جست‌وجوی استاد و پژوهشگر

• Algebraic Geometry
• Topology
• Representation Theory
• +۷ مورد دیگر

Prof. Balázs Szendroi is a University Professor in Algebraic Geometry at the University of Vienna's Faculty of Mathematics, Department of Mathematics. His research focuses on algebraic geometry, topology, representation theory, and combinatorics, with significant contributions to Calabi-Yau manifolds, Hilbert schemes, and geometric structures. He organizes international workshops including the Austria-Uganda collaboration series and leads projects to develop mathematics in East Africa. Affiliations: University of Vienna (current), former roles at University of Oxford and other institutions Teaching includes advanced courses on algebraic geometry, commutative algebra, and group theory Active in academic outreach: coordinating the Vienna African Mathematics Scholarships and mentoring over 40 students/postdocs Key collaborations include projects with institutions in Uganda, Kenya, Rwanda, and South Africa Research interests span algebraic geometry's pure and applied facets, including symmetries in Islamic art, representation theory, and geometric invariant theory. He co-authored the textbook Geometry and Topology with Miles Reid.

• Robotics
• Computational Geometry
• Topology in Robotics
• +۳ مورد دیگر

Anastasiia Varava is an affiliated faculty member and PhD student at KTH Royal Institute of Technology, working within the Division of Robotics, Perception and Learning. Her research focuses on computational geometry and topology applied to robotic manipulation, particularly caging theory and its practical implementations for handling rigid and deformable objects. She collaborates with advisors Florian Pokorny and Danica Kragic on theoretical and applied robotics challenges. Her work integrates robotics with interdisciplinary fields such as topology, machine learning, and computer vision. Key topics include path planning, deformable object manipulation, and geometric algorithms for motion verification. Recent contributions emphasize data-driven approaches for latent space modeling and visual action planning in robotics systems. Varava has authored or co-authored over 30 peer-reviewed articles and conference papers, covering topics from caging-based motion planning to molecular screening algorithms inspired by robotics. Her publications span journals like IEEE Transactions on Robotics and conferences such as IROS and ICRA, reflecting a strong focus on practical robotic systems with theoretical rigor. Her doctoral thesis, Path-Connectivity of the Free Space: Caging and Path Existence (2019), established foundational work on caging and path non-existence in robotics. Current projects involve advancing geometric evaluation techniques for data representations and exploring diffusion models for robotic skill learning.

Daniele Zambonمشاهده پروفایل

پژوهشگر

• Graph Representation Learning
• Machine Learning
• Learning in Non-Stationary Environments
• +۷ مورد دیگر

Daniele Zambon is a postdoctoral researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA), affiliated with Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Faculty of Computer Science and the Graph Machine Learning Group, as well as the IEEE Task Force on Learning for Graphs. PhD : Informatics, Università della Svizzera italiana (USI), 2022 Master’s & Bachelor’s : Mathematics, University of Milan, Italy Visiting Researcher : University of Florida, University of Exeter Internship : STMicroelectronics, Italy His research lies at the intersection of machine learning and graph-structured data, with a strong emphasis on graph representation learning , learning in non-stationary environments , and time series analysis . He explores how to model dynamic graphs, detect anomalies and changes over time, and develop deep learning methods for spatiotemporal forecasting. His work integrates statistical testing, geometric deep learning, and neural architectures like Graph Neural Networks (GNNs) and Neural ODEs. The recent publications highlight a clear trend toward temporal and dynamic graph modeling , especially for time series forecasting and irregularly sampled data . There is a growing focus on generative and foundation models for graphs , uncertainty-aware learning , and the creation of benchmark datasets like PeakWeather. His work bridges theoretical contributions (e.g., statistical tests, Kalman filters on graphs) with practical applications in sensing, environmental modeling, and system monitoring. Co-author of patent: Method for the Detecting Electrocardiogram Anomalies and Corresponding System (US10610162B2) PhD thesis featured in D22 Excellent Computer Science Dissertations (2022) Associate Editor, IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS) Organizer of tutorials and special sessions at ICML, LoG, KDD, and ESANN Daniele actively contributes to the academic community through advising and teaching at USI’s Bachelor’s and Master’s programs. He has co-supervised research projects and co-organized educational initiatives such as tutorials on graph deep learning. His collaborative work involves grants and partnerships with institutions like MeteoSwiss, leading to impactful datasets and applied research. He is deeply involved in building research capacity through workshops and community engagement in the graph learning field. He is a core member of the Graph Machine Learning Group at IDSIA and contributes to the IEEE Task Force on Learning for Graphs , fostering international collaboration and setting research agendas in the domain of graph-based AI.

Federica Bacciniمشاهده پروفایل

استادیار

• Analysis of Multilayer and Higher-Order Networks
• Graph-Structured Data
• Machine Learning for Graphs
• +۱۰ مورد دیگر

Federica Baccini is an Assistant Professor at the Department of Computer, Control, and Management Engineering "Antonio Ruberti", Sapienza University of Rome. She is actively involved in the RSTLess research group, collaborating with Professors Fabrizio Silvestri and Irene Amerini. Her academic journey includes a PhD in Computer Science from the University of Pisa and a Master’s in Applied Mathematics from the University of Siena. Her educational background includes: PhD in Computer Science, University of Pisa, thesis: Analysis of Multiple Relations in Multilayer and Higher-Order Networks , supervised by Prof. Monica Bianchini and Dr. Filippo Geraci. Master’s Degree in Applied Mathematics, University of Siena, summa cum laude , thesis: Network analysis for the Integration of histone modification data to explain haematopoiesis . Visiting PhD student at Queen Mary University of London under Prof. Ginestra Bianconi. Federica's research is centered on the analysis of graph-structured data, particularly focusing on multilayer and higher-order networks. She investigates machine learning models for graphs, with emphasis on similarity network fusion , weighted simplicial complexes , and data integration across disciplines. Her work spans applications in biomedicine, environmental science, scientometrics, and AI security. She has published in high-impact journals such as Physical Review E , Journal of Informetrics , and Mathematics . The recent trend in her publications highlights a strong interdisciplinary approach, combining network science with machine learning to solve real-world problems in healthcare, scholarly communication, and climate systems. Her 2025 work on adversarial data poisoning shows engagement with AI security, while her medical applications (e.g., haemodialysis app, IgA nephropathy) demonstrate translational research impact. She has no listed scientific awards at this time. Federica is actively involved in academic advising and teaching, offering the course Fundamentals of Artificial Intelligence in the Bachelor's program in Mathematical Sciences for Artificial Intelligence. While specific grant details are not mentioned, her ongoing research in AI, network science, and interdisciplinary applications suggests active participation in funded projects. She is a member of the Theory of Deep Learning initiative. She is a key contributor to the RSTLess research group , where she collaborates on cutting-edge topics in deep learning and network analysis.

Henry Hanمشاهده پروفایل

استاد

• Data Science
• Fintech
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Henry Han is a Professor and holds the McCollum Family Chair in Data Science at Baylor University’s Hankamer School of Business, Department of Data Science. His interdisciplinary research spans data science, fintech, artificial intelligence, bioinformatics, health informatics, cybersecurity, and quantum computing. PhD, Applied Mathematical & Computational Sciences, University of Iowa (2004) MS, Computer Science, University of Iowa (2001) MS, Applied Mathematics, University of Iowa (2001) Dr. Han’s research integrates advanced machine learning, optimization, and data analytics techniques to solve complex problems in finance, healthcare, and cybersecurity. His work emphasizes manifold learning , deep neural networks , evolutionary computation , and graph-based learning for real-world applications such as high-frequency trading, automobile damage classification, and single-cell genomics. He applies these methods across domains including financial modeling, biomedical data analysis, and sports forecasting. His recent publications demonstrate a strong trend toward interdisciplinary machine learning , with applications in computational finance, bioinformatics, and intelligent systems. Many of his works utilize optimization-based deep learning and unsupervised representation learning to extract meaningful patterns from high-dimensional data. Dr. Han has received recognition through prestigious appointments: McCollum Family Chair in Data Science He actively collaborates on research projects and has secured funding for advanced data science initiatives, though specific grant details are not listed. His work supports both academic advancement and practical innovation in data-driven decision-making. He advises graduate students and contributes to the development of next-generation data science methodologies. Dr. Han is a key member of the data science research team at the Hankamer School of Business, contributing to interdisciplinary labs and research groups focused on fintech, AI, and health informatics.

Maximilian Wiesmannمشاهده پروفایل

پژوهشگر ارشد

• Algebraic Geometry
• Computational Geometry
• Combinatorics
• +۴ مورد دیگر

Maximilian Wiesmann is a mathematician working as an ELBE Postdoctoral Fellow at the Center for Systems Biology Dresden, Germany. Previously, he completed his PhD at the Max-Planck-Institute for Mathematics in the Sciences in Leipzig under the supervision of Eliana Duarte and Bernd Sturmfels. Mentored by Aida Maraj and Ivo Sbalzarini at CSBD, his work bridges pure mathematics with applications in computational biology and quantum information. His educational background includes: PhD in Mathematics, Max-Planck-Institute for Mathematics in the Sciences, Leipzig (2025) MASt in Pure Mathematics, University of Cambridge (2022) B.Sc. in Mathematics, University of Bonn (2021) Wiesmann's research focuses on the intersection of algebraic geometry and computational mathematics, particularly in nonlinear algebra applications. His work spans theoretical investigations in toric geometry and combinatorics to practical implementations in quantum information and machine learning. He has developed significant software tools including the EulerStratifications.jl Julia package, which accompanies his research on hypersurface families. His publications demonstrate a consistent trajectory from foundational mathematical structures to their applications in statistical modeling and computational science. Analysis of his 11 most recent publications reveals a strong focus on algebraic statistics and geometric approaches to statistical problems, with increasing attention to machine learning applications. His work connects pure mathematical structures with practical computational frameworks, particularly through the lens of nonlinear algebra. The development of specialized software like EulerStratifications.jl demonstrates his commitment to making theoretical advances accessible for practical computation. Wiesmann has served as a tutor in linear algebra and foundational mathematics at the University of Bonn during winter terms 2019/20 and 2020/21. He also completed a Quantum Science Summer Internship at Riverlane in Cambridge, UK in 2021, and was a visiting student at the Institute for Mathematical and Statistical Innovation at the University of Chicago in September and October 2023. At CSBD, Wiesmann is part of a vibrant research environment focused on systems biology, where he applies his expertise in algebraic geometry to biological modeling problems. His work with the Nonlinear Algebra Community on Zenodo demonstrates his commitment to open science and community building in his field.

Georg Pölzlbauerمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Visualization
• Artificial Intelligence
• +۳ مورد دیگر

Georg Pölzlbauer is a researcher affiliated with the Department of Information and Software Engineering at Vienna University of Technology (TU Wien). His work focuses on machine learning, data visualization, and artificial intelligence, particularly leveraging self-organizing maps (SOM) for exploratory data analysis. He holds a Dipl.-Ing. (Diploma in Engineering) and a Dr.techn. (Doctor of Technical Sciences). His research emphasizes advanced visualization techniques for SOMs, including vector fields and graph-based methods, applied to diverse domains like petroleum data and political datasets. He has contributed to supervised learning algorithms inspired by self-organization, such as Decision Manifolds. Publications span algorithm design, cluster analysis, and pattern recognition, with applications in music feature extraction and industry-specific data visualization. No scientific awards are explicitly mentioned, but his work reflects sustained engagement with computational and visual data analysis. While no advising or grant information is provided, his affiliation with TU Wien’s research unit suggests active participation in collaborative academic projects.

Daniel Berwick Evansمشاهده پروفایل

دانشیار

• Supersymmetric Quantum Field Theories
• Differential Geometry
• Algebraic Topology
• +۳ مورد دیگر

Daniel Berwick Evans is an Associate Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign, specializing in the intersection of theoretical physics and pure mathematics with emphasis on supersymmetric quantum field theories, differential geometry, and algebraic topology. His academic journey includes a PhD from UC Berkeley (2013) under Peter Teichner, followed by a Szego Assistant Professorship at Stanford University (2013-2015). Evans' research investigates deep connections between quantum field theory and topological structures, with specific focus on elliptic cohomology, equivariant topology, and vector bundle geometries. His work frequently employs higher categorical frameworks to resolve problems in mathematical physics, particularly exploring how supersymmetric field theories generate topological invariants through modular forms and string-theoretic constructions. Analysis of his recent publications reveals a cohesive trajectory toward unifying discrete and continuous geometric methods, with significant contributions to discrete exterior calculus, infinity-sheaf classifications, and principal 2-group bundles. These works collectively advance the mathematical foundations of topological quantum field theories and their applications to string theory. His major recognition includes: NSF CAREER Award (2024) As an NSF CAREER awardee, Evans leads a federally funded research program integrating graduate mentorship with collaborative investigations into quantum symmetries and topological invariants, positioning his work at the forefront of mathematical physics.

Vanessa Robinsمشاهده پروفایل

دانشیار

• Computational Topology
• Geometry and Topology
• Materials Science
• +۳ مورد دیگر

Vanessa Robins is an Associate Professor in the Department of Fundamental & Theoretical Physics at the Australian National University (ANU). She holds editorial roles in the Journal of Applied and Computational Topology . Her research focuses on computational topology and geometry, with applications to scientific problems such as material science and fluid dynamics. She has supervised multiple research students and contributed to projects funded by ANU and international collaborations. Education: PhD from the University of Colorado at Boulder (2000). Research interests include persistent homology, minimal surfaces, symmetry detection, and topological data analysis applied to porous materials and magnetic fields. Her work bridges pure mathematics with practical applications in engineering and physics. Over 1475 citations highlight the impact of her research, with notable contributions to algorithms for image analysis and material characterization. Key projects include developing algorithms for topological image processing and studying the elastic properties of porous materials. She has led or co-led six funded projects, including initiatives on digital rock physics and tomographic imaging techniques. Labs and collaborations involve ANU's computational topology group and international partnerships in materials science. Her work integrates advanced computational methods with experimental data from X-ray tomography and fluid dynamics experiments.

Janice Scealyمشاهده پروفایل

دانشیار

• Compositional Data Analysis
• Directional Statistics
• Robust Statistics
• +۵ مورد دیگر

Janice Scealy is an Associate Professor in the Research School of Finance, Actuarial Studies & Statistics at The Australian National University (ANU). She holds a PhD in Statistics from ANU (2011) and a Bachelor of Mathematics (Honours) from the University of Wollongong (2003). Her research focuses on compositional data analysis, directional statistics, robust statistics, and applications in geosciences like palaeomagnetism and seismology. She has held roles including ARC DECRA Fellow (2018-2022) and has supervised multiple research projects. Notable contributions include advancements in hypersphere-based statistical models and robust estimation techniques. Education: PhD (ANU 2011), BMath (Wollongong 2003) Her research interests include statistical methods for manifold-valued data, geostatistics, and model selection in linear mixed models. Recent work applies elliptical distributions to seismic moment tensor classification and integrates Fisher's palaeomagnetic theories with modern statistics. She leads projects on non-Euclidean data analysis and biologics immunogenicity assessment. Key collaborations involve geophysical applications and statistical methodologies for spatial data. Awards include ANU research grants (2018–2025) and contributions to international statistical reviews.

Sigrid Elschotمشاهده پروفایل

دانشیار

• Space Environment and Satellite Systems
• Space Weather Modeling
• Plasma Physics
• +۳ مورد دیگر

Sigrid Elschot is an Associate Professor of Aeronautics and Astronautics at Stanford University, leading the Space Environment and Satellite Systems laboratory. Her research focuses on space weather, plasma interactions, and hypervelocity impact effects on spacecraft. She holds prestigious awards such as the PECASE and NSF/DoE CAREER awards. She teaches courses like Plasma Physics and Space Environment Analysis. Education & Affiliations: Stanford University faculty since at least 2011, affiliated with the School of Engineering. Research Interests: Space Situational Awareness (SSA), plasma dynamics in space environments, ground-to-satellite communication systems, and hypervelocity impact physics. Her lab uses dust accelerators and light-gas guns to study spacecraft impacts, alongside Particle-In-Cell simulations. Recent work includes analyzing electromagnetic pulses from meteoroid impacts and radar characterization of space debris. Over 65 publications span plasma physics, astrodynamics, and space engineering. Grants & Funding: DoE, NSF, and Hellman Fellowships support her work. Professional Roles: Member of URSI Commission G, AMISR Advisory Panel, and DOE Fusion Energy Sciences advisory roles. Her team advises over 20 doctoral and master’s students, with active projects on plasma charging, soliton dynamics, and meteoroid orbital analysis. Current courses include AA242A (Plasma Physics) and AA244A (Space Environment).

Wilderich Tuschmannمشاهده پروفایل

استاد

• Differential Geometry
• Riemannian Metrics
• Moduli Spaces
• +۴ مورد دیگر

Prof. Dr. Wilderich Tuschmann is a full professor at the Institute for Algebra and Geometry within the Faculty of Mathematics at the Karlsruher Institut für Technologie (KIT) . He leads the Differential Geometry Research Group and actively contributes to geometric analysis, moduli spaces of Riemannian metrics, and applications of geometry to data science and medical technology. Research Focus: Differential geometry, geometric topology, curvature analysis, and interdisciplinary applications in medical shape sensing and epidemic modeling. Key Collaborations: DFG Research Training Group 2229, HeKKSaGOn strategic partnerships, China Institute for Anthropomatics and Robotics. Recent Publications (2025-2024): Explored cohomology-based similarity metrics, moduli spaces with curvature bounds, geometric machine learning frameworks, and non-negatively curved metrics on manifolds. Conferences: Organized workshops in Oberwolfach, Kyoto/Sendai, Karlsruhe, and Sanya, focusing on metric geometry and data science intersections. Teaching: Delivers advanced mathematics lectures and differential geometry seminars at KIT.

Charles Francesمشاهده پروفایل

استاد

• Conformal Geometry
• Pseudo-Riemannian Geometry
• Lorentzian Geometry
• +۴ مورد دیگر

Charles Frances is a Professor of Mathematics at the University of Strasbourg , affiliated with the IRMA (Institute of Research in Mathematics) . His research focuses on Conformal Geometry , Pseudo-Riemannian Geometry , and Dynamical Systems . His work explores conformal transformations, isometry groups, and geometric rigidity in Lorentzian and pseudo-Riemannian manifolds. Research Trends : His publications (2002-2021) address topics like Liouville's theorem , Ferrand-Obata conjectures , Lorentzian Kleinian groups , and automorphism groups of Cartan geometries . Key subfields include conformal dynamics , geometric group actions , and normal forms for conformal fields . Education : He defended a thesis in Conformal Lorentzian Geometry and obtained authorization to supervise research from the University of Strasbourg. Conference Contributions : He presented work on anti-de Sitter space-times , essential conformal structures , and Lorentzian 3-manifolds .

• Artificial Neural Networks
• Machine Learning
• Self-Organized Learning
• +۷ مورد دیگر

Erzsébet Merényi is a Research Professor at Rice University with dual appointments in the Department of Statistics and the Department of Electrical and Computer Engineering. She is also affiliated with the Rice Space Institute as a Faculty Affiliate. Her academic career spans several decades with significant contributions to neural computation and hyperspectral image analysis. Dr. Merényi earned her M.Sc. in Mathematics and Ph.D. in Computational Science from Szeged (Attila József) University in Hungary. Her educational background provided the foundation for her interdisciplinary research that bridges mathematics, computer science, and planetary science. Her research focuses on neural computation, machine learning, and self-organized learning techniques applied to high-dimensional data analysis. She has developed methods for structure discovery in complex data, segmentation and classification, pattern recognition, parameter inference, variable selection, data fusion, and knowledge integration. Her work has particular emphasis on applying these techniques to multispectral and hyperspectral image analysis for planetary surface composition determination, environmental resource mapping, and medical image analysis. Analysis of her publication record reveals a consistent focus on self-organizing neural networks for spectral image analysis, with applications spanning planetary science (Mars, Moon, asteroids, and comets), Earth observation, and medical applications. Her work demonstrates a progression from theoretical neural network development to practical applications in remote sensing, with particular expertise in handling high-dimensional spectral data. NASA Applied Information Systems Research Program (AISRP) funding 2000-2004 NASA Applied Information Systems Research Program (AISRP) funding 2004-2009 NASA Applied Information Systems Research Program (AISRP) funding 1997-2000 Dr. Merényi has advised multiple graduate students including Kadim Tasdemir, Captain Michael Mendenhall, and Abha Jain. Her research has been supported by NASA grants through the Applied Information Systems Research Program, focusing on precision mining of spectral data for planetary resource identification. She has collaborated with researchers from institutions including the University of Arizona's Lunar and Planetary Laboratory, Paisley University in Scotland, and the University of Leipzig. Her work has involved developing software tools for neural network analysis of spectral data, with applications to Mars surface composition mapping, lunar geology studies using Clementine data, analysis of Halley's Comet images, and Earth resource mapping. She has created specialized neural network approaches for handling the high dimensionality of hyperspectral data while maintaining computational efficiency.

Jia Liuمشاهده پروفایل

استاد

• Computational Mathematics
• Numerical Linear Algebra
• Sparse Matrix Computations
• +۹ مورد دیگر

Dr. Jia Liu is a Professor and Chair of the Department of Mathematics and Statistics at the University of West Florida, within the Hal Marcus College of Science and Engineering. She has been a key academic figure at UWF since 2006, leading both research and departmental initiatives in computational and applied mathematics. Her educational background includes a Ph.D. in Mathematics from Emory University, funded by the National Science Foundation, focusing on preconditioned Krylov subspace methods for incompressible flow problems. She earned her M.S. and B.A. in Mathematics from Central China Normal University, where her bachelor's thesis received the highest honor. Dr. Liu's research lies at the intersection of numerical linear algebra, scientific computing, and interdisciplinary applications. Her primary interests include: Numerical solvers for large sparse linear systems Krylov subspace iterative methods Preconditioning techniques Applications to Navier-Stokes and optimization problems Geometric and topological analysis of ellipsoids Complex networks and community detection via spectral clustering Machine learning for disease prediction and biological modeling The trends in her publications reflect a consistent focus on robust numerical algorithms with applications across fluid dynamics, network science, and biomedical modeling. Her work emphasizes both theoretical development and practical implementation in high-performance computing environments. Dr. Liu has served as an editor and editorial board member for several peer-reviewed journals and regularly reviews submissions. While specific awards are not listed, her sustained publication record in prestigious venues such as SIAM Journal on Scientific Computing and Journal of Biological Dynamics underscores her scholarly impact. As department chair and professor, she plays a central role in academic advising, curriculum development, and research mentorship. She teaches core courses including Differential Equations, Numerical Analysis, and Real Analysis, contributing significantly to both undergraduate and graduate education. Her leadership extends to managing research grants and fostering collaborations across disciplines. Though no formal lab name is mentioned, her research activities suggest involvement with computational modeling groups, likely associated with applied mathematics and data science initiatives at UWF. Her ongoing work continues to advance numerical methods for complex systems in science and engineering.