جست‌وجوی استاد و پژوهشگر

Laura Balzanoمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Signal Processing
• +۷ مورد دیگر

Laura Balzano is an Associate Professor of Electrical Engineering and Computer Science at the University of Michigan, with a courtesy appointment in the Department of Statistics within the College of Literature, Science, and the Arts. Her research focuses on optimization, machine learning, and signal processing, particularly in high-dimensional data analysis, subspace learning, and low-rank structures. She holds affiliations with the Department of Statistics and is located at 323 West Hall, Ann Arbor, MI. Contact information includes girasole@umich.edu . Her work bridges theoretical foundations and practical applications, addressing challenges in heterogeneous data, online learning, and robust algorithms. Key research areas include subspace clustering, tensor decomposition, and model compression for deep learning systems. Recent contributions explore dynamic networks, control theory integration, and energy-efficient algorithms for large-scale models. Laura Balzano's publications span topics like PCA variants for noisy data, optimization on Riemannian manifolds, and neural collapse analysis. Her methods emphasize scalable solutions for real-time applications, such as energy disaggregation and medical imaging. While no explicit awards are listed, her extensive publication record reflects recognition in her field. Advising and grant details are not provided in the available text.

Moody T. Chuمشاهده پروفایل

استاد

• Numerical Ordinary Differential Equations
• Numerical Linear Algebra
• Dynamical Systems
• +۵ مورد دیگر

Moody T. Chu is a Professor in the Department of Mathematics at North Carolina State University since 1982, holding a PhD from Michigan State University. His research focuses on numerical linear algebra, dynamical systems, inverse problems, and quantum computing. He has received prestigious teaching awards including the Alumni Distinguished Undergraduate Professorship (2006) and multiple Board of Governors Awards (2010, 2013, 2014). His work bridges computational mathematics with applications in physics, engineering, and data science. Research interests include numerical methods for differential equations, tensor approximation, and quantum simulation. His articles explore topics like Cartan decomposition for quantum Hamiltonians, Lax dynamics, and low-rank tensor approximations. Over 200 publications span numerical analysis, inverse eigenvalue problems, and optimization techniques. Chu’s contributions also address algorithm design for matrix completion and structured low-rank approximations. He has advised numerous graduate students (details not listed here) and led projects on adaptive optics and stochastic processes. His work on nonnegative matrix factorization and Markov chain dynamics has influenced data mining and machine learning applications. Chu’s lab focuses on advancing computational frameworks for complex systems, emphasizing interdisciplinary collaboration.

Anna Seigalمشاهده پروفایل

استادیار

• Applied Algebraic Geometry
• Algebraic Statistics
• Tensors and Multilinear Algebra
• +۵ مورد دیگر

Anna Seigal is an Assistant Professor of Applied Mathematics at Harvard University's School of Engineering and Applied Sciences (SEAS), with an affiliation in the Department of Statistics. Her research focuses on applied algebraic geometry, tensors, multilinear algebra, and algebraic statistics, particularly in the context of data science. She explores algebraic approaches to data analysis, including matrix/tensor factorizations, parameter estimation, causal inference, and optimization, with applications to physical and biological systems. Her work is supported by the Sloan Foundation and Harvard's Dean’s Competitive Fund. Research Interests: Algebraic statistics, tensors and multilinear algebra, applied algebraic geometry, and the mathematics of data science. She investigates group symmetries in models, dimensionality reduction techniques, and machine learning algorithms. Current projects include causal disentanglement via cumulants and invariant theory applications to maximum likelihood estimation. Her academic contributions span theoretical advancements and interdisciplinary applications, such as genomic template analysis and COVID-19 molecular phenotyping. She collaborates with postdocs and students on research projects and teaches courses like Applied Math 210. Awards and Funding: Supported by the Alfred P. Sloan Foundation and Harvard's internal grants. No explicit named awards listed, but her research is institutionally recognized. Labs/Teams: Leads a research group focused on applied algebra and geometry in data science. Collaborates across departments in SEAS and the Statistics Department.

André Uschmajewمشاهده پروفایل

استاد

• Numerical Analysis
• Tensor Methods
• Low-Rank Approximation
• +۷ مورد دیگر

Prof. Dr. André Uschmajew is a full professor and holds the Chair of Mathematical Data Science at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences, and Materials Engineering, University of Augsburg, Germany. He has held prominent research and academic positions at institutions including the Max Planck Institute for Mathematics in the Sciences (Leipzig), University of Bonn, and EPF Lausanne. 2022–present: Chair of Mathematical Data Science, University of Augsburg 2017–2022: Research Group Leader, Max Planck Institute MiS Leipzig 2014–2017: Bonn Junior Fellow Professorship, University of Bonn 2013: Ph.D. in Mathematics, TU Berlin His research centers on the theoretical and computational aspects of low-rank tensor and matrix approximations, with deep connections to Riemannian optimization, functional analysis, and high-dimensional scientific computing. He investigates the geometry of low-rank varieties, convergence of alternating algorithms, and applications in data science and dynamical systems. His work combines rigorous mathematical analysis with algorithmic innovation. The recent publications (2023–2025) reflect a strong focus on optimization methods for low-rank structures, dynamical low-rank approximation for PDEs like the Vlasov-Poisson equation, randomized SVD, Sinkhorn-type algorithms with overrelaxation, and Kronecker product operator approximation. Key themes include convergence analysis, algorithmic acceleration, and applications in scientific computing and signal processing. Although no specific awards are listed, his publication record in top-tier journals such as Numerische Mathematik , SIAM Journal on Optimization , and Foundations of Computational Mathematics indicates significant recognition in applied mathematics and numerical analysis. He advises students and researchers in mathematical data science and numerical analysis, though specific advisees are not named. He teaches courses such as Kernel Methods and Linear Algebra II. He has collaborated with leading researchers including Bart Vandereycken, Daniel Kressner, and Wolfgang Hackbusch. His work is supported through institutional affiliations and likely research grants, though specific grants are not listed. He is actively involved in the development of numerical methods for high-dimensional problems, particularly using tensor networks and manifold optimization. He is affiliated with research teams at the University of Augsburg and previously led a group at the Max Planck Institute MiS Leipzig, focusing on mathematical aspects of data science and tensor methods.

Ada Boraleviمشاهده پروفایل

دانشیار

• Algebraic Geometry
• Homogeneous Vector Bundles
• Quiver Representations
• +۳۵ مورد دیگر

Ada Boralevi is an Associate Professor in the Department of Mathematical Sciences "G. L. Lagrange" (DISMA) at Politecnico di Torino, Italy. She is a prominent researcher in algebraic geometry, specializing in vector bundles, tensor decomposition, determinantal representations, and related algebraic structures. Her work bridges pure mathematics with applications in numerical analysis, mathematical physics, and computational algebra. PhD in Mathematics, University of Florence, 2008 MA in Mathematics, UCLA, 2006 Laurea in Mathematics, University of Florence, 2004 Her research interests lie at the intersection of algebraic geometry, commutative algebra, and multilinear algebra. She investigates homogeneous vector bundles, secant varieties, constant rank matrices, and tensor decompositions, with a focus on geometric and algebraic properties. Her work often involves moduli spaces, stability conditions, and equivariant constructions. She is particularly known for her contributions to the theory of instanton bundles and determinantal representations. The analysis of her recent publications reveals a sustained focus on algebraic structures derived from matrices and tensors. Her work spans from foundational algebraic geometry (e.g., sections of homogeneous bundles) to applied topics such as uniform determinantal representations and tensor decomposition. A recurring theme is the interplay between geometry and linear algebra, especially in the context of rank constraints and symmetry. Her collaborations with leading mathematicians highlight her active role in the international research community. She is actively involved in the mathematical community through service and leadership. She is a member of the European Women in Mathematics and the Unione Matematica Italiana. She has organized numerous conferences and summer schools, including the TAGSS series, which promotes women in mathematics. She has also served on organizing committees for MEGA 2017, Vector Bundles Days, and several workshops on tensors and algebraic geometry. Ada Boralevi mentors PhD students, including Stefano Canino, and participates in the doctoral college in Pure and Applied Mathematics at Politecnico di Torino. She has secured research contracts with institutions such as SISSA, TU Eindhoven, and IMPAN. Her teaching includes undergraduate courses in linear algebra and geometry, as well as advanced PhD-level topics on spaces of matrices and their applications. She is a key organizer of algebraic geometry activities in Turin, jointly between Politecnico di Torino and the University of Turin. She co-founded the TAGSS (Trieste Algebraic Geometry Summer School) with Valentina Beorchia and Barbara Fantechi, a series of schools taught by outstanding women mathematicians. Her leadership in these initiatives underscores her commitment to education and gender equity in mathematics.

Joung Ohمشاهده پروفایل

دانشیار

• Rock Mechanics
• Tunnelling Support Systems
• Numerical Modelling
• +۵ مورد دیگر

Associate Professor Joung Oh is affiliated with the School of Minerals and Energy Resources Engineering at the University of New South Wales (UNSW), where he teaches and conducts research in mining geomechanics , tunnelling , and rock mechanics for caving mechanisms . His expertise spans rock joint behavior, ground-structure interaction, and numerical modelling, with prior industry experience at Parsons Brinckerhoff in New York. PhD in Civil Engineering (Geotechnical) from University of Illinois at Urbana-Champaign Research focuses on borehole breakout analysis , hydromechanical coupling , and machine learning applications in stress estimation . Recent work includes nonlinear flow in rock fractures , shotcrete bonding mechanisms , and ground support systems in burst-prone areas . Publications emphasize numerical methods , rock anisotropy , and seismic event cascades in mining contexts. Scientific contributions include 15+ recent articles on topics like granite microcrack characterization , seismicity during hydraulic fracturing , and multiscale stress estimation . These works integrate acoustic emission tomography , UDEC-GBM simulations , and artificial neural networks for geotechnical analysis. Professional service involves editorial roles at International Journal of Mining Science and Technology and memberships in societies like the Society of Mining Professors and Australasian Tunnelling Society . Grants from ACARP funded projects on shotcrete effectiveness and horizontal stress estimation in coal mines.

• Matrix and Tensor Factorizations
• Data Fusion
• Machine Learning
• +۳ مورد دیگر

Carla Schenker is a Postdoctoral Fellow in the Department of Data Science and Knowledge Discovery at Simula Metropolitan, specializing in advanced tensor decomposition methods for multi-modal data analysis. Her research bridges machine learning, optimization, and neuroimaging applications, with a focus on interpretable pattern discovery from complex datasets. Her educational background includes: PhD from Oslo Metropolitan University, Norway (Thesis: A Flexible Framework for Data Fusion Based on Coupled Matrix and Tensor Factorizations for Interpretable Pattern Discovery) Dr. Schenker's research centers on Matrix and Tensor Factorizations , where she develops constrained optimization frameworks for PARAFAC2 and coupled decompositions. Her work enables Data Fusion across dynamic and static sources, with critical applications in neuroimaging biomarker discovery and temporal pattern tracking . She pioneers methods for handling incomplete temporal data while maintaining model interpretability, advancing both theoretical foundations and real-world implementations in multi-way data analysis. Analysis of her 11 publications (2019-2025) reveals a clear evolution: early work established optimization frameworks for regularized tensor factorizations (2019-2021), while recent breakthroughs (2023-2025) focus on temporal dynamics, interpretable evolving patterns, and constrained PARAFAC2 variants. Her research consistently bridges Machine Learning theory with applications in neuroscience and signal processing, demonstrating increasing sophistication in handling heterogeneous, multi-modal datasets. No scientific awards are documented in available sources. Public records indicate no formal student advising or grant leadership, though her collaborative publications involve significant interdisciplinary partnerships with institutions like Oslo Metropolitan University and international research teams. As a core member of Simula Metropolitan's Data Science and Knowledge Discovery department, she contributes to Norway's national research infrastructure for digital engineering, working within teams focused on algorithmic innovation for complex data challenges in healthcare and industrial applications.

• Signal Processing
• Machine Learning
• Optimization
• +۷ مورد دیگر

Hiroyuki Kasai is a Full Professor at the School of Fundamental Science and Engineering, Waseda University, where he leads research in signal processing, machine learning, and optimization. He holds a B.Eng. (1996), M.Eng. (1998), and Dr.Eng. (2000) in Electronics, Information, and Communication Engineering from Waseda University. His career includes positions as Associate Professor and Professor at the University of Electro-Communications (2007-2019), Senior Policy Researcher at Japan's Cabinet Office (2011-2013), and visiting roles at Technical University of Munich and British Telecom. His research spans: Fundamental methodologies : Riemannian optimization, stochastic gradient algorithms, tensor decomposition Applied domains : Network analysis, multimedia systems, environmental sound processing, video coding Emerging areas : Low-rank modeling, manifold learning, and large-scale anomaly detection His publications focus on efficient algorithms for high-dimensional data, with recent work emphasizing Riemannian manifold optimization and real-time tensor analysis. This includes development of open-source tools like SGDLibrary (MATLAB) and McTorch (PyTorch) for optimization tasks. Awards include: IEEE ICCE Best Paper Award (2011) Yamashita Memorial Award (2003) Ericsson Young Scientist Award (2001) 電気通信普及財団賞 (2015) IEICE Service Recognition Award (2010) He maintains memberships in IEEE, IEICE, IPSJ, and JSIAM, and has contributed to over 100 peer-reviewed publications with significant citation impact (h-index 27 via Google Scholar).

• Theoretical Particle Physics
• Muon g-2
• Hadronic Light-by-Light Scattering
• +۳ مورد دیگر

Antonio Rodríguez-Sánchez is a theoretical particle physicist at the Instituto de Física Corpuscular (IFIC), University of Valencia, specializing in high-precision Standard-Model tests, especially the muon anomalous magnetic moment, hadronic light-by-light scattering, tau-lepton physics, and effective field theories. After his PhD at Valencia and postdocs at SISSA Trieste and IJCLab Orsay, he returned to IFIC in 2024 as a permanent research faculty member. Research Interests: Muon g-2 and its hadronic contributions Hadronic light-by-light scattering and short-distance constraints Standard Model Effective Field Theory (SMEFT) and top-quark physics Tau-lepton decays and flavor physics CP violation and weak-basis invariants His work combines perturbative QCD, dispersion relations, and lattice QCD input to reduce theoretical uncertainties in precision observables. He has co-authored flagship reviews on the Standard-Model prediction of the muon g-2 and is active in global SMEFT analyses. Selected Scientific Output: 42 peer-reviewed articles (2020-2025) with >300 citations on INSPIRE Co-leader of short-distance constraints program for hadronic light-by-light contributions to muon g-2 Contributor to Snowmass 2021 white papers on g-2 and strong coupling extraction No graduate students, grants, or major awards are listed in the available sources; his primary role is research faculty at IFIC.

Rolando de Santiagoمشاهده پروفایل

استادیار

• Operator Algebras
• von Neumann Algebras
• Functional Analysis
• +۳ مورد دیگر

Rolando de Santiago is an Assistant Professor in the Department of Mathematics and Statistics at California State University Long Beach. Previously, he was an Assistant Professor at Purdue University (2020-2024) and a Postdoctoral Fellow at UCLA (2017-2020). His educational background includes a Ph.D. in Mathematics from the University of Iowa (2017) under Professor Ionut Chifan, an MS in Mathematics from the University of Iowa (2014), and both BS and MS degrees in Mathematics from Cal Poly Pomona (2012). Rolando's research focuses on Operator Algebras, von Neumann Algebras, Functional Analysis, Ergodic Theory, Group Theory, and Quantum Graphs. His work primarily investigates the classification of type II 1 factors arising from groups through Popa's deformation/rigidity theory. He has made significant contributions to understanding graph product von Neumann algebras, Thompson-like groups, and quantum chromatic numbers. His recent publications demonstrate a strong trend in applying operator algebra techniques to problems in quantum information theory, particularly in the area of quantum graphs and non-local games. His research connects operator algebras with geometric group theory through the study of group von Neumann algebras and their structural properties, with applications to quantum computation and information theory. UC Presidential Postdoctoral Fellowship (2018-2020) NSF Award: East Coast Operator Algebras Symposium DMS-2321632 (2024) NSF Award: Groundwork in Operator Algebras Lecture Series DMS-2247796 (2022) NSF Award: Groundwork in Operator Algebras Lecture Series DMS-2154574 (2022) NSF Award: Groundwork in Operator Algebras Lecture Series DMS-200131 (2020) MAA Project NExT Member (2020) Rolando actively mentors students including Patrick DeBonis (PhD candidate at Purdue), A. Meenakshi McNamara (undergraduate researcher), and Krishnendu Khan (Visiting Scholar). He has been deeply involved in outreach programs to increase representation of underrepresented groups in mathematics, serving as a mentor for the Math Alliance Field of Dreams Conference and the Pacific Math Alliance PUMP Research Symposium. He co-organizes the Operator Algebras Seminar at CSULB and is a co-creator and editor of Sorin Popa's W*-News Blog, which provides educational insights into notable problems in von Neumann algebras for the broader mathematical community.

Mel Chekolمشاهده پروفایل

استادیار

• Knowledge Graphs
• Artificial Intelligence
• Data Science
• +۴ مورد دیگر

Mel Chekol is an Assistant Professor at Utrecht University, affiliated with the Data Intensive Systems group within the Science faculty. He holds a PhD from INRIA Rhône-Alpes and a double MSc from Vienna University of Technology and Free University of Bozen-Bolzano. His research focuses on knowledge graphs, spatio-temporal data integration, probabilistic inference, and scalable machine learning applications. Previously, he worked at institutions including INRIA Nancy Grand Est, University of Mannheim, and the National Institute of Informatics in Tokyo. Key research interests include reasoning in knowledge graphs, temporal data modeling, and applying language models to enhance knowledge representation. He contributes to projects like the Utrecht Platform for Applied Data Science and has collaborated on frameworks such as the EXMO and WAM teams. His work emphasizes practical applications of AI and data science in governance and sustainability. Mel has published extensively in venues like VLDB Journal, ISWC, and AAAI, focusing on topics like rule learning, temporal knowledge graphs, and scalable inference systems. His research bridges theoretical advancements with real-world data challenges.

Gen Liمشاهده پروفایل

دانشیار

• Biostatistics
• Microbiome Data Analysis
• Multi-omics Data Integration
• +۴ مورد دیگر

Gen Li, PhD, is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He holds a PhD from the University of North Carolina at Chapel Hill (2015) and a BS from Beijing Normal University (2010). His research focuses on developing statistical methods for complex biomedical data, including high-dimensional data, tensor arrays, and multi-omics studies. Key interests include dimension reduction, predictive modeling, network analysis, and data integration in genomics, microbiome, and multi-omics contexts. His work has been supported by NIH grants and recognized through awards like the John G. Searle Assistant Professorship (2021). Education: PhD in Statistics, University of North Carolina at Chapel Hill, 2015 BS in Mathematical Sciences, Beijing Normal University, 2010 Research Interests: Low-rank models, tensor analysis, network analysis, longitudinal omics data, microbiome analysis, and data integration. His projects include developing methods for differential analysis of longitudinal omics data, network estimation for multi-omics, and nonlinear regression for microbiome data. Awards: John G. Searle Assistant Professorship (University of Michigan, 2021) Sigma Xi Inductee (2019) Sanford Bolton Faculty Scholar (Columbia, 2018) Calderone Junior Faculty Award (Columbia, 2016) Advising & Grants: Dr. Li’s NIH-funded research emphasizes multi-omics integration and microbiome-driven health studies. His grants support collaborations in cancer, chronic disease, and pediatric health. He advises students on statistical methods for biomedical data analysis. Labs/Teams: Active in the University of Michigan’s Biostatistics Research Group and collaborates with multi-disciplinary teams in genomics and public health.

Lars Karlssonمشاهده پروفایل

دانشیار

• High-Performance Computing
• Numerical Linear Algebra
• Parallel Algorithms
• +۲ مورد دیگر

Lars Karlsson is an Associate Professor at the Department of Computing Science, Umeå University, where he serves as Assistant Head of Department with responsibilities for undergraduate education. His research focuses on developing efficient algorithms for matrix and tensor computations within high-performance computing environments. He is affiliated with the Parallel and Scientific Computing research group at Umeå. Karlsson's primary research areas include: Numerical Linear Algebra : Specializing in matrix factorizations and eigenvalue computations Parallel Algorithms : Designing scalable solutions for distributed and shared-memory systems Tensor Computations : Developing decomposition methods and completion algorithms Performance Optimization : Auto-tuning techniques for modern computing architectures His publications (2010-2025) demonstrate consistent focus on parallel algorithms for numerical problems, particularly matrix reductions, eigenvalue computations, and tensor decompositions. Recent work emphasizes auto-tuning, robustness in numerical methods, and efficient scheduling for high-performance systems. The majority of publications involve collaborative research within European computing consortia like NLAFET. Karlsson contributes to educational research, having explored mastery learning approaches in university settings. His administrative responsibilities include oversight of undergraduate programs at the Department of Computing Science.

Haohan Wangمشاهده پروفایل

استادیار

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +۵ مورد دیگر

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

Hans-Jörg von Mettenheimمشاهده پروفایل

استاد

• Decision Support Systems
• Artificial Intelligence in Finance
• Machine Learning
• +۵ مورد دیگر

Hans-Jörg von Mettenheim is Professor and Director of the Chair of Quantitative Finance and Risk Management at IPAG Business School Paris. He earned his Dr. rer. pol. (PhD) from Leibniz University Hannover with a dissertation on advanced neural networks in finance and forecasting. Current: IPAG Business School Paris Previous: École d'Économie (Leibniz University Hannover, 2010-2016) His research focuses on decision support systems, artificial intelligence in finance, and algorithmic trading. He has published extensively on neural networks, high-frequency trading, and energy economics. Recent work includes hybrid optimization techniques for energy forecasting, ESG sentiment-driven portfolio management, and cryptocurrency analytics. His editorial roles include Co-Editor-in-Chief of the Journal of Forecasting and founding Secretary General of the Forecasting Financial Markets Association.