جست‌وجوی استاد و پژوهشگر

Jesse Perlaمشاهده پروفایل

دانشیار

• Macroeconomics
• Economic Growth
• Information Diffusion
• +۵ مورد دیگر

Jesse Perla is an Associate Professor in the Vancouver School of Economics at the University of British Columbia, Faculty of Arts. He maintains an office in the Iona Building 201B and is actively engaged in research and teaching within the economics department. Dr. Perla received his PhD in Economics from New York University in 2013 and completed his undergraduate studies in Applied Mathematics at Columbia University in 1997. His academic journey has positioned him at the intersection of economics, mathematics, and computational methods. His research focuses on macroeconomics and growth from the firm perspective, with particular emphasis on information diffusion, heterogeneous agents, and computational approaches to economic modeling. He has developed significant expertise in applying machine learning techniques and high-dimensional methods to economic problems, with notable work on technology diffusion, financial frictions, and dynamic programming. His research agenda spans theoretical and computational economics, with increasing integration of artificial intelligence methods in recent years. Perla's publications reveal a strong trend toward computational economics, with growing emphasis on deep learning applications to overcome the curse of dimensionality in economic models. His work frequently explores how information diffusion affects firm behavior and economic growth, often employing sophisticated mathematical and computational techniques to model complex economic phenomena. He is a co-author of the influential open textbook Quantitative Economics with Julia and has made significant contributions to computational economics education. His teaching focuses on preparing students for graduate work in economics, with particular emphasis on mathematical preparation and computational skills. He has published extensive course advice for UBC undergraduates interested in pursuing graduate studies. Perla actively contributes to the development of computational tools for economists, particularly through the Julia programming language ecosystem. His work includes developing educational materials on differential equations for epidemiological modeling in economics and maintaining comprehensive data source references for economic researchers.

• Geometric Quantization
• Complex Geometry
• Mathematical Physics
• +۳ مورد دیگر

Tatyana Barron is a Professor in the Department of Mathematics at the University of Western Ontario since July 2022. She holds a Ph.D. from Pennsylvania State University (1998) and has held academic positions at institutions like the University of Michigan and the University of Arizona. Her research focuses on geometric quantization, complex geometry, and mathematical physics. She has supervised five Ph.D. students and has an extensive publication record spanning topics from Toeplitz operators to entropy analysis in quantum systems. Education: Ph.D. in Mathematics, Pennsylvania State University (1998) Bachelor's Degree, Moscow Institute of Physics and Technology (1994) Research Interests: Tatyana Barron explores geometric quantization techniques applied to complex manifolds, with particular emphasis on Kähler and multisymplectic structures. Her work bridges differential geometry, algebraic geometry, and quantum mechanics, addressing topics like automorphic forms, entropy in quantum systems, and the asymptotic properties of geometric operators. She has contributed to understanding Berezin-Toeplitz operators, entanglement in mixed states, and geometric models for therapeutic approaches involving electromagnetic fields. Her recent publications (2023-2025) highlight advancements in geometric signal theory, entanglement quantification, and semiclassical asymptotics. She has also explored mathematical models for medical applications using electromagnetic principles. Advising: Supervised five Ph.D. students focusing on topics ranging from holomorphic differentials to singular Lagrangian submanifolds. She co-supervised three of these students with colleagues in geometric analysis and differential geometry.

Jairo Bochiمشاهده پروفایل

استاد

• Dynamical Systems
• Ergodic Theory
• Geometry
• +۵ مورد دیگر

Jairo Bochi is a Professor in the Department of Mathematics at Pennsylvania State University (Penn State), part of the Eberly College of Science. He is a member of the Anatole Katok Center for Dynamical Systems and Geometry. His research focuses on Dynamical Systems, including interactions with Geometry, Linear Algebra, and Control Theory. Bochi earned his PhD from the Instituto de Matemática Pura e Aplicada (IMPA) in Brazil (2001), with prior academic roles at institutions like PUC-Chile, PUC-Rio, and UFRGS. Education: PhD in Mathematics from IMPA (2001), M.S. and B.S. in Mathematics from UFRGS (1997 and 1996). His academic journey includes postdoctoral and faculty positions in Brazil, Chile, and now the U.S. Research Interests: Dynamical Systems, Ergodic Theory, Lyapunov exponents, Multiplicative Ergodic Theory, and applications to Linear Algebra and Control Theory. His work bridges pure mathematics with geometric and analytical techniques. Key Contributions: Pioneering studies on generic properties of Lyapunov exponents, dominated splittings, and flexibility of entropy in dynamical systems. He has organized major conferences, including the 11th Beyond Uniform Hyperbolicity conference (2025). Bochi is an Affiliate Member of the Brazilian Academy of Sciences (2007–2011) and serves on the editorial board of Discrete and Continuous Dynamical Systems . Scientific Service: Organizer of international workshops, advisor to graduate students, and recipient of research grants from institutions like Fondecyt and Penn State's Eberly College. His teaching spans advanced courses in Dynamical Systems, Ergodic Theory, and related fields. Labs/Teams: Active in the Anatole Katok Center, contributing to collaborative research on dynamical systems and geometry.

Matthew B Dayمشاهده پروفایل

دانشیار

• Geometric Group Theory
• Low Dimensional Topology
• Combinatorial Group Theory
• +۵ مورد دیگر

Matthew B. Day is an Associate Professor in the Department of Mathematical Sciences at the University of Arkansas, Fayetteville. He holds a Ph.D. in Mathematics from the University of Chicago (2008) and previously held postdoctoral positions at Caltech. His research focuses on geometric group theory, low-dimensional topology, and combinatorial group theory, with a recent emphasis on automorphism groups of right-angled Artin groups. He has received multiple NSF grants and fellowships, including a Postdoctoral Research Fellowship (2008–2010) and a Graduate Research Fellowship (2002–2007). He has co-organized conferences like the Redbud Topology Conference and contributed to teaching graduate and undergraduate courses in topology, algebra, and analysis. Education: B.S. Mathematics, B.A. Linguistics & Philosophy (UT Austin, 2002); M.S. and Ph.D. in Mathematics (University of Chicago, 2004 and 2008). Professional Service: Organized the 2015 Arkansas Spring Lecture Series, reviewed for Mathematical Reviews, and served on university committees for hiring and curriculum. Research Trends: His work bridges geometric group theory with low-dimensional topology, exploring automorphism groups, mapping class groups, and symplectic structures. Recent papers address cohomological dimensions, subspace arrangements, and finiteness properties of groups. He has advised conferences and collaborates with institutions like the Ohio State University and Trinity College, Dublin. His teaching spans foundational courses to specialized graduate topics in topology and algebra.

Eric L. Grinbergمشاهده پروفایل

استاد

• Integral Geometry
• Mathematical Analysis
• Geometric Analysis
• +۳ مورد دیگر

Eric L. Grinberg is a Professor in the Department of Mathematics at the University of Massachusetts Boston, affiliated with the College of Science and Mathematics. He holds a Ph.D. in Mathematics from Harvard University. His research focuses on integral geometry, geometric analysis, and their applications, with notable contributions to Radon transforms, X-ray transforms, and geometric inequalities. Education: Ph.D. in Mathematics, Harvard University Research interests include: Integral transforms (Radon, X-ray) Geometric analysis (Busemann-Petty problems, convex bodies) Applications in discrete mathematics and algebra Recent work explores matrix theory, geometric puzzles, and finite field geometry. His articles often bridge theoretical mathematics with applied problems in imaging and signal processing. No awards listed. Advising and grants details are not explicitly provided in the text. He collaborates with researchers like Mehmet Orhon on geometric projects. Office: Wheatley 3-154-10.

Long Leeمشاهده پروفایل

استاد

• Numerical Analysis
• Computational Fluid Dynamics
• Nonlinear Partial Differential Equations
• +۷ مورد دیگر

Professor Long Lee is a faculty member in the Department of Mathematics and Statistics at the University of Wyoming, where he has held the rank of Professor of Mathematics since 2005. His research spans numerical analysis, computational fluid dynamics, and nonlinear partial differential equations, with recent extensions into image registration and pattern classification. Ph.D. in Applied Mathematics, University of Washington, 2002 M.S. in Applied Mathematics, University of Maryland at College Park, 1998 M.S. in Geophysics, National Central University, Taiwan, 1990 B.S. in Bio-Machinery Engineering, National Taiwan University, 1988 Lee’s research focuses on developing advanced numerical algorithms for Euler-Poincaré equations, shallow-water waves, and computational methods for medical imaging and epidemiology. His work bridges mathematical rigor with real-world applications in fluid dynamics, nonlinear optics, and ecological modeling. Recent publications highlight his contributions to self-similar asymptotics, geodesic shooting algorithms for template matching, and numerical solutions for ill-conditioned systems. These articles demonstrate expertise in numerical linear algebra, geometric pattern recognition, and nonlinear dynamics.

Son-Young Yiمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +۴ مورد دیگر

Son-Young Yi is a Professor in the Department of Mathematical Sciences at the University of Texas at El Paso (UTEP), specializing in Computational Science. Her research focuses on numerical methods for partial differential equations, particularly finite element methods applied to solid mechanics, flow in deformable porous media, fluid-solid interactions, and multiscale analysis. She develops physics-preserving numerical schemes and contributes to the study of locally-conservative discretizations and reduced-order modeling. Her research interests span numerical analysis, scientific computing, and computational mechanics. Notable areas include enriched Galerkin methods for coupled thermo-poroelasticity systems, locking-free formulations for poroelasticity and linear elasticity, and numerical studies of financial models like European option pricing with non-Lévy jumps. Recent advancements in her work include projection-based reduced order modeling for iterative coupling schemes and immersed interface methods for interface problems. These contributions address challenges in accurately simulating complex physical phenomena across multiple scales and coupled processes. Dr. Yi's work has been supported by significant grants, including a $5 million award from UTEP for climate change mitigation research. Her publications reflect interdisciplinary collaborations in computational fluid dynamics, solid mechanics, and geophysical modeling.

• Process Control
• System Dynamics
• Process Design Optimization
• +۵ مورد دیگر

Nikolaos A. Diangelakis is an Assistant Professor at the Technical University of Crete (TUC), affiliated with the School of Chemical Engineering (ChEnvEng) and the Division II: Process Development, Analysis and Design. His research focuses on system dynamics, process control, and integrated process design optimization. He holds a PhD from Imperial College London (2017), an M.Sc. from the same institution (2012), and a Diploma in Chemical Engineering from the National Technical University of Athens (2011). He has conducted a Visiting Ph.D. research at Texas A&M University (2017). Diangelakis specializes in multi-parametric optimization and control strategies, with applications in chemical, environmental, and energy systems. He has contributed to software tools like PAROC and POP, advancing explicit model predictive control (MPC) and robust optimization frameworks. His work integrates process design, scheduling, and control to enhance operational efficiency in industries such as pharmaceuticals and energy systems. He has secured research funding through projects like 'PAROC' (National Science Foundation) and 'U Psi Psi' (EPSRC), and has been recognized with awards including the 2017 Excellence Award for Outstanding PhD Thesis in Computer-Aided Process Engineering (CAPE). His academic contributions span over 30 peer-reviewed publications, emphasizing topics like microgrid optimization, rotary tablet press control, and energy-efficient industrial processes. Diangelakis teaches Linear Algebra at TUC and actively engages in academic conferences, delivering invited lectures and presenting innovative research on MPC strategies and process operability. His research group collaborates internationally, advancing the frontiers of model-based systems engineering.

• Quantum Computing
• Mathematical Logic
• Fuzzy Logic
• +۳ مورد دیگر

Roberto Leporini is an Associate Professor at the University of Bergamo, where he has been a researcher since 2005. His research focuses on quantum information processing systems, including conservative and multivalued computation, optical microdevices, fuzzy and quantum logics, and compositional/holistic semantics. He holds a PhD in Computer Science from the University of Milan (2004) and has authored over 40 international publications. Leporini teaches Computer Science courses at the University of Bergamo and supplementary courses in Cybernetics and Analysis at other institutions. He has been actively involved in academic governance, serving in roles such as member of the Academic Senate and President of the International Quantum Structures Association (IQSA). Additionally, he holds a patent for quantum circuit construction and collaborates with the National Institute of Nuclear Physics. His research interests bridge quantum computing, mathematical logic, and their applications to computational systems. Notable contributions include studies on quantum entanglement, fuzzy logic representations of quantum gates, and holistic computational semantics. Leporini’s work emphasizes interdisciplinary approaches, integrating theoretical computer science with quantum physics principles.

Xiaozhe Huمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Multigrid Methods
• +۷ مورد دیگر

Xiaozhe Hu is a full-time Professor in the Department of Mathematics at Tufts University since July 2024. Previously held positions include Associate Professor (2019-2024) and Assistant Professor (2014-2019) at Tufts, and Adjunct Associate Professor (2020-2022) at the University of Bergen, Norway. Education : PhD in Computational Mathematics (Zhejiang University, 2009); BS in Information and Computer Science (Zhejiang University, 2004) His research focuses on scientific computing and numerical analysis , particularly: Development of adaptive and parallel numerical methods for PDEs and graph problems Multigrid/multilevel solvers for large-scale coupled systems Quantum algorithms and spectral graph theory Applications in poromechanics , reservoir simulation , and bioinformatics Recent publications demonstrate expertise in preconditioning techniques for Biot’s model, meshless methods for fluid-structure interaction, and data-driven discretization approaches . Awards include the Reimann-Louville Award (2016) and outstanding PhD graduate recognition (Zhejiang Province, 2009). PhD Students : Junyuan Lin (Loyola Marymount), Peter Ohm (RIKEN), Casey Cavanaugh (LSU), Kaiyi Wu, Eoghan O'Keefe Master Students : Charles Colley (Purdue PhD), Yue Shen (Florida State PhD), Samuel Rabinowitz, Phong Huang, Samuel Hocking Co-organizer of the Computational and Applied Mathematics Seminar and contributor to open-source HAZmath finite element library.

Aritra Duttaمشاهده پروفایل

استادیار

• Machine Learning
• Federated Learning
• Optimization
• +۷ مورد دیگر

Aritra Dutta is an Assistant Professor at the AI Initiative of the University of Central Florida (UCF), primarily affiliated with the Department of Mathematics and secondarily with the Department of Computer Science. He also holds an affiliation with the Pioneer Centre for AI (P1), Denmark. His research focuses on optimization (stochastic/nonconvex), distributed computing (including federated learning), numerical linear algebra, machine learning, low-rank approximation, and computer vision applications such as image/video analysis and object detection/tracking. Dr. Dutta’s work emphasizes interdisciplinary approaches, blending mathematical rigor with computational efficiency. He actively seeks Ph.D. students and postdocs with strong foundations in mathematics (optimization, linear algebra) or computer science (ML, distributed systems), prioritizing candidates with publication records in top-tier venues. His teaching includes courses in applied mathematics and computational methods. His research outputs span communication-efficient distributed learning frameworks, convergence analysis of optimization algorithms, and vision transformer architectures. Notable projects include GAEA (geolocation-aware conversational models) and MAVREC (multi-view aerial visual recognition). Postdoc opportunities: Open for exceptional candidates. Labs/Initiatives: UCF AI Initiative (UCF Aii), UCF Center for Research in Computer Vision (CRCV). Grants: Competitive research assistantships with tuition support.

Christino Tamonمشاهده پروفایل

استاد

• Theoretical Computer Science
• Quantum Computing
• Machine Learning
• +۳ مورد دیگر

Christino Tamon is a Professor of Computer Science at Clarkson University's Coulter School of Engineering & Applied Sciences. He holds a Ph.D. from the University of Calgary (1993-1996), an M.S. from the University of Toronto (1990-1992), and a B.Sc. in Computer Science and Applied Mathematics from the University of Calgary (1986-1990). His research focuses on theoretical computer science, quantum computing, graph theory, and machine learning, with notable contributions to quantum state transfer and quantum walks on graphs. Recipient of the Clarkson University Distinguished Teaching Award (2009) and New Teacher Award (2000). Principal Investigator on multiple NSF grants, including quantum computing and REU mathematics programs. Visiting appointments at institutions like the Institut Henri Poincaré and the University of Waterloo. Research interests include quantum algorithms, graph spectral theory, and the application of algebraic methods to discrete systems. His work on quantum walks explores perfect state transfer, fractional revival, and spatial search optimization. Recent grants support quantum advantage in algorithm design and interdisciplinary research in quantum dynamics. Advised numerous graduate students and mentors undergraduate research through REU programs. Active in professional service, including organizing workshops on quantum mathematics and algebraic graph theory.

Magda Peligradمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Processes
• Limit Theorems
• +۲ مورد دیگر

Magda Peligrad is the Charles Phelps Taft Professor in the Department of Mathematical Sciences at the University of Cincinnati. She specializes in probability theory and stochastic processes, particularly limit theorems for dependent structures. Her research has been continuously supported by NSF and NSA grants since 1996. Education: Ph.D. from Center of Statistics of the Roumanian Academy of Science (1981) Her research focuses on modeling dependence structures through martingale approximation techniques and spectral analysis of stochastic processes. This work bridges probability theory, statistics, and ergodic theory, with applications to random matrix theory and Gaussian approximations. Her publications demonstrate consistent focus on limit theorems, Markov processes, and dependence structures across random fields. Recent work emphasizes non-stationary systems and refined convergence rates for complex stochastic systems. Major Awards: Fellow of Institute of Mathematical Statistics (1995) Distinguished Research Taft Professor (2003) University of Cincinnati Graduate Fellow (2017) As primary advisor, she has guided 8 PhD students and hosted 2 postdoctoral researchers. Current grant support includes NSF funding for 'Asymptotic results for stochastic processes via new projective methods' (2021-2024).

Jens Lechtenbörgerمشاهده پروفایل

مدرس

• Open Educational Resources (OER)
• Data Warehousing
• Business Intelligence
• +۴ مورد دیگر

Dr. Jens Lechtenbörger is a Faculty member and Lecturer at the Chair of Machine Learning and Data Engineering at the University of Münster's School of Business and Economics. He holds a Doctoral degree (2001) and a diploma in Computer Science (1997) from the University of Münster and Oldenburg, respectively. His roles include serving as Akademischer Oberrat since 2006, Acting Professor (2016), and Fellow for innovation in digital university teaching (2016–2017). He leads projects like Tutor.AI and OER infrastructure development, focusing on Open Educational Resources (OER), data warehousing, and educational technology. Research interests span OER creation, data integration, business intelligence, and machine learning applications in education. He has published extensively on data warehouse design, database systems, and educational technologies. Notable awards include the Best Evolutionary & Swarm Computation Paper Award (2019) and the Dissertation Prize 2003. Teaching responsibilities include courses on data structures, operating systems, and management information systems. He supervises theses on topics like AI-driven learning systems, data privacy, and business logic vulnerabilities. Active in academic service, he chairs committees on digital teaching and curriculum reform at the University of Münster.

Yifan Sunمشاهده پروفایل

استادیار

• Optimization Algorithms
• Machine Learning
• Scientific Computing
• +۱۰ مورد دیگر

Yifan Sun is an Assistant Professor in the Department of Computer Science at Stony Brook University. She is also affiliated with the AI Institute and the Institute of Advanced Computational Science (IACS) at Stony Brook. Dr. Sun received her PhD in Electrical Engineering from UCLA in 2015, with research focusing on convex optimization and semidefinite programming. Prior to joining Stony Brook, she worked at Technicolor Research and Innovation on machine learning applications and completed postdoctoral research at the University of British Columbia in Vancouver and INRIA in Paris. Her research centers on the design and analysis of optimization algorithms, particularly those arising in large-scale machine learning and scientific computing. Dr. Sun studies how structural properties like sparsity, curvature, and decomposability can be exploited to design faster, more stable, and more interpretable optimization methods. Her work spans both theoretical foundations and practical applications, covering first-order methods, quasi-Newton techniques, and algorithms for convex programming to nonconvex deep models. She also investigates how optimization theory, particularly insights from linear algebra and geometry, can improve understanding of deep learning models, regularization techniques, and representation learning. Dr. Sun's recent publications demonstrate strong trends in large-scale graph learning, advanced optimization techniques (particularly Frank-Wolfe variants), and applications to natural language processing. Her work often bridges theoretical guarantees with practical implementations, as evidenced by her open-source code repositories. She leads the OptML Research lab at Stony Brook, focusing on optimization methods for machine learning challenges. Her publications appear in top venues including NeurIPS, ICML, CVPR, and various optimization journals, reflecting her interdisciplinary approach that connects computer science, applied mathematics, and engineering disciplines.