جست‌وجوی استاد و پژوهشگر

Christian B. Mendlمشاهده پروفایل

استادیار

• Quantum Computing
• Tensor Network Methods
• Computational Physics
• +۳ مورد دیگر

Christian B. Mendl is an Assistant Professor (Rudolf Mößbauer Tenure Track) at Technische Universität München's Department of Computer Science. His research focuses on quantum computing, tensor network methods, computational physics/chemistry, high-performance computing, and theoretical condensed matter physics. He holds a PhD in Physics from LMU München and dual diplomas in Physics and Mathematics from TU München, with postdoctoral experience at Stanford University and TU Dresden. His work bridges quantum algorithms, numerical methods, and interdisciplinary applications. Notable contributions include Riemannian quantum circuit optimization, tensor network simulations, and quantum-classical computing frameworks. He collaborates with institutions globally and actively develops open-source tools like PyTreeNet for tensor networks. Recent research emphasizes scalable quantum algorithms, optimization techniques, and hybrid quantum-classical systems. His work addresses challenges in quantum computing, many-body systems, and high-performance simulation frameworks.

Ivan Markovskyمشاهده پروفایل

دانشیار

• Structured Low-Rank Approximation
• System Identification
• Data-Driven Control
• +۷ مورد دیگر

Ivan Markovsky is an Associate Professor at the Department of Fundamental Electricity and Instrumentation (ELEC) of the Vrije Universiteit Brussel. He previously held positions at the School of Electronics and Computer Science (ECS) at the University of Southampton. His research focuses on structured low-rank approximation, system identification, and data-driven control, with applications in signal processing and control systems. Education: PhD in Electrical Engineering from Katholieke Universiteit Leuven (2005). Research Interests: Specializes in low-rank approximation methods, system identification, and their applications in control theory and signal processing. Develops numerical optimization algorithms for structured matrices and tensors, with emphasis on practical engineering problems. His work bridges theoretical mathematics and applied control systems engineering. Awards: Received an ERC Starting Grant (2011) for research on structured low-rank approximation. Grants & Projects: ERC Starting Grant (2011-2016) - Structured Low-Rank Approximation FWOAL827 (2017-2020) - Block-oriented nonlinear identification using Volterra series FWOAL758 (2015-2018) - Decoupling multivariate polynomials in nonlinear system identification Labs/Teams: Leads research groups focused on system identification, matrix approximation, and data-driven control methodologies. Collaborates internationally on projects involving tensor approximation and nonlinear system modeling.

Hiba Nassarمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Computational Mathematics
• Numerical Methods
• +۳ مورد دیگر

Hiba Nassar is an Associate Professor at the Department of Applied Mathematics and Computer Science at the Technical University of Denmark. Her research focuses on functional data analysis, spline-based methods, and numerical algorithms for computational mathematics. Current affiliation: Technical University of Denmark Academic rank: Associate Professor Research domains: Functional data analysis, numerical analysis, statistical modeling Research Interests Dr. Nassar specializes in developing mathematical frameworks for analyzing complex data structures. Key areas include: Orthonormalization of B-splines Functional representation on multivariate domains Band matrix diagonalization techniques Data-driven basis selection in functional analysis Academic Contributions Her recent publications (2022–2025) demonstrate expertise in tensor methods, knot optimization, and sparsity preservation. She actively supervises PhD projects in computational mathematics and applied statistics. Supervision & Projects Dr. Nassar serves as a supervisor in three PhD projects: Federated Learning for Personalized Audiology (2025–2028) Modelling with 3D elastica (2024–2027) Mathematics of Surface Stackability (2024–2027)

Mark Colarussoمشاهده پروفایل

دانشیار

• Lie theory
• algebraic geometry
• representation theory
• +۲ مورد دیگر

Mark Colarusso is an Associate Professor in the Department of Math and Statistics at the University of South Alabama within the College of Arts and Sciences . His research bridges Lie theory, algebraic geometry, and integrable systems through algebraic and geometric methods. Education Ph.D. in Mathematics, University of California, San Diego Research Interests Mark specializes in the Gelfand-Zeitlin integrable system, analyzing its action on Lie algebras like gl(n) and so(n), and studying K-orbits on flag varieties. His work connects Poisson geometry with representation theory, focusing on algebraic integrability and eigenvalue coincidences. He also explores Lie-Poisson theory for direct limit Lie algebras and geometric structures in algebraic combinatorics. Publication Trends His publications emphasize the interplay between integrable systems and Lie theory, with recurring themes in Poisson geometry, orbit decompositions, and algebraic structures. Collaborations with Sam Evens and Michael Lau highlight interdisciplinary approaches to representation theory and symplectic geometry. Teaching He teaches across the calculus sequence (College Algebra, Differential Equations) and upper-level courses for secondary education teachers, including Abstract Algebra and Axiomatic Geometry.

Iveta Hnetynkovaمشاهده پروفایل

دانشیار

• Numerical Mathematics
• Linear Algebra
• Inverse Problems
• +۵ مورد دیگر

Iveta Hnetynkova is an Associate Professor at the Department of Numerical Mathematics, Faculty of Mathematics and Physics, Charles University in Prague. She specializes in numerical linear algebra, inverse problems, and regularization methods, with applications in image processing and scientific computing. Education: Doctor of Natural Sciences (RNDr.) from Charles University (2003) Ph.D. in Scientific Computations from Charles University (2006) Habilitation thesis on error-contaminated linear approximation (2019) Research Interests: Krylov subspace methods Total Least Squares (TLS) formulations Noise revealing in discrete inverse problems Tensor generalizations and structured matrices Applications in image processing and jewelry defect analysis Scientific Awards: Visegrad Group Young Researcher Award (2014) J. Jirsa Prize for textbook excellence (2013) I. Babuska Prize (2nd place) (2007) SVOČ Prize in mathematics (2003)

Felix Krahmerمشاهده پروفایل

دانشیار

• Optimization and Data Analysis
• Compressed Sensing
• Unlimited Sampling
• +۷ مورد دیگر

Prof. Dr. Felix Krahmer is an Associate Professor for Optimization and Data Analysis at the Department of Mathematics, Technical University of Munich (TUM) . His research focuses on mathematical foundations of data science, with expertise in Compressed Sensing Unlimited Sampling Quantization of Signals and Images Neural Networks Uncertainty Quantification Low-Rank Recovery He leads the Data Science research group within TUM's School of Computation, Information and Technology, collaborating with institutions like TU Berlin, RWTH Aachen, and Technion. His recent publications span topics like Robust blind deconvolution under adversarial noise Kronecker-structured dimensionality reduction Hysteresis in unlimited sampling systems Efficient sparse FFT algorithms Notable scientific awards include two August-Wilhelm-Scheer Visiting Professorships (2016, 2019) for hosting international collaborators. Former and current students include Olga Graf Dominik Stöger Claudio Verdun Felipe Pagginelli Patricio Juana Kostin He has co-organized workshops at SAMPTA, Oberwolfach, and ISIT, serving as associate editor for journals including SIAM Journal on Matrix Analysis and Frontiers in Signal Processing.

• Machine Learning
• Economics/Blockchain
• Mathematical Physics
• +۸ مورد دیگر

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

• Functional Analysis
• Harmonic Analysis
• Operator Theory
• +۴ مورد دیگر

Dmitriy Zanin is a Senior Lecturer in the School of Mathematics and Statistics at the University of New South Wales (UNSW), Faculty of Science. He is an active member of the Functional and Harmonic Analysis Group, which specializes in theoretical studies of functions and operators with applications spanning quantum physics and signal processing. His academic role involves research and instruction in advanced mathematical theories within a globally recognized institution. Zanin's research is deeply rooted in functional and harmonic analysis, extending into operator theory, noncommutative geometry, and mathematical physics. Key domains include: Operator algebras (von Neumann algebras, symmetric operator spaces) Noncommutative geometric structures (quantum tori, spectral invariants) Functional inequalities and embedding theorems Applications to quantum systems and signal processing frameworks His work consistently bridges abstract mathematical constructs with physical models. Analysis of Zanin's 15 most recent publications (2024–2025) reveals a strong focus on operator-theoretic frameworks in noncommutative settings. Predominant themes include: Sobolev embeddings and interpolation inequalities on quantum spaces Compactness criteria in quasi-Banach operator algebras Schatten-class commutator estimates and trace formulas Geometric invariants of deformed noncommutative manifolds This corpus demonstrates rigorous theoretical development at the intersection of functional analysis and quantum geometry.

Anne Margrethe Myhreمشاهده پروفایل

استاد

• Child and Adolescent Psychiatry
• Early-Onset Psychosis
• Schizophrenia
• +۵ مورد دیگر

Anne Margrethe Myhre is a Professor II in the Department of Child and Adolescent Psychiatry at the University of Oslo's Faculty of Medicine. Her research focuses on early-onset psychiatric disorders, particularly psychosis, schizophrenia, ADHD, and autism spectrum disorders in children and adolescents. Her research interests span child and adolescent psychiatry, neuroimaging in psychiatric disorders, early-onset psychosis, schizophrenia, ADHD, autism spectrum disorders, and the intersection of epigenetics with mental health. Dr. Myhre investigates both the neurobiological underpinnings and clinical manifestations of these conditions, with particular attention to cognitive functioning, brain structure and function, and potential biomarkers. Analysis of her recent publications reveals a strong focus on early-onset psychosis, with numerous studies examining structural and functional brain abnormalities, cognitive impairments, and potential biomarkers. Her work frequently employs advanced neuroimaging techniques and often involves large-scale collaborations such as the ENIGMA consortium. Many studies investigate the relationship between symptom domains, cognitive functioning, and brain structure in adolescent populations. Dr. Myhre is actively involved in the Imaging Psychosis research group at the University of Oslo and has contributed to the Ungdoms-TOP project, which appears to focus on youth mental health and psychosis. Her collaborative work spans multiple institutions and frequently involves large, multi-site studies that enhance the reliability and generalizability of findings in child and adolescent psychiatry.

Kirshanthan Sundararajahمشاهده پروفایل

استادیار

• Compilers
• Programming Languages
• High-Performance Computing
• +۳ مورد دیگر

Kirshanthan Sundararajah is an Assistant Professor in the Department of Computer Science at Virginia Tech's College of Engineering. His research focuses on compilers, programming languages, and high-performance computing, with an emphasis on optimizing algorithms for complex data structures like sparse tensors and tree traversals. He holds a Ph.D. and M.S. in Electrical and Computer Engineering from Purdue University (2022) and a B.S. in Electronics and Telecommunication Engineering from the University of Moratuwa, Sri Lanka (2014). His work spans compiler optimization techniques, parallel computing strategies, and efficient execution models for heterogeneous architectures. Key contributions include frameworks like Orchard for heterogeneous parallelism, SparseAuto for sparse tensor computations, and SABLE for blocked evaluation of sparse matrices. He also explores dynamic symbolic execution and secure multi-party computation through metaprogramming (HACCLE). No scientific awards were explicitly mentioned in the provided text. His advising and grant activities are not detailed here, though his research likely involves collaborative projects in high-performance computing and compiler design. He is affiliated with the Department of Computer Science at Virginia Tech and contributes to academic initiatives in parallel algorithms and computational efficiency.

• Statistics
• Machine Learning
• Tensor Methods
• +۳ مورد دیگر

Yuchen Zhou is an Assistant Professor in the Department of Statistics at the University of Illinois. His research focuses on high-dimensional statistics, tensor methods, and optimization, with applications in data analysis and machine learning. He has contributed to advancements in areas such as heteroskedastic PCA, low-rank tensor inference, and sparse group lasso regularization. His work emphasizes statistical theory and methodology, addressing challenges in high-dimensional data structures and computational efficiency. Collaborations and research outputs highlight interests in matrix and tensor decomposition, regularization techniques, and non-asymptotic analysis. Zhou's publications span reputable journals like the Annals of Statistics and IEEE Transactions on Information Theory, reflecting his expertise in both theoretical and applied statistical problems. He has no listed academic awards or current advisees.

Carlos I. Perez-Sanchezمشاهده پروفایل

پژوهشگر

• Mathematical Physics
• Noncommutative Geometry
• Random Matrices
• +۷ مورد دیگر

Carlos I. Perez-Sanchez is a postdoctoral researcher at the Institute for Theoretical Physics , University of Heidelberg, working under Răzvan Gurău . He previously held postdoctoral positions at the University of Warsaw (2021) and completed his PhD in mathematics at the University of Münster under Raimar Wulkenhaar. His research spans mathematical physics , focusing on random noncommutative geometry , tensor field theory , and their intersections with quantum gravity , functional renormalization , and bootstrap methods . His work on noncommutative geometry involves quantizing spectral triples via random matrices and unitary matrix ensembles to model gauge-Higgs theories and quantum gravity. He has advanced tensor field theory through topological recursion and Ward-Takahashi identities , bridging combinatorial structures with physical partition functions. His recent focus on functional renormalization (Wetterich equation) explores the renormalization group flow in multi-trace matrix models and free algebra frameworks. Publications include 15 recent articles (2025–2018) on topics like loop equations , Borel summability , and quiver-based noncommutative geometries . He co-organized workshops such as The QFT Path (2024) and Random Geometry in Heidelberg (2022), and has delivered talks at institutions in Bordeaux, Vienna, Warsaw, and Okinawa. His teaching includes Theoretical Statistical Physics and Large-N Methods in Field Theory at Heidelberg.

Devin Matthewsمشاهده پروفایل

استادیار

• Computational Chemistry
• Quantum Mechanics
• Computational Spectroscopy
• +۳ مورد دیگر

Devin Matthews is an Assistant Professor of Chemistry at Southern Methodist University (SMU), leading the Matthews Research Group. His research focuses on developing and applying ab initio quantum mechanical methods, with an emphasis on computational spectroscopy, tensor contraction algorithms, and coupled cluster theory. He holds a Ph.D. from The University of Texas at Austin (2014), following a B.S. in Chemistry (2010, also from UT Austin). Prior to SMU, he was a Postdoctoral Fellow at UT Austin's Institute for Computational Engineering and Sciences (2014-2018). Key research interests include Development of high-performance computational methods Quantum mechanical modeling of molecular systems X-ray spectroscopy simulations Algorithm optimization for parallel computing Matthews has been recognized with prestigious awards, including the Arnold O. Beckman Postdoctoral Fellowship (2015-2018) and the DOE Computational Science Graduate Fellowship (2010-2014). His work bridges chemistry, physics, mathematics, and computer science, with applications in understanding molecular structure and reactivity. Publications highlight advancements in tensor contraction techniques, coupled cluster theory scalability, and computational spectroscopy. The Matthews Group collaborates on projects involving electronic structure calculations and algorithm design for large-scale systems.

Joshua Agterbergمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Matrix and Tensor Analysis
• Nonconvex Optimization
• +۴ مورد دیگر

Joshua Agterberg is an Assistant Professor in the Department of Statistics at the University of Illinois, College of Liberal Arts & Sciences. His research focuses on high-dimensional statistics, matrix and tensor analysis, nonconvex optimization, and statistical network analysis. He holds a B.S. in Actuarial Science and Mathematics from the University of Wisconsin-Madison, followed by M.S. and Ph.D. degrees in Statistics from Johns Hopkins University. After a postdoctoral fellowship at the University of Pennsylvania, he returned to the Midwest to join UIUC. Education: Bachelor of Science in Actuarial Science and Mathematics, University of Wisconsin-Madison Master of Science in Statistics, Johns Hopkins University Doctor of Philosophy in Statistics, Johns Hopkins University Research Interests: His work bridges theoretical foundations and practical applications, addressing problems in nonconvex matrix sensing, multilayer network analysis, and statistical-computational gaps. He aims to develop methodologies for analyzing complex data structures, particularly in neuroscience, sociology, and engineering. Recent projects include advancing spectral methods, nonconvex optimization, and hypothesis testing frameworks for networks. Teaching Philosophy: Agterberg emphasizes statistical intuition over formulaic approaches, encouraging students to tackle problems from first principles. He teaches courses on theoretical statistics and hopes to develop a high-dimensional statistics curriculum for PhD students. Future Goals: He seeks to create a principled pipeline for network data analysis and to enhance understanding of machine learning algorithms' theoretical underpinnings. Collaborations include work on dynamic networks and statistical methodologies for connectomics.

Erik Hoyمشاهده پروفایل

دانشیار

• Quantum Chemistry
• Electron Transport
• Quantum Sensors
• +۳ مورد دیگر

Erik Hoy is an Associate Professor in the Department of Chemistry & Biochemistry at Rowan University, part of the College of Science & Mathematics. He holds a Ph.D. in Physical Chemistry from the University of Chicago and postdoctoral training at Johns Hopkins University and Northwestern University. His research focuses on quantum chemistry methods for studying electron transport, molecular devices, and organic materials. Key areas include developing novel Green’s function approaches combined with high-accuracy quantum chemistry techniques to design quantum sensors, electronic devices, and renewable energy materials. Education: Postdoctoral Associate at Johns Hopkins University and Northwestern University; Ph.D. in Physical Chemistry (University of Chicago); B.S. in Chemistry (Tennessee Tech University). Research Interests: Quantum Chemistry, Electron Transport, Quantum Sensors, Molecular Electronics, Computational Organic Reactions. His group emphasizes high-accuracy electronic structure methods and their application to nanoscale systems, with recent work on molecular junctions and electrocatalysis. Awards: NSF CAREER Award (2024), NSF ExpandQISE Award (2024), Eppley Foundation Grant (2023). His group has secured grants for projects like 'Strongly Correlated Molecular Qubits' and 'Quantum Sensors.' Advising & Grants: Supervises a diverse group of undergraduate and graduate students, including notable advisees such as Grace McGowan and Kale Kroenke. Collaborates with industry and academic partners, such as the Lofland group at Rowan and the Sand group at Butler University. Lab Activities: The Hoy Research Group develops computational tools like pyRUQT and contributes to software like MoleMod. Their work is highlighted in journals like Advanced Functional Materials and Physical Chemistry Chemical Physics .