جست‌وجوی استاد و پژوهشگر

Jaroslaw Zolaمشاهده پروفایل

دانشیار

• Scalable Computing
• High Performance Computing
• Parallel Computing
• +۴ مورد دیگر

Dr. Jaroslaw (Jaric) Zola is an Associate Professor in the Department of Computer Science and Engineering at the University at Buffalo (SUNY), where he co-directs undergraduate studies. He leads the Scalable Computing Research (SCoRe) Group, focusing on algorithms and systems for large-scale biomedical and engineering data analysis. His research emphasizes scalable computing, parallel processing, and computational biology, with applications in metagenomics, mobile DNA analysis, and manifold learning. Education: M.Sc. in Computer Science from Czestochowa University of Technology (2001), Ph.D. from Grenoble Institute of Technology (2005). Before joining UB in 2014, he worked at Rutgers Discovery Informatics Institute and Iowa State University. Key research areas include scalable computing , parallel algorithms , computational biology , and data-driven scientific computing . His work bridges high-performance computing with real-world biomedical challenges, such as metagenomic classification and mobile DNA analysis. Notable awards include the DASH-IF excellence in DASH award (3rd place, 2022) and best paper award finalist recognitions. He has contributed to open-source tools like Faster2 for bioinformatics and the SCoRe Group’s projects. Advising and grants: Actively mentors students in systems programming and bioinformatics. Leads projects funded by institutions like City Football Services and the National Science Foundation. His lab develops tools for distributed computing and mobile DNA analysis, including the Coriolis system. Labs/Teams: SCoRe Group, which emphasizes interdisciplinary collaboration between computer science and biomedical research.

• Computational Geometry
• Discrete Geometry
• Convex Optimization
• +۶ مورد دیگر

Francisco Criado is a researcher with significant contributions to computational geometry, discrete mathematics, and applied algorithm design. His work spans theoretical and applied domains, including convex optimization, Voronoi diagrams, and geometric modeling in gas dynamics and earthquake decision-making frameworks. Collaborators : Francisco Santos, Sebastian Pokutta, Michael Joswig Key Venues : Discrete & Computational Geometry , Foundations of Computational Mathematics , NeurIPS Research Interests focus on geometric algorithms, polyhedral complexes, and applications of fuzzy logic. His 2025 papers explore convex hulls, zonotopes, and tropical geometry, while earlier works address gas dynamics and decision-making models. Article Trends reveal expertise in high-dimensional geometry, combinatorial optimization, and numerical methods. He frequently employs randomized and linear convergence algorithms in 2022–2025. Advising and Grants : No explicit data provided, but his extensive co-authorship network suggests collaborative research leadership.

• 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.

Baltasar Enrique Beferull Lozanoمشاهده پروفایل

استاد

• Data Science
• Machine Learning
• Signal Processing
• +۳ مورد دیگر

Baltasar Enrique Beferull Lozano is a tenured Professor at the University of Agder , leading the Center Intelligent Signal Processing and Wireless Networks (WISENET) since 2015. With a PhD in Electrical Engineering from USC (2002) and prior roles at EPFL, AT&T Shannon Labs, and University of Valencia, his career spans 20+ years of academic and industrial research in signal processing, wireless systems, and AI. Education: PhD (USC), MSc (USC), MSc (University of Valencia) Expertise: Data Science, Machine Learning, Graph Signal Processing, Cyber-Physical Systems His research focuses on AI-driven wireless networks and in-network collective intelligence , addressing fundamental and applied challenges in smart water systems , energy management , and next-gen 5G/6G . He has secured 20+ international projects including 10 EU-funded initiatives (HYDROBIONETS, SENDORA) and 5 RCN-funded projects. Recent publications emphasize dynamic graph learning from time series data, quantized graph filters , and multi-agent reinforcement learning for networked environments. Awards include IEEE Best Paper Awards (2012, 2021), TOPPFORSK Grant (2015), and Ramón y Cajal Program Rank #1 (2005). As a Senior IEEE Member , he serves as Area Editor for IEEE Transactions on Signal Processing and evaluates research proposals for the European Commission , NSF , and Qatar National Research Fund . His lab has produced 15 PhD graduates and collaborates with 12+ industry partners including Telenor, IBM, and SINTEF.

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.

Jinchi Lvمشاهده پروفایل

استاد

• Statistics
• Data Science
• Artificial Intelligence
• +۴ مورد دیگر

Jinchi Lv is the Department Chair and Professor in the Data Sciences and Operations Department at the Marshall School of Business , University of Southern California , with a joint appointment in the Department of Mathematics at USC. He earned his Ph.D. in Mathematics from Princeton University in 2007. Specializes in Statistics , Data Science , and Artificial Intelligence . Focuses on Large Language Models (LLMs) , High-Dimensional Statistics , and Blockchain applications. His research spans interdisciplinary domains, including Computer Science , Economics , and Bioinformatics . Recent work emphasizes Network Analysis , Statistical Inference , and Machine Learning in diverging dimensions. Dr. Lv has received prestigious honors such as Fellow of the Asia-Pacific Artificial Intelligence Association (2024) , NSF Grants (2023, 2020, 2008), and the Royal Statistical Society Guy Medal in Bronze (2015) . He has advised over 20 Ph.D. students and Postdoctoral Scholars, many of whom have secured academic or industry leadership roles. Currently, he leads the USC Marshall Stats Group and contributes to editorial boards of top journals like Operations Research and JASA . His teaching includes courses on Deep Learning and High-Dimensional Statistics .

Li Chenمشاهده پروفایل

استاد

• Discrete Geometry
• Digital Geometry
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Li Chen is a Professor in the Department of Computer Science and Information Technology at the University of the District of Columbia (UDC), affiliated with the School of Engineering and Applied Sciences. He specializes in discrete geometry, digital geometry, AI, and data science. Previously held the ACM Distinguished Speaker title (2015-2021). His research focuses on algorithm design, topological data analysis, and quantum computing education. Education: Ph.D. in Computer Science, University of Bedfordshire, UK M.Sc. in Computer Science, Utah State University B.Sc. in Computer Science, Wuhan University, China Research Interests: Dr. Chen’s work spans discrete geometry (e.g., digital surfaces, topological invariants), AI applications, data science methodologies, and algorithmic solutions for geometric problems. His contributions include foundational texts on mathematical problems in data science and innovations in quantum computing education. Publications Trends: Recent work emphasizes algorithmic approaches to topological challenges, AI limitations (e.g., image segmentation), and quantum computing pedagogy. Earlier research includes digital geometry surveys and medical imaging applications. Awards: ACM Distinguished Speaker (2015-2021) Teaching & Grants: Teaches Advanced Machine Learning, Introduction to Quantum Computing, and Algorithm Design. His grants and lab activities are not explicitly listed but implied through his extensive publication record. Labs/Teams: No specific lab affiliations mentioned, but collaborations on digital geometry and data science projects are evident from his work.

• Mathematics
• Number Theory
• Analysis
• +۴ مورد دیگر

Dr. Joshua Friedman is a Professor in the Department of Math and Science at the United States Merchant Marine Academy (USMMA) since 2005. His research focuses on advanced mathematical analysis including Selberg trace formulas, automorphic forms, zeta functions, and Fuchsian/Kleinian groups. He has contributed to areas such as analytic number theory, spectral geometry, and combinatorial optimization through his publications. Teaching interests span all mathematics courses. Education: PhD and MA in Mathematics (Stony Brook University), BS in Math & Physics (Binghamton University) His work bridges pure mathematics with applied problems like automated timetabling using integer programming. Recent research emphasizes zeta functions, regularized determinants, and scattering theory applications. Notable contributions include studies on Bessel function integrals linked to Mahler measure (2022), deep learning approaches to graph theory problems (2021), and effective bounds in modular form analysis (2019). His 2016 papers address scattering determinants and hyperbolic manifold applications.

Konstantin Zuevمشاهده پروفایل

استاد آموزشی

• Statistical Data Analysis
• Network Data Analysis
• Machine Learning
• +۷ مورد دیگر

Konstantin Zuev is a Teaching Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. He holds dual Ph.D.s in Mathematics (Lomonosov Moscow State University) and Civil Engineering (Hong Kong University of Science & Technology). Zuev is also the Undergraduate Option Representative for Information and Data Sciences and Graduate Minor Advisor for the same field. His research focuses on statistical data analysis, network science, machine learning, and computational methods for complex systems. His teaching includes courses like Applied Linear Algebra, Probability Models, and Statistical Inference. Zuev has received multiple prestigious awards, including the 2023 ASCIT and GSC Teaching Awards, the 2021 Humboldt Research Fellowship, and the 2019 Northrop Grumman Prize for Excellence in Teaching. His work spans interdisciplinary topics such as financial market dynamics, epidemiological modeling, and academic curriculum analysis through network theory. Zuev’s research has been published in journals like *Proceedings of the National Academy of Sciences*, *Scientific Reports*, and *Physical Review E*. He is actively involved in consulting projects with organizations like Virtualitics, Inc., focusing on applications in healthcare and network analytics. Beyond academia, he advises student groups like the Caltech Chess Club and Karate Club, reflecting his passion for fostering community engagement and mentorship.

• Artificial Intelligence (AI)
• Machine Learning (ML)
• Algorithms and Computational Complexity
• +۵ مورد دیگر

Abhishek K. Umrawal is a Teaching Assistant Professor at the University of Illinois Urbana-Champaign, affiliated with the Department of Electrical and Computer Engineering and the Coordinated Science Lab. He holds a Ph.D. in Industrial Engineering from Purdue University, an MS in Economics, and an MS in Statistics from IIT Kanpur. His research focuses on AI/ML fundamentals (Generative AI, Causal AI, Reinforcement Learning), algorithmic theory (Combinatorial Optimization, Game Theory), and applications in social networks, transportation systems, and conversational AI. Umrawal teaches core courses like ECE 313 (Probability), ECE 120 (Intro to Computing), and CS/ECE 374 (Algorithms). He has published 10+ peer-reviewed articles in top venues (IEEE TETCI, ACM TDS, AAAI), focusing on influence maximization, reinforcement learning, and causal graph applications. His work bridges theoretical foundations with real-world problems in transportation and social networks. Recognitions include the Bilsland Dissertation Fellowship (2022), multiple mentorship awards, and research travel grants. He actively reviews for NeurIPS, ICML, and journals like IEEE T-ITS. Service roles include Scholarship Committees in Grainger College of Engineering and leadership in ACM/IEEE societies.

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.

• Differential Geometry
• Integral Geometry
• Riemannian Geometry
• +۳ مورد دیگر

Ana Dorotea Tarrío Tobar is a Professor in the Department of Mathematics at Universidade da Coruña (UDC) in Spain, specializing in Applied Mathematics. She is an active member of the Differential Geometry and Its Applications research group, with a strong publication record spanning over three decades. Her research focuses on Integral Geometry, Riemann Geometry, Contact Detection, and Applications of Geometry in Engineering. Key research areas include Extended Bianchi-Cartan-Vranceanu spaces, Curvature analysis, Geodesics, Contact detection, Quadrics, and graph bases. Her work bridges theoretical geometry with practical engineering applications. Dr. Tarrío Tobar teaches Mathematics I and II for the Degree in Technical Architecture, and Mathematics 1 for the Concurrent program of the Degree in Biology and Chemistry. Her recent publications (2022-2016) show a consistent output in prestigious mathematics journals, with increasing focus on computational geometry and educational applications. Her collaborative work spans multiple institutions across Spain and internationally, with frequent co-authorship with researchers like María J. Souto-Salorio, M. Brozos-Vázquez, and María J. Pereira-Sáez. While she has not directed bachelor's or master's theses since 2013, her research projects have been funded by various Spanish institutions including the Ministry of Culture, Education and University Planning, Xunta de Galicia, and the Ministry of Economy and Competitiveness.

• Geometric Deep Learning
• Machine Learning
• Data Science
• +۱۱ مورد دیگر

Michael A. Perlmutter is an Assistant Professor in the Department of Mathematics at Boise State University and an affiliate faculty member in the Computing Ph.D. program. His research bridges theoretical and applied aspects of machine learning, data science, and inverse problems, with interdisciplinary applications in biomedicine and environmental systems. Research Interests: His work primarily focuses on Geometric Deep Learning , particularly neural network models for non-Euclidean data such as graphs and manifolds. This includes theoretical development of the geometric scattering transform and designing deep learning architectures for signed and directed graphs. He also investigates Phase Retrieval in ptychographic imaging, emphasizing robust, efficient algorithms with provable guarantees. His applied interests span AI-aided drug discovery, analysis of metabolic networks, single-cell data prediction, tensor compression, audio denoising, and data science for ecological and hydrological systems. Scientific Awards: Advising and Grants: While specific students and grants are not listed, his active research program and Ph.D. affiliation suggest involvement in mentoring graduate students and pursuit of external funding, particularly in interdisciplinary data science and machine learning. His career trajectory includes multiple competitive postdoctoral appointments, indicating a strong research profile. Labs and Teams: Though not explicitly named, his work implies collaboration with interdisciplinary teams in computational mathematics, biomedical data science, and environmental modeling, possibly within or affiliated with Boise State's data science and computing initiatives.