جست‌وجوی استاد و پژوهشگر

Laura Ciobanu Radomirovicمشاهده پروفایل

استاد

• Combinatorial group theory
• Geometric group theory
• Algorithmic group theory
• +۳ مورد دیگر

Laura Ciobanu Radomirovic is a Professor in the Department of Mathematics at Heriot-Watt University's School of Mathematical and Computer Sciences, where she serves as Co-Director of the Maxwell Institute for Mathematical Sciences. Her research profile shows active engagement in both theoretical and computational aspects of mathematics with significant contributions to group theory and related fields. Her educational background includes: Bachelor's degree in Mathematics with minor in Computer Science from Franklin & Marshall College, Lancaster PA (1999) Third year studies at ETH Zürich, Switzerland PhD in Mathematics from Rutgers University, NJ (January 2005) Professor Ciobanu Radomirovic specializes in combinatorial, geometric and algorithmic group theory, with strong connections to combinatorics and theoretical computer science. Her work bridges abstract mathematical concepts with computational applications, particularly focusing on equation solving in groups, growth functions, and the interplay between language theory and algebraic structures. She maintains active research collaborations across multiple countries, as evidenced by her international network of co-authors and research partners. Analysis of her recent publications (2023-2025) reveals a consistent focus on group equations, growth phenomena in various algebraic structures, and the application of computational methods to solve theoretical problems. Her work shows increasing sophistication in handling virtually abelian groups, dihedral Artin groups, and the connections between formal languages and group-theoretic properties. The interdisciplinary nature of her research is evident in the blend of pure mathematics, computational approaches, and theoretical computer science concepts. Her notable scientific achievements include: Swiss National Science Foundation professorship Ambizione fellowship at the University of Neuchâtel Fellowship at the Hausdorff Institute of Mathematics in Bonn Professor Ciobanu Radomirovic serves as Principal Investigator for an EPSRC Standard Grant (2018-2021) and actively mentors PhD students. Her professional service includes editorial work for the Proceedings of the Edinburgh Mathematical Society (2018-2025) and committee membership with the London Mathematical Society (2023-2027). She regularly presents her research through invited talks and conference contributions, maintaining an active presence in the international mathematical community. As Co-Director of the Maxwell Institute, she plays a key leadership role in one of the UK's prominent mathematical research centers, fostering collaboration between pure and applied mathematicians across Heriot-Watt University and the University of Edinburgh.

Şükran Uygunمشاهده پروفایل

دانشیار

• Number Theory
• Applied Mathematics
• Matrix Analysis
• +۳ مورد دیگر

Şükran Uygun is an Associate Professor at Gaziantep University's Faculty of Arts and Sciences, Department of Mathematics, where she has held positions since 2003. She earned her PhD in Mathematics from Selçuk University (2013) and a Master's in Mathematics from Gaziantep University (2005). Education: PhD (2013) - Selçuk University MS (2005) - Gaziantep University BS (2002) - Gaziantep University Her research focuses on number theory, matrix analysis, and special sequences, particularly bi-periodic Pell/Jacobsthal sequences and their matrix representations. She has published extensively on spectral norms of circulant/Toeplitz matrices and applications to coding theory. Recent publications analyze advanced encryption schemes using Fibonacci matrices, complex Jacobsthal numbers, and binomial transforms of generalized sequences. Her work connects Pell/Jacobsthal sequences with Chebyshev polynomials and explores negative-index extensions. Awarded twice by TÜBİTAK for publication excellence, she has supervised 15 graduate theses and taught courses in numerical analysis, special functions, and differential equations.

• Medical Imaging
• Cardiovascular Imaging
• Cardiology
• +۳ مورد دیگر

Markus Henningsson serves as an Adjunct University Lecturer at Linköping University's Department of Health, Medicine and Care (HMV) and Department of Diagnostics and Specialist Medicine (DISP). His academic work centers on advanced medical imaging techniques with particular emphasis on cardiovascular applications. His research interests span Medical Imaging , Cardiovascular Imaging , and Artificial Intelligence in Medicine . Dr. Henningsson specializes in developing and applying novel MRI techniques for cardiac analysis, particularly focusing on left atrial structure, epicardial adipose tissue, and fibrosis segmentation. His work bridges engineering methodologies with clinical cardiology applications. Analysis of his recent publications reveals a strong trend toward integrating machine learning with medical imaging, particularly using deep learning architectures like geometric UNet for tissue segmentation. His research consistently addresses clinically relevant questions in atrial fibrillation and cardiovascular disease assessment through advanced imaging biomarkers. While no formal scientific awards are documented in the available information, his work appears in reputable journals including Scientific Reports , Physics in Medicine and Biology , and Magnetic Resonance in Medicine . His collaborative approach is evident through numerous co-authorships with researchers across cardiovascular sciences. Dr. Henningsson's work appears connected to Linköping University's Cardiovascular Sciences Unit, which conducts research in cardiovascular physiology, cardiology, and related surgical disciplines. His contributions support the department's focus on both basic and clinical cardiovascular research with direct patient care applications.

• Riemannian Optimization
• Matrix Manifolds
• Low-Rank Matrix Completion
• +۴ مورد دیگر

Pierre-Antoine Absil is a Full Professor at the Louvain Polytechnic School (EPL) , part of the Université catholique de Louvain (UCLouvain) , affiliated with the Mathematical Engineering Center (INMA) and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM) . His research spans Riemannian optimization , matrix manifolds , and low-rank approximation with applications in astronomical imaging , signal processing , and environmental data imputation . His work emphasizes Riemannian optimization algorithms matrix and tensor completion geometric data analysis applications in astrophysics and bioinformatics Recent publications focus on Stiefel and Grassmann manifolds , variable projection methods , and exoplanet detection via alternating minimization . While no explicit scientific awards are listed, his collaborations with institutions like IEEE and Springer highlight his impact in computational mathematics and engineering. He has supervised researchers such as Simon Vary , Guillaume Olikier , and Valentin Christiaens through projects in direct imaging , economic dispatch , and graph-based data processing . His laboratory, INMA , drives methodological advances in manifold-valued data analysis and nonlinear optimization .

Giorgio Scorzelliمشاهده پروفایل

پژوهشگر

• Extreme Data Management
• Scientific Visualization
• Computational Topology
• +۶ مورد دیگر

Giorgio Scorzelli is a researcher at the University of Utah, serving as Director of Software Development for the Center for Extreme Data Management, Analysis, and Visualization (CEDMAV) and the National Science Data Fabric (NSDF) . He specializes in extreme data management, scientific visualization, and computational topology, with a focus on scalable solutions for climate science, materials science, and neuroscience datasets. His work emphasizes democratizing data access through platforms like OpenVisus , enabling efficient analysis of petascale and exascale data. Key contributions include orchestrating cyberinfrastructure, optimizing parallel I/O, and developing real-time visualization systems for heterogeneous resources. Notable scientific contributions include the NSF Grant #2127548 for NSDF development . His projects integrate cloud computing, geo-distributed storage, and FAIR digital objects to lower barriers to data democratization. Giorgio's research spans multi-resolution algorithms , computational topology , and 3D geometric modeling , with applications in infrastructure security, archaeological reconstruction, and biomedical imaging. His work bridges abstract mathematical frameworks (e.g., Boolean algebras, chain complexes) with practical software solutions.

Arijit Bishnuمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Randomized Algorithms
• Approximation Algorithms
• +۶ مورد دیگر

Arijit Bishnu is an Associate Professor at the Indian Statistical Institute in the Advanced Computing and Microelectronics Unit (ACMU). He has taught courses such as Design and Analysis of Algorithms , Randomized Algorithms , Computational Geometry , and Algorithms for Big Data over multiple years (2008–2025), focusing on theoretical and applied aspects of computer science. Research Interests: His work spans Theoretical Computer Science , Randomized and Approximation Algorithms , Computational Geometry , and Combinatorics . He explores problems in sublinear algorithms, streaming computation, geometric data analysis, and complexity theory. Publications: Recent papers include contributions to STOC 2025 , RANDOM 2025 , and APPROX 2024 , covering topics like property testing, triangle counting complexity, and streaming algorithms. Collaborations with researchers like Sourav Chakraborty, Gopinath Mishra, and Sayantan Sen highlight his interdisciplinary approach. Academic Leadership: He has co-organized research courses such as Approximation Algorithms and Topics in Algorithms and Complexity , emphasizing mentorship and knowledge dissemination in theoretical computer science. His comprehensive work integrates algorithmic innovation with rigorous mathematical analysis, advancing computational techniques for large-scale and geometric data.

Volkert Paulsenمشاهده پروفایل

مدرس ارشد

• Financial Mathematics
• Stochastic Analysis
• Optimal Stopping
• +۵ مورد دیگر

Volkert Paulsen is a Senior Lecturer at the Institute of Mathematical Stochastics at the University of Münster. His career spans institutions including the University of Kiel, where he completed his Habilitation (2000), Dissertation (1994), and Diplomarbeit (1989). He has taught extensively in Financial Mathematics , Stochastic Analysis , and Mathematical Statistics , supervising over 50 Bachelor, Master, and Diploma theses on topics such as risk modeling, portfolio optimization, and derivative valuation. Research Interests: Paulsen's work focuses on Financial Mathematics (continuous-time models, American options, unit-linked insurance), Stochastic Analysis (optimal stopping, martingale methods), and Risk Modeling (credit risk, extreme value statistics). His publications include foundational studies on nonlinear observation costs in optimal stopping problems and stochastic approaches to portfolio management. Scientific Contributions: His research spans journal articles in Stochastic Processes and their Applications and Journal of Applied Probability , with recent seminar topics covering Lévy Processes , Copula Modeling , and Stochastic Volatility . He employs R for statistical applications and integrates mathematical theory with practical finance and insurance contexts. Contact: Email: Volkert.Paulsen@uni-muenster.de Room: 130.010, Orléans-Ring 10, 48149 Münster Phone: +49 251 83-33771

• Acoustic Signal Processing
• Blind Source Separation
• Digital Signal Processing
• +۶ مورد دیگر

Shoji Makino is a Professor at Waseda University's Graduate School of Information, Production and Systems. He has held academic and research positions at institutions such as the University of Tsukuba and NTT Communication Science Laboratories. His work spans acoustic signal processing, blind source separation, and adaptive filtering. Education: Ph.D., Tohoku University (1993.03) Mechanical Engineering, Tohoku University Graduate School of Engineering (1979.04–1981.03) Engineering, Tohoku University Faculty of Engineering (1975.04–1979.03) Research Interests: His research focuses on acoustic signal processing for speech and audio, including blind source separation (BSS) , beamforming , and adaptive filtering . He pioneered methods for solving permutation alignment in frequency-domain BSS and developed geometrically constrained ICA techniques. Scientific Awards: Hoko Award (2018.10, Hattori Hokokai Foundation) Outstanding Contribution Award of the Institute of Electronics, Information, and Communication Engineers (2018.06) IEEE Signal Processing Society Best Paper Award (2014.01) IEEE Fellow (2004.01) IEICE Achievement Award (1997.05) Committee Memberships: He has served as Chair of the IEEE CAS Society's Blind Signal Processing TC, General Chair of IEEE WASPAA2007, and Associate Editor of IEEE Trans. SAP. He is actively involved in EURASIP, APSIPA, and the Acoustical Society of Japan.

• Biomedical Image Analysis
• Cryogenic Electron Microscopy (Cryo-EM)
• MRI and Ultrasound Image Segmentation
• +۵ مورد دیگر

Dr. Hemant Tagare is a Professor at Yale School of Medicine with primary appointments in the Radiology & Biomedical Imaging department and secondary appointments in Biomedical Engineering and Statistics . He is affiliated with multiple research centers, including the Bioimaging Sciences , Center for Brain & Mind Health , and Yale Biomedical Imaging Institute . Research Interests Cryogenic Electron Microscopy (Cryo-EM) for protein structure reconstruction MRI/ultrasound image segmentation using machine learning Non-rigid registration and shape theory Mathematical foundations of image analysis Articles Trends : Recent publications focus on cryo-EM particle picking consensus algorithms, T2-weighted MRI standardization, and IVIM perfusion measurement in peripheral artery disease. Earlier works emphasize segmentation techniques, non-rigid registration, and phantom validation. Scientific Awards Best Poster Award (2003) for ultrasound segmentation algorithm Francois Erbsmann Award (1993) for medical image processing National Science Talent Search Scholarship (1977) from India

Gaurav Rattanمشاهده پروفایل

استادیار

• Graph Theory
• Machine Learning on Graphs
• Graph Neural Networks
• +۵ مورد دیگر

Gaurav Rattan is an Assistant Professor in the Department of Applied Mathematics at the University of Twente's Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), where he joined in May 2024. His research focuses on the mathematical foundations of machine learning on graphs and discrete structures, with particular emphasis on theoretical aspects of graph neural networks. University of Twente, Department of Applied Mathematics (May 2024-present) TU Darmstadt, Postdoctoral Researcher in Pascal Schweitzer's group RWTH Aachen, DFG Eigene Stelle Researcher in Martin Grohe's group Dr. Rattan completed his PhD at IMSc Chennai under V. Arvind and earned his B. Tech. from IIT Bombay, establishing a strong foundation in theoretical computer science and mathematics. His research spans graph theory, algorithms, and machine learning on graphs, with specific expertise in graph isomorphism, graph homomorphisms, and the theoretical underpinnings of graph neural networks. He applies mathematical techniques from logic and algebra to develop theory-driven approaches for graph learning systems, with practical applications in optimization, bioinformatics, and databases. Dr. Rattan's publication record reveals a consistent focus on the intersection of theoretical computer science and machine learning. His recent work explores Weisfeiler-Leman algorithms, symmetry breaking techniques, and parameterized complexity of graph problems, demonstrating how classical graph algorithms connect with modern graph learning methodologies. His research provides crucial theoretical foundations for understanding the capabilities and limitations of graph neural networks. Active in the academic community, Dr. Rattan regularly presents at conferences including the Netherlands Mathematical Congress, SIGAlgo, LOGAMS, and specialized workshops on graph learning. Recent presentations include "From Graph Homomorphisms Densities to Graph Learning" at the Graph Learning Workshop at NITMB Chicago and "Color Refinement: One Algorithm, Many Facets" at SIGAlgo 2024.

Pablo G Cámaraمشاهده پروفایل

دانشیار

• Computational Biology
• Genetics
• Biomedical Informatics
• +۶ مورد دیگر

Pablo G Cámara is an Associate Professor of Genetics at the University of Pennsylvania's Perelman School of Medicine. He serves as Senior Fellow at the Institute for Biomedical Informatics and is affiliated with the Center for AI and Data Science in Integrated Diagnostics, the Statistical Center for Single-Cell and Spatial Genomics, and the Department of Genetics. His research focuses on computational approaches to cellular heterogeneity in diseases, particularly brain tumors, integrating topology, geometry, statistics, physics, and computer science. BS, Theoretical Physics, Universidad Autonoma de Madrid (2002) PhD, Theoretical Physics, Universidad Autonoma de Madrid (2006) His work combines single-cell technologies with mathematical frameworks to decode tumor ecosystems and signaling networks. Key themes include metric geometry applications, tumor-genome interactions, and interdisciplinary collaborations in neurodevelopment and cancer research. Recent publications highlight his methodological innovations in single-cell data analysis, tumor biology, CAR T-cell therapy, and quantum computing. Collaborations span immunology, neuro-oncology, and developmental biology domains. The Camara Lab at Penn Medicine develops computational tools for integrating morphometric, transcriptomic, and physiological data, aiming to advance precision oncology and developmental neuroscience through geometric and topological approaches.

• Machine Learning
• Statistical Learning Theory
• Algorithmic Fairness
• +۱۲ مورد دیگر

Massi Pontil is a part-time Professor of Computational Statistics & Machine Learning in the Department of Computer Science at University College London (UCL). He joined UCL as a lecturer in 2003 and was promoted to Professor in 2010. Since 2016, his primary appointment has been at the Istituto Italiano di Tecnologia (IIT), where he leads the CSML research group. His work bridges theoretical machine learning with practical applications in physical sciences. His research interests span a wide range of topics in machine learning theory and algorithms: Machine Learning Theory and Statistical Learning Algorithmic Fairness and Ethical AI Kernel Methods and Reproducing Kernel Hilbert Spaces Transfer Learning, Multitask Learning, and Meta-Learning Operator Learning and Dynamical Systems Sparsity Regularization and Optimization Pontil's recent work focuses on the intersection of machine learning with numerical simulations of physical systems, particularly in molecular dynamics and climate science. His publications demonstrate a strong emphasis on theoretical foundations while addressing practical challenges in high-dimensional systems, symmetry-aware learning, and uncertainty quantification. Among his notable honors are: Best Paper Runner Up Award from ICML 2013 EPSRC Advanced Research Fellowship (2006-2011) Edoardo R. Caianiello Award for the Best Italian PhD Thesis on Connectionism (2002) Professor Pontil has served on program committees for major machine learning conferences (COLT, ICML, NeurIPS) and on editorial boards of prestigious journals including Machine Learning Journal, Statistics and Computing, and JMLR. He teaches Advanced Topics in Machine Learning at UCL, with a focus on convex optimization and statistical learning theory.

Yihong Wuمشاهده پروفایل

استاد

• High-dimensional statistics
• Information theory
• Statistical inference on graphs
• +۳ مورد دیگر

Yihong Wu is the James A. Attwood Professor of Statistics and Data Science at Yale University, where he also serves as Chair of the Department of Statistics and Data Science. His academic career spans prestigious institutions with a focus on theoretical and applied statistical methods. His research bridges information theory and statistics, with applications across multiple domains of data science. Professor Wu's research focuses on the theoretical foundations of high-dimensional statistics, information theory, and optimization. His work explores dimensionality reduction through both intrinsic low-dimensionality (sparsity, smoothness) and extrinsic low-dimensionality (functional estimation). He has made significant contributions to understanding statistical-computational tradeoffs in problems involving random graphs and combinatorial structures. His research has important applications in machine learning, network analysis, and signal processing. His recent publications reveal a strong focus on information-theoretic approaches to statistical problems, with particular emphasis on graph matching, empirical Bayes methods, and high-dimensional inference. Wu's work consistently addresses fundamental questions about the limits of statistical estimation and the computational feasibility of achieving those limits. His research spans theoretical foundations while maintaining relevance to practical data analysis challenges. Professor Wu actively contributes to academic education through multiple graduate-level courses including Information Theory, Statistical Inference on Graphs, and Topics in High-Dimensional Statistics and Information Theory. His teaching reflects his research interests, emphasizing mathematical rigor and theoretical foundations.

Tim Ruben Davidsonمشاهده پروفایل

پژوهشگر

• deep generative models
• agentic systems
• synthetic data
• +۱ مورد دیگر

Tim Ruben Davidson is a Researcher at the Digital Life Lab (DLAB) within the School of Computer and Communication Sciences at EPFL. His current role is Doctoral Assistant, pursuing a doctoral program in Computer and Communication Sciences. His research focuses on advanced machine learning topics including deep generative models, agentic systems, synthetic data applications, and representation learning. His work bridges theoretical advancements in AI with practical implications, particularly in understanding AI agency, synthetic data generation, and latent space structures. Key research trends in his articles include exploring AI self-awareness, evaluating AI-driven peer review systems, and optimizing generative models through geometric and topological approaches. He has contributed to foundational studies on hyperspherical VAEs and reparameterization techniques on Lie groups, advancing mathematical foundations of neural networks. His work is published in top-tier venues and reflects interdisciplinary engagement between computer science, mathematics, and ethical AI considerations.

Xiaoli Fernمشاهده پروفایل

دانشیار

• Machine Learning
• Data Mining
• Graph Learning
• +۵ مورد دیگر

Xiaoli Fern is an Associate Professor in the School of Electrical Engineering and Computer Science at Oregon State University. She holds a Ph.D. in Computer Engineering from Purdue University (2005) and dual degrees (B.S. and M.S.) in Automation and Computer Science from Shanghai Jiao Tong University (2000). Her research focuses on applied machine learning , graph learning , and explainability in AI systems , with applications in microbiome analysis , ecological monitoring , and human-computer interaction . Research Expertise: Unsupervised learning, clustering, correlation analysis, outlier detection, and scientific data mining. Collaborations: Active involvement in the IGERT Ecosystem Informatics program and interdisciplinary projects with ecologists, roboticists, and biologists. Awards: 2011 NSF CAREER Award for early-career excellence in research. Her recent work includes applying deep learning to microbiome data and developing interactive systems that bridge theory with real-world applications in biology and materials science. She mentors students across all academic levels and emphasizes the importance of collaborative, real-world problem-solving in her research lab.