جست‌وجوی استاد و پژوهشگر

Rabia Djellouliمشاهده پروفایل

استاد

• Mathematical and Numerical Aspects of Wave Propagation
• Inverse Problems
• Computational Mechanics
• +۵ مورد دیگر

Rabia Djellouli is a Professor in the Department of Mathematics at California State University Northridge (CSUN), where she has been since 2003. Previously, she held academic positions at institutions including the University of Colorado Boulder (1996–2003) and Tunisian universities such as Ecole Supérieure des Télécommunications de Tunisie (1995–1996). She earned her Ph.D. in Mathematics from the University of Paris-XI and École Polytechnique (France) in 1988, following degrees from the University of Paris-Sud and University of Algiers. Research Interests focus on wave propagation phenomena, inverse problems, computational mechanics, and biomedical engineering. Her work spans mathematical physics, numerical analysis, and bio-mathematics, with applications in optical fibers, fluid-structure interactions, and medical imaging. Her publications emphasize solutions to acoustic and elastoacoustic scattering problems, numerical methods for partial differential equations, and applications in biomedical engineering such as fibrous capsule growth modeling. She has secured grants from NSF and industry partners like Medtronic, supporting research in computational methods and medical device innovation. Service & Mentoring includes roles as Department Graduate Committee Chair, organizer of student research symposia, and mentor for over 20 undergraduate and graduate students. She has led NSF-funded programs like PUMP (Preparing Undergraduates through Mentoring toward Ph.D.s), fostering student research in mathematics. Affiliations include the College of Science and Mathematics at CSUN, and collaborations with institutions like INRIA (France) through international research initiatives.

• Hardware Security
• Cryptography
• Embedded Systems
• +۲ مورد دیگر

Vittorio Zaccaria is Associate Professor at Politecnico di Milano's Department of Electronics, Information and Bioengineering. His research focuses on secure embedded systems with emphasis on hardware security and cryptographic implementations. Research interests include: Side-channel attack mitigation Formal verification methods Secure processor architecture Probabilistic security models Hardware countermeasures His publications demonstrate expertise in developing mathematical frameworks for analyzing information leakage and implementing robust security measures for embedded devices. Recent work focuses on spectral analysis of probing security and formal verification of cryptographic implementations.

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.

Ilya Karlinمشاهده پروفایل

مدرس

• Fluid Dynamics
• Computational Fluid Dynamics
• Thermodynamics
• +۳ مورد دیگر

Prof. Ilya Karlin is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. His research focuses on advanced computational fluid dynamics, particularly leveraging lattice Boltzmann methods for simulating complex fluid phenomena. Key areas include non-ideal fluid behavior, multiphase flows, combustion processes, and hydrodynamic closures. He has contributed extensively to improving numerical methods for compressible flows, turbulent systems, and reactive mixtures in porous media. His work bridges kinetic theory with continuum mechanics, addressing challenges in hydrodynamic manifolds and non-local effects. Dr. Karlin's publications emphasize rigorous mathematical analysis alongside computational innovation, such as spectral closure techniques and entropy-based models. His research often explores the interplay between microscopic kinetic descriptions and macroscopic hydrodynamic equations. He has developed novel algorithms like the 'particles on demand' method for handling strong discontinuities in flows. Recent work includes studies on rarefaction effects, capillarity-viscosity balance, and exact hydrodynamic manifolds for BGK equations. His studies span diverse applications from microfluidics and phase transitions to detonation modeling and environmental fluid mechanics. While no specific awards are listed in the provided text, his prolific publication record indicates significant contributions to the field of computational fluid dynamics.

Amir Demboمشاهده پروفایل

استاد

• Probability
• Stochastic Processes
• Random Graphs
• +۴ مورد دیگر

Amir Dembo is the Marjorie Mhoon Fair Professor in Quantitative Science at Stanford University, holding appointments in the Department of Mathematics and Statistics, and adjunct status in the Department of Electrical Engineering. His research focuses on probability theory and stochastic processes, with contributions to random graphs, large deviations, spin glasses, and statistical mechanics. He has authored numerous influential papers on topics such as random cluster models, Gibbs measures, and universality in interacting systems. Dembo teaches advanced probability courses (e.g., STAT310/MATH230) and has explored applications of probability in cryptography, including proof-of-stake blockchain protocols. His work bridges theoretical foundations with interdisciplinary applications in physics and computer science. Education details are not explicitly provided, but his academic trajectory is reflected through his professorial roles and research output. His research interests emphasize probabilistic analysis of complex systems, including random walks, graph structures, and non-equilibrium dynamics. Over 30 years of publications highlight his expertise in large deviation principles, asymptotic analysis of stochastic processes, and the interplay between combinatorics and probability. His recent work (2023–2025) explores cutting-edge topics like regular-tree-like graph models, flow-type scaling limits, and geometric area tilts in Brownian polymers. Earlier contributions include foundational studies on Erdős–Rényi hypergraphs, spectral measures of random matrices, and the universality of Langevin spin glass dynamics. While no explicit awards are listed, his prolific publication record and academic leadership reflect significant scholarly impact. Dembo’s advising and grants are not detailed here, but his teaching and research indicate mentorship in probability theory and stochastic modeling. Collaborations with interdisciplinary teams likely extend into areas like theoretical computer science and statistical physics.

Michail Papathomasمشاهده پروفایل

مدرس ارشد

• Bayesian Statistics
• Biostatistics
• Ecological Statistics
• +۷ مورد دیگر

Dr Michail Papathomas is a Senior Lecturer in Statistics at the School of Mathematics and Statistics, University of St Andrews. He is affiliated with the Centre for Research into Ecological & Environmental Modelling. His research focuses on Bayesian statistical methodology, mixture modeling, and applications in ecology, public health, and genomics. He has supervised PhD students including Chao Gao, Thu Nguyen, and Hanyu Wang. His work bridges theoretical developments with practical applications in areas like spatial capture-recapture techniques and antibiotic resistance epidemiology. Recent projects include analysis of multidrug-resistant infections in East Africa and chemical evolution studies of galaxies. Education: PhD in Statistics (inferred from academic role) Research interests span Bayesian nonparametrics, Dirichlet process mixtures, and statistical methods for ecological and biomedical data. He collaborates internationally on projects addressing global health challenges and environmental monitoring. His publications often integrate advanced computational methods with real-world datasets. Key research trends include: (1) Developing flexible statistical frameworks for complex ecological systems; (2) Addressing parameter identifiability in log-linear models; (3) Applying mixture models to genomic and epidemiological datasets. Grants: Received funding for a cancer research studentship from The Melville Trust, and Scottish Funding Council support for blockchain applications in irrigation governance. Led development of spatial capture-recapture methodologies and contributed to tools for galaxy spectral analysis. Active in interdisciplinary collaborations across medicine, ecology, and astrophysics.

Qianqian Yangمشاهده پروفایل

مدرس ارشد

• Fractional Differential Equations
• Numerical Methods
• Medical Imaging (MRI)
• +۳ مورد دیگر

Dr. Qianqian Yang is a Senior Lecturer in applied and computational mathematics at the School of Mathematical Sciences, Queensland University of Technology (QUT). She holds a PhD in computational mathematics from QUT (2010) and has been recognized with prestigious awards such as the QUT Outstanding Doctoral Thesis Award (2010) and an ARC DECRA fellowship (2014). Her research focuses on fractional differential equations, numerical methods, and their applications in medical imaging, particularly diffusion MRI. She has secured major grants, including an ARC DECRA fellowship (2015–2022, part-time) and an ARC Discovery Project (2019–2022). Dr. Yang’s work bridges computational mathematics and biomedical applications, aiming to model brain tissue microstructure using fractional models. She teaches computational mathematics units and has supervised 2 postdocs, 3 PhDs, and numerous Honours and VRES students. Her research is published in top journals like NeuroImage and SIAM Journal on Scientific Computing. Expertise: Fractional PDEs, Numerical Analysis, MRI Modeling Grants: ARC DECRA, ARC Discovery Project

Michael Levitinمشاهده پروفایل

استاد

• Spectral Theory
• Spectral Geometry
• Mathematical Physics
• +۴ مورد دیگر

Michael Levitin is a Professor of Applied Mathematics at the Department of Mathematics and Statistics, University of Reading. He leads the Pure Mathematics Group and serves on the Departmental Research Committee. His research focuses on spectral theory, spectral geometry, and applications in mathematical physics, numerical analysis, and operator theory. Levitin holds a Candidate of Sciences (Ph.D.) from the Moscow Institute of Physics and Technology (1989), with a thesis on fluid-structure interactions. Prior to Reading, he held roles at Heriot-Watt University, Cardiff University, and Sussex University. His work includes foundational contributions to spectral asymptotics, non-self-adjoint operators, and geometric spectral theory. Key projects involve Pólya’s conjecture for eigenvalues, Steklov problems, and waveguide eigenmodes. He co-authored the textbook Topics in Spectral Geometry (2023) and frequently organizes international workshops, such as the 2026 Modern Applications of Microlocal Analysis conference honoring Dmitri Vassiliev. Levitin’s research integrates pure and applied mathematics, with applications in physics and engineering. Education: BSc+MSc (Applied Mathematics, Moscow Institute of Physics and Technology, 1986), PhD (1989). Employment History: University of Reading (2010–present), Cardiff University (2007–2010), Heriot-Watt University (1993–2007), University of Sussex (1992–1993). Research Interests: Spectral geometry of Laplace/Dirac/Maxwell operators, eigenvalue inequalities, numerical methods, and operator pencils. Collaborations include notable mathematicians like Iosif Polterovich, David Sher, and Matteo Capoferri. Grants and recognitions include EPSRC funding for microlocal analysis applications. Teaching includes Real Analysis and Complex Analysis courses at Reading. Publications span over 50 peer-reviewed papers in journals like Inventiones Mathematicae , SIAM Journal on Mathematical Analysis , and Journal of Spectral Theory . His work emphasizes interdisciplinary approaches, combining analytical techniques with numerical methods. Current projects include spectral asymptotics in linear elasticity and geometric wave propagators on manifolds.

Gregory Markowskyمشاهده پروفایل

مدرس ارشد

• Stochastic processes
• Complex analysis
• Graph theory

Gregory Markowsky serves as a Senior Lecturer in the School of Mathematics at Monash University, maintaining an active research profile with continuous publication output through 2025. His academic appointment shows no indication of part-time status, emeritus designation, or former staff classification. His research centers on three interconnected domains: Stochastic processes, particularly planar Brownian motion and random walk theory Complex analysis with applications to probabilistic models Graph theory focusing on algebraic and combinatorial structures These areas converge in his investigation of probabilistic phenomena with geometric interpretations. Recent publication trends reveal sustained productivity with 61 research outputs cataloged, including 4 articles in 2025 alone. His work demonstrates consistent methodological focus on stochastic analysis and combinatorial structures, with increasing cross-disciplinary applications in forensic science and econometrics as evidenced by 2024-2025 publications. Markowsky has secured three significant research projects funded by the Australian Research Council, including: "Planar Brownian motion and complex analysis" (2014-2017) as Primary Chief Investigator "Finite Markov chains in statistical mechanics and combinatorics" (2014-2017) "AMSI Industry Internship: Melbourne Storm" (2013) His ORCID profile (0000-0003-1656-337X) documents extensive scholarly activity with international collaborations. Professional engagement includes contributions toward UN Sustainable Development Goals through mathematical applications, though specific goal alignments aren't detailed in available materials. His email contact Greg.Markowsky@monash.edu remains active with current research dissemination.

Bojan Moharمشاهده پروفایل

استاد

• Topological Graph Theory
• Graph Minors
• Graph Coloring
• +۳ مورد دیگر

Bojan Mohar is a Professor in the Department of Mathematics at Simon Fraser University (SFU), within the Faculty of Science. He holds a Ph.D. in Mathematics from the University of Ljubljana, Slovenia (1986). His research focuses on advanced topics in graph theory, including topological graph theory (graphs on surfaces, planar graphs), graph minors, graph coloring (list coloring, edge-coloring, nowhere-zero flows), algebraic graph theory (Laplace eigenvalues, spectral analysis), and graph algorithms. Mohar's work bridges theoretical foundations with computational methods, emphasizing interdisciplinary applications. His research interests span diverse areas such as the spectral properties of infinite graphs, graph embeddings, and combinatorial optimization. He is affiliated with the Centre for Operations Research and Decision Sciences (CORDS) at SFU. Mohar has made significant contributions to understanding graph structures, eigenvalues, and algorithm design, with a particular emphasis on topological and algebraic aspects. His recent work includes proofs of long-standing conjectures in graph theory and the development of efficient approximation algorithms for graph genus calculations. Mohar’s academic contributions are reflected in his extensive publication record, focusing on graph minors, eigenvalue analysis, and topological embeddings. He teaches courses such as MATH 345 D100: Introduction to Graph Theory (Fall 2025). No formal advising relationships or awards are explicitly listed in the provided materials, though his research impact is evident through collaborations and symposium participations like the International Symposium on Computational Geometry (SoCG).

Steven Ruuthمشاهده پروفایل

استاد

• Applied Mathematics
• Numerical Analysis
• Partial Differential Equations
• +۴ مورد دیگر

Steven Ruuth is a Professor in the Department of Mathematics at Simon Fraser University (SFU), within the Faculty of Science. He specializes in numerical methods for partial differential equations (PDEs), particularly those involving complex geometries and discontinuous solutions. His work bridges applied mathematics and computational science, focusing on interface dynamics, geometric numerical methods, and efficient algorithm development. Ph.D. in Applied Mathematics, University of British Columbia (1996) NSERC Postdoctoral Fellow and Visiting Assistant Professor at UCLA (1996–1999) His research emphasizes robust numerical techniques for evolving networks of interfaces, implicit-explicit time integration methods, and the closest point method for PDEs on manifolds. Applications span computational fluid dynamics, materials science, and computer vision. He has contributed to advancements in meshfree methods, RBF-FD discretizations, and parallel domain decomposition algorithms. Recipient of the Germund Dahlquist Prize (2011) and CAIMS Research Prize (2020) , he is also an editorial board member for SIAM Journal on Scientific Computing and Numerical Mathematics: Theory, Methods and Applications. Teaching responsibilities include courses such as MATH 260 (Introduction to Ordinary Differential Equations). His research group actively explores cutting-edge computational methods with applications to geometry processing and scientific computing challenges.

Octav Corneaمشاهده پروفایل

استاد

• Algebraic Topology
• Differential Topology
• Symplectic Geometry
• +۳ مورد دیگر

Octav Cornea is a Professor in the Département de Mathématiques et Statistique at Université de Montréal. His research focuses on symplectic topology, algebraic topology, and Lagrangian submanifolds. He is an active editor for journals like Annales Mathématiques du Québec and Canadian Journal of Mathematics . He co-organizes the Symplectic Zoominar , a collaborative seminar involving institutions such as CRM-Montreal, Princeton, and Paris. Current roles include sabbatical leave (2025-2026). His work explores geometric structures like Fukaya categories and spectral metrics, with contributions to persistent homology and categorical frameworks. Education: Ph.D. in Mathematics from the University of Rochester (1993). Past affiliation: Member at the Institute for Advanced Study (2015-2016). His research integrates algebraic and geometric methods to address problems in symplectic dynamics and Lagrangian topology.

Renato Feresمشاهده پروفایل

استاد

• Differential Geometry
• Dynamical Systems
• Stochastic Processes
• +۴ مورد دیگر

Renato Feres is a Professor of Mathematics at Washington University in St. Louis, affiliated with the Department of Mathematics within the Arts & Sciences college. He holds a PhD from the California Institute of Technology. His research focuses on differential geometry, dynamical systems, and stochastic dynamics, with recent work exploring random billiards and stochastic thermodynamics. Feres has taught advanced courses such as 'The Mathematics of Quantum Theory,' 'Differential Geometry,' and 'Stochastic Processes.' His research interests bridge pure and applied mathematics, with contributions to geometric mechanics, quantum theory, and mathematical physics. Recent courses include Math 401: Honors Seminar in Math (Spring 2025) Math 444: The Mathematics of Quantum Theory (Spring 2025) Math 5047: Geometry/Topology III: Differential Geometry (Fall 2024) His research explores stochastic dynamics (e.g., random billiards, stochastic thermodynamics), differential geometry (connections to mechanical systems), and mathematical physics. Recent publications address reaction-advection-diffusion systems, chaotic billiards, and entropy production in random systems. Feres has advised numerous graduate students through his teaching and research mentorship, though specific student names are not listed. He collaborates with researchers in chemical engineering and applied mathematics, contributing to interdisciplinary projects in reaction-diffusion systems and transport phenomena. No scientific awards are explicitly mentioned in the provided materials. His work is supported by ongoing research activities within the university's mathematics department and collaborations with institutions like the California Institute of Technology and the University of Michigan.

Shahin Tavakoliمشاهده پروفایل

مدرس ارشد

• Statistics
• Functional Data Analysis
• Neuro-imaging
• +۴ مورد دیگر

Shahin Tavakoli is a Senior Lecturer in the Research Institute for Statistics and Information Science at the Geneva School of Economics and Management (University of Geneva). He holds a PhD in Mathematical Statistics from EPFL and has held positions as a University Research Fellow at the University of Cambridge and a tenure-track Assistant Professor at the University of Warwick. His research focuses on functional data analysis with applications in neuroimaging, phonetics, biophysics, econometrics, and genomics. Education: BSc/MSc in Mathematics (EPFL), PhD in Mathematical Statistics (EPFL). Key roles include Associate Editor for the Journal of Statistical Planning and Inference and proposer for a JRSS B discussion paper. Teaching includes Applied Bayesian Statistics, Multivariate Analysis, and Mathematics courses at the University of Geneva. Research interests emphasize statistical methodologies for complex data structures, including high-dimensional functional time series and spatial modeling of linguistic data. Notable recent publications address phonetic analysis, brain imaging, and econometric factor models. Collaborations span institutions like the University of Cambridge, University of Warwick, and LMU Munich. Advising includes PhD students Marco Palma and Beatrice Matteo, with contributions to projects such as functional regression clustering and normative brain mapping. His work bridges theoretical statistics with applied domains, reflecting interdisciplinary impact across natural and social sciences.

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.