnique Fédérale de Lausanne, Courant Institute, and EPFL.

• Biostatistics
• Genetics
• Mechanisms of Cancer
• +5 more

Eric Lock is an Associate Professor in the Department of Biostatistics at the University of Minnesota. His research spans biostatistics, genetics, proteomics, computational biology, and neuroimaging, with a focus on neurodegenerative diseases like ALS and Friedreich's ataxia, as well as pulmonary sarcoidosis and microbiome research. Current affiliations: University of Minnesota (2020-present), NIH-funded projects Key collaborators: NIH (NHGRI, NHLBI), Ohio State University, University of Chicago, FDA Research Interests : Eric develops advanced statistical and computational methods for high-dimensional biological data. His work includes tensor array techniques for RNA-Seq analysis, proteomic classifiers for pulmonary sarcoidosis, and neuroimaging biomarkers for tracking neurodegenerative disease progression. He also contributes to microbiome dataset curation frameworks. Article Trends : Recent publications emphasize Bayesian statistical models for missing data Neuroimaging in ALS and Friedreich's ataxia Proteomic and microbiome data integration Computational immunity models for vaccination response prediction Grants & Collaborations : Actively leads NIH-funded projects on tensor array methods and human microbiome compendium. Collaborates on multimodal imaging studies with institutions across the US, focusing on longitudinal disease tracking and molecular diagnostics.

Ajey JacobView profile

Research Professor

• Silicon photonics
• Non-volatile memory
• Neuromorphic computing
• +5 more

Ajey Jacob is the Director of the Application Specific Intelligent Computing (ASIC) Lab at the University of Southern California's Information Sciences Institute (ISI). With 16 years of industry experience from Intel Corporation and GlobalFoundries, Dr. Jacob brings deep expertise in semiconductor research, development, and manufacturing to his academic role. His professional journey includes founding and leading GlobalFoundries' Differentiating Technology Research Group and managing strategic research consortia at Intel. Dr. Jacob's research focuses on advanced semiconductor technologies with particular emphasis on silicon photonics , non-volatile memory (MRAM, FeRAM, RRAM), photonic SRAM , in-memory computing , and processing-in-pixel architectures . His work bridges materials, devices, integration, and fabrication aspects of next-generation computing systems. Recent publications demonstrate a strong trajectory toward photonic computing for AI acceleration and energy-efficient edge intelligence systems. Dr. Jacob's publication record is exceptional with over 300 worldwide patents (including more than 220 USPTO issued patents), four book chapters, and more than 100 journal and conference papers. His most recent work (2023-2025) shows continued innovation in photonic memory systems, neuromorphic computing architectures, and optical interconnects for high-performance computing. Master Inventor at GlobalFoundries (2017-2020) Semiconductor Research Corporation (SRC) Mahboob Khan Outstanding Industry Liaison Award (2013, 2016) Dr. Jacob actively contributes to the research community as a committee member for IEEE Electronics Components and Technology Conference (ECTC) Silicon Photonics Session, has served as chair and co-chair for the International Conference on Frontiers of Characterization and Metrology for Nanoelectronics (FCMN), and is co-chair for the MIT/AIM Photonics monolithic integration session of the Integrated Photonics Systems Roadmap (IPSR). His industry-academia bridge provides unique opportunities for students interested in both fundamental research and practical semiconductor technology development. The ASIC Lab under Dr. Jacob's direction represents a convergence point for photonics, memory systems, and AI acceleration research, with strong connections to semiconductor industry challenges and opportunities. His work on photonic SRAM and in-memory computing addresses critical bottlenecks in modern computing architectures while maintaining practical considerations from his extensive industry experience.

• Spatial Statistics
• Computational Biology
• Bayesian Modeling
• +3 more

Joel Eliason is a postdoctoral researcher in the Popel Lab at Johns Hopkins University , specializing in computational biology and spatial statistics. His work focuses on developing Bayesian methods for modeling asymmetric spatial associations between cell types in tissue microenvironments. Developed the SHADE R package for directional spatial analysis Co-created DIMPLE framework for cell-cell interaction modeling Research interests span: Multilevel Bayesian modeling Uncertainty quantification in virtual clinical trials Spatial point process modeling Tumor microenvironment analysis Recent publications demonstrate expertise in: High-dimensional multiplex imaging analysis Spatial interaction curves (SICs) Joint modeling across subjects Tensor decomposition for spatial contexts Technical skills include: Stan and CmdStanr for Bayesian inference Reproducible R package development Quarto and knitr for computational documentation

• Mathematical Physics
• Clifford Algebras
• Yang-Mills Equations
• +4 more

Dmitry Shirokov is a Professor at the Department of Mathematics within the Faculty of Economic Sciences at the National Research University Higher School of Economics (HSE) in Moscow. He concurrently serves as a Senior Researcher at the M.S. Pinsker Laboratory №1 of the Institute for Information Transmission Problems, Russian Academy of Sciences (IITP RAS). He heads the Laboratory of Geometric Algebra and Applications at HSE and leads multiple research groups including 'Clifford Algebras and Applications' and 'Algebraic-Geometric Methods in Applied Sciences'. Shirokov's research focuses on mathematical physics, Clifford algebras, Yang-Mills equations, Dirac equations, and geometric algebras. His work bridges theoretical mathematics with applications in quantum field theory and particle physics, exploring algebraic structures like spinor groups and multivectors in pseudo-Euclidean spaces. His publications demonstrate strong trends in Clifford algebra generalizations, geometric approaches to quantum equations, and computational methods in algebra. Recent work shows increasing integration of geometric algebra with machine learning architectures. Scientific Awards: Best Teacher of HSE (2016-2024) Winner of Best Russian-Language Scientific Works Competition in Mathematics (2022, 2023) Grants and Leadership: RSF Grants: 18-71-00010, 21-71-00043, 23-71-10028 RFBR Grants: 16-31-00374, 20-11-0009 Presidential Grant: MK-404.2020.1 HSE Scientific Foundation Grants: 17-01-0009, 20-01-003, 22-00-001, 24-00-001 Leads 'Mirror Laboratories' project with North-Eastern Federal University He directs the Laboratory of Geometric Algebra and Applications at HSE and organizes regular seminars on Clifford algebras. Serves on editorial boards for Advances in Applied Clifford Algebras and Journal of Geometry and Symmetry in Physics .

• Systems and Control Theory
• Differential-Algebraic Equations
• Data-Driven Modelling
• +3 more

Jan Heiland is an Assistant Professor at Otto von Guericke University Magdeburg and a Researcher at the Max Planck Institute for Dynamics of Complex Technical Systems. His work focuses on computational methods in systems and control theory, particularly for differential-algebraic equations (DAEs) and data-driven modeling of fluid dynamics. Research Interests: Systems and Control Theory, Differential-Algebraic Equations, Data-Driven Modelling, Flow Control, Fluid Dynamics, Reduced-Order Modeling Publication Trends: Recent work emphasizes low-dimensional approximations of nonlinear systems using machine learning techniques (e.g., polytopic autoencoders, convolutional autoencoders) and robust control methods for Navier-Stokes equations. Key themes include model reduction, uncertainty quantification, and computational approaches to large-scale dynamical systems. Teaching: He teaches numerical methods for partial differential equations and scientific computing at TU Ilmenau and OVGU Magdeburg.

Nathan B. GawView profile

Assistant Professor

• Machine Learning
• Multimodal Data Fusion
• Healthcare Telemonitoring
• +7 more

Dr. Nathan B. Gaw is an Assistant Professor of Data Science at the Air Force Institute of Technology (AFIT), where he develops advanced statistical machine learning algorithms for multimodal data fusion in defense, healthcare, and remote sensing applications. He holds a Ph.D. in Industrial Engineering from Arizona State University (2019) and completed postdoctoral research at both the ASU-Mayo Clinic Center for Innovative Imaging (2019-2020) and Georgia Institute of Technology's School of Industrial and Systems Engineering (2020-2021). Research Interests: His work focuses on three core areas: Feature extraction/selection using tensor decomposition and functional data analysis Spatio-temporal neural networks with attention-based ConvLSTMs Image registration/fusion through convolutional neural networks Key application domains include aviation human factors, weather remote sensing, and cybersecurity. Scientific Awards: Dr. Gaw has received 14 major scientific awards including: AFWERX Datathon Challenge (5 awards) INFORMS Principal Cup (2nd place) Harold Wolff-John Graham Award (American Academy of Neurology) Tau Beta Pi Fellowship Dean's Fellowship, ASU Professional Engagements: He serves as chair of the INFORMS Data Mining Society and council member of IISE Data Analytics Division, while maintaining IEEE membership. His 16 peer-reviewed publications (12 Q1, 4 Q2) include a U.S. patent and book chapter contributions.

Jian CaoView profile

Professor

• Manufacturing Processes
• Deformation-based Manufacturing
• Laser Additive Manufacturing
• +5 more

Jian Cao is the Cardiss Collins Professor of Mechanical Engineering at Northwestern University's McCormick School of Engineering, with courtesy appointments in Civil Engineering and Materials Science. She serves as Director of the Northwestern Initiative for Manufacturing Science and Innovation (NIMSI) and Associate Vice President for Research. Her education includes a PhD in Mechanical Engineering from MIT and a BS from Shanghai Jiao Tong University. Professor Cao leads pioneering research in manufacturing systems, focusing on deformation-based processes, laser additive/subtractive technologies, and AI-driven manufacturing innovations. Her work integrates material behavior characterization, computational modeling, and machine learning to advance energy-efficient manufacturing and surface engineering. Recent publications demonstrate strong emphasis on AI-accelerated simulations, real-time process control, and multi-material systems for additive manufacturing. Recognized with elite honors including membership in both the National Academy of Engineering and American Academy of Arts & Sciences, her scientific awards span: ASME DeVor-Kapoor Manufacturing Medal (2023) Vannevar Bush Faculty Fellowship (2019) SME Gold Medal (2020) Frederick W. Taylor Research Medal (2016) She directs the Cao Research Group developing novel manufacturing equipment and leads NIMSI's industry-academic partnerships. Her professional service includes editorial roles for leading journals and leadership positions in ASME/SME manufacturing divisions.

• Nonlinear System Identification
• Machine Learning
• Computational Neuroscience
• +7 more

Hua-Liang Wei is a Senior Lecturer at the University of Sheffield 's School of Electrical and Electronic Engineering. He leads two innovative research labs: the Dynamical Modelling, Data Mining and Decision Making (3DM) and the Digital Medicine & Computational Neuroscience (DMCN) Research Groups. Specializes in system identification for nonlinear dynamics Develops interpretable AI for healthcare applications Active in space weather and environmental forecasting His methodological expertise spans NARMAX modeling, wavelet neural networks, and multiresolution analysis. Collaborations include Sheffield Teaching Hospitals NHS Trust, multiple University of Sheffield departments (Chemistry, Oncology, Psychology), and international institutions like Beihang University. Scientific Awards include STFC and NERC grants for radiation belt modeling and environmental systems research, EU Horizon 2020 funding, EPSRC Platform grants, Royal Society support, and medical charity partnerships. Recent publications focus on hybrid wavelet-LSTM for wind power forecasting EEG analysis in epilepsy and Alzheimer's domain adaptation for fault diagnosis interpretable models for medical data covering applications from renewable energy to clinical diagnostics.

Antonio CausaView profile

Assistant Professor

• Algebraic Geometry
• Coding Theory
• Variational Inequalities
• +3 more

Antonio Causa is an Assistant Professor of Geometry at the Department of Mathematics and Computer Science, University of Catania. He has held this position since 2000, following his appointment as a researcher in Geometry in 1997. His research spans multiple mathematical disciplines: Algebraic Geometry, with significant contributions to the Waring problem for real binary symmetric tensors Coding Theory Variational Inequalities, focusing on regularity properties of solutions Commutative Algebra and Combinatorial Geometry, particularly Edge ideals and Covering ideals associated with matroids and Steiner systems Causa's work addresses fundamental questions in tensor decomposition and ideal theory, examining how combinatorial structures relate to algebraic properties. His research on the symbolic power containment problem (I (m) ⊆ I r ) has advanced understanding of algebraic invariants including Betti numbers, Hilbert functions, and Castelnuovo-Mumford regularity. As an educator, he has taught Geometry, Linear Algebra, and Coding Theory across Engineering, Mathematics, and Computer Science programs since 1998, demonstrating his ability to bridge theoretical concepts with practical applications. His office is located in room 332 of the Department of Mathematics and Computer Science, with office hours held on Tuesdays (9:00-11:00) and Thursdays (11:00-13:00).

Elena Maria GUARDOView profile

Professor

• Algebraic Geometry
• Commutative Algebra
• Combinatorics
• +5 more

Elena Maria GUARDO is a Full Professor of Geometry (SSD Mat/03) at the Department of Mathematics and Computer Science (DMI) of the University of Catania. Since October 2024, she serves as the Responsible of the Mathematics Section of DMI for the 2024-2028 period. Previously, she was the Leader of the First Degree Level in Mathematics from January 29, 2019 to September 30, 2023. She teaches courses in both Engineering and Mathematics programs at the university. Her primary research interests focus on Algebraic Geometry, Commutative Algebra, and Combinatorics, with specific expertise in 0-dimensional schemes in (multi)projective spaces, Hadamard Varieties, and Steiner systems. Her work extends to fat points in projective spaces, extended bicolorations of Steiner systems, and applications of graph theory. As a prominent researcher, Professor Guardo serves as Local Principal Investigator for multiple significant projects including the national PRIN 2022 initiative on 0-Dimensional Schemes and Tensor Theory, and the PIACERI 2024-26 project on Geometry and Topology with combinatorial applications. She has been involved in numerous PRIN projects dating back to 2003. Local PI of PRIN 2022 project: 0-DIMENSIONAL SCHEMES, TENSOR THEORY, AND APPLICATIONS Local PI of PIACERI 2024-26 project: Geometry, Topology towards Combinatorial applications Local PI of PIACERI 2020-22 project: New Trends in Combinatorics, Geometry and Topology Member of PRIN projects (2003, 2005, 2007, 2011, 2015, 2020) Professor Guardo actively organizes academic events including the 'Mathematics in the Street' initiative and regular seminars featuring international researchers. Her supervision spans diverse thesis topics including Singular Value Decomposition applications, graph theory in biological networks, matroid theory, and combinatorial geometry problems. She maintains an active research presence with publications indexed on Scopus (author ID: 6507958419) and arXiv.