جست‌وجوی استاد و پژوهشگر

David I Shumanمشاهده پروفایل

استاد

• Signal Processing on Graphs
• Ranked Choice Voting
• Numerical Linear Algebra
• +۳ مورد دیگر

David I Shuman is a Professor of Data Science and Applied Mathematics at Olin College of Engineering. He holds a Ph.D. and M.S. in Electrical Engineering: Systems from the University of Michigan, and additional M.S. degrees in Applied Mathematics and Engineering-Economic Systems & Operations Research from Stanford University. Education: Ph.D., Electrical Engineering: Systems, University of Michigan, Ann Arbor (2010) M.S., Applied Mathematics, University of Michigan, Ann Arbor (2009) M.S., Electrical Engineering: Systems, University of Michigan, Ann Arbor (2006) M.S., Engineering-Economic Systems & Operations Research, Stanford University (2001) B.A., Economics with Minor in Computer Science, Stanford University (2001) Research Interests: Signal processing on graphs Ranked choice voting Numerical linear algebra Stochastic scheduling and resource allocation Applications in energy, social, biological, and sensor networks, machine learning, medical imaging, genetics, and astrophysics His work focuses on developing graph-based signal processing tools for high-dimensional data, enabling advancements in data compression, statistical analysis, and machine learning regularization. Scientific Awards: 2016 IEEE Signal Processing Magazine Best Paper Award Mathematical Association of America Project NExT Fellow (2014-2015) University of Michigan College of Engineering Distinguished Achievement Award (2010) Teaching: Quantitative Engineering Analysis (QEA) Probabilistic Modeling (ProbMod)

Noah Williamsمشاهده پروفایل

دانشیار

• Probability Theory
• Random Polynomials
• Random Matrices
• +۵ مورد دیگر

Noah Williams is a tenured Associate Professor in the Department of Mathematical Sciences at Appalachian State University, part of the College of Arts and Sciences. He has completed seven years of service as of fall 2025, actively contributing to both teaching and research in probability and its applications. Research Interests: Dr. Williams specializes in probability theory , particularly focusing on random polynomials and random matrices . His work investigates the asymptotic behavior of roots and critical points, pairing phenomena, and eigenvalue statistics. He also explores combinatorial problems arising in genome rearrangement, applying probabilistic and discrete methods to model evolutionary distance. Recent Research Trends: His recent publications reveal a strong focus on the interplay between complex analysis and probability, especially in the context of random polynomials. He frequently collaborates with Sean O'Rourke, producing rigorous results on the Gauss-Lucas theorem and local root-critical point pairing. A secondary thread involves discrete-to-continuous transitions in arithmetic progressions and genome evolution models under operations like DCJ and deletion-insertion. No scientific awards or honors are mentioned in the available text. Teaching & Academic Service: Dr. Williams teaches a wide range of courses including Calculus I, Techniques of Proof, Linear Algebra, and graduate-level Computational Probability. He previously served in multiple instructional roles at the University of Colorado Boulder, including Instructor of Record, Assistant Coordinator for large calculus sections, and Lead Graduate Teacher for pedagogical training. His teaching philosophy incorporates Inquiry-Based Learning (IBL), supported by resources from established IBL initiatives. Laboratories & Research Teams: While no formal lab is mentioned, Dr. Williams is part of a collaborative research network, primarily with Sean O'Rourke (University of Colorado Boulder), and has co-authored work with other mathematicians such as T. Greenwood, J. Kariv, A. Brisbin, M. Riehl, and J. Christy. These collaborations suggest an active research group or team in probabilistic combinatorics and random matrix theory.

Scott Kaschnerمشاهده پروفایل

استادیار

• Complex Dynamics
• Julia Sets
• Rational Maps
• +۱۱ مورد دیگر

Scott Kaschner is an Assistant Professor in the Department of Mathematics and Actuarial Science at Butler University, College of Liberal Arts and Sciences. His work bridges pure mathematics, applied mathematical biology, and mathematics education. He is actively engaged in research and undergraduate teaching, with a diverse portfolio of scholarly output. Education: Ph.D. in Mathematics, Department of Mathematical Sciences, IUPUI, 2013 M.S. in Theoretical Mathematics, University of Akron, 2008 B.S. in Theoretical Mathematics, University of Akron, 2003 His research interests span Complex Dynamics , particularly Julia sets, rational maps, and bicomplex analysis; Mathematical Virology , including modeling of coronavirus and respiratory syncytial virus (RSV) replication; and Mathematics Education , with focus on student success in introductory courses, assessment design, and interdisciplinary co-teaching. His work reflects a strong interdisciplinary approach, combining deep mathematical theory with real-world applications in biology and pedagogy. The 15 most recent publications reveal a consistent trajectory across three domains: foundational work in complex dynamics and operator theory, applied modeling in virology, and scholarship of teaching and learning in mathematics. The keywords and sub-fields reflect a sophisticated blend of pure and applied mathematics, with increasing engagement in biological modeling and educational research. Scientific Awards: No awards explicitly mentioned in the text. Scott Kaschner has advised or collaborated with numerous students and researchers, particularly evident in his co-authored papers in virology and mathematics education. His involvement in the NSF GK-12 program as a fellow indicates a long-standing commitment to STEM outreach and pedagogical development. He has secured research support through collaborative grants, especially in virology and education. He co-authored an open textbook on linear transformations, contributing to accessible educational resources. He is involved in interdisciplinary research teams, particularly in virology (with Christopher Stobart and others) and mathematics education (with Aubrey Neihaus and others). His work on virus modeling suggests collaboration with biologists, and his educational research involves partnerships across disciplines. These teams reflect a commitment to collaborative, cross-cutting scholarship.

Keith Howardمشاهده پروفایل

استاد

• K-12 Computer Science Education
• Equity in STEM Education
• Career and Technical Education (CTE)
• +۷ مورد دیگر

Dr. Keith Howard is a Professor and Director of the Ph.D. in Education Program at the Attallah College of Educational Studies, Chapman University. He is a prominent scholar in K-12 computer science education, equity in STEM access, and career and technical education research. His work integrates technology, ethics, and social justice to improve educational outcomes, particularly for underrepresented students. Dr. Howard earned his Ph.D. in Educational Psychology and Technology from the University of Southern California, an MA in Teaching and Curriculum from California State University, Dominguez Hills, and a BA in Business Administration from California State University, Long Beach. He has taught at USC, UCLA, CSUDH, and Chapman University, covering subjects such as educational psychology, teacher education, and quantitative statistics. His research interests focus on technology integration in education, equity in mathematics and STEM, and ethical issues in K-12 schooling. He has conducted extensive work on schema-based mathematics instruction, metacognition, and working memory. A significant portion of his recent scholarship examines disparities in CTE outcomes and access to advanced computer science courses like AP Computer Science Principles. His publications reveal a consistent trend toward analyzing structural inequities in education using national datasets. He frequently investigates how race, gender, and socioeconomic status influence access to and outcomes in STEM and CTE programs. His work often emphasizes practical implications for policy and teaching practices aimed at fostering inclusion. Career and Technical Education’s Unequal Dividends for High School Students (2022) Advanced Placement (AP) Computer Science Principles: Searching for Equity in a Two-Tiered Solution to Underrepresentation (2019) Let’s talk: An examination of parental involvement as a predictor of STEM achievement in math for high school girls (2019) Using Tablet Technologies to Engage and Motivate Urban High School Students (2017) Success after Failure: Academic effects and psychological implications of early universal algebra policies (2015) Dr. Howard has received no explicitly mentioned scientific awards in the provided text. He previously served as a senior research associate at UCLA’s CRESST, where he co-led professional development for a major IES-funded study on middle school mathematics. He has conducted professional development for teachers across Southern California and Arizona. He has advised or collaborated with several researchers, including Nicol R. Howard, Douglas D. Havard, and others, though formal advisees are not listed. He has also served as co-editor of the Journal of Computer Science Integration. Dr. Howard has led multiple research and evaluation projects, including the Chapman-Orange High School iPad Program Evaluation and assessments of independent learning centers. His work bridges academic research with practical program evaluation in K-12 settings.

Alex J Feingoldمشاهده پروفایل

استاد

• Algebra
• Lie Algebras
• Kac-Moody Algebras
• +۷ مورد دیگر

Alex J Feingold is a Professor in the Department of Mathematics and Statistics at Binghamton University, where he has taught since 1979. His research focuses on algebraic structures such as Kac-Moody Lie algebras, vertex operator algebras, and conformal field theory, with notable contributions to hyperbolic Weyl groups and fusion rules. Education: Ph.D., Yale University (1977) He teaches foundational and advanced courses like Linear Algebra (Math 304) and Advanced Linear Algebra (Math 404), often serving as course coordinator. His scholarly work includes collaborations with institutions like the Albert Einstein Institute and IHES, and he has supervised multiple Ph.D. students. Research Trends: His publications span hyperbolic Kac-Moody algebras, modular forms, and mathematical physics, often intersecting with representation theory and combinatorics. Recent work includes decomposition of Kac-Moody algebras and fusion rule theorems. Advising: He actively advises honor societies Phi Beta Kappa and Pi Mu Epsilon at Binghamton University, organizing induction ceremonies. Labs/Projects: Known for mathematical sculptures, he has exhibited works at venues like the Roberson Museum and the University of South Florida, blending art with hyperbolic geometry and knot theory.

Ilse C.F. Ipsenمشاهده پروفایل

استاد

• Numerical Linear Algebra
• Randomized Algorithms
• Probabilistic Numerical Analysis
• +۸ مورد دیگر

Ilse C.F. Ipsen is a Distinguished Professor in the Department of Mathematics at North Carolina State University, with a joint appointment in the Department of Statistics. She is a Fellow of the American Mathematical Society and the Society for Industrial and Applied Mathematics (SIAM), and delivered the Olga Taussky-Todd Lecture at ICIAM 2023. Institution: North Carolina State University School: College of Sciences Department: Department of Mathematics Rank: Professor Email: ipsen@ncsu.edu Her research centers on numerical linear algebra, randomized algorithms, and probabilistic numerical analysis. She has made significant contributions to error analysis in floating-point computation, matrix perturbation theory, and the development of randomized methods for large-scale data problems. Her work bridges theoretical foundations with practical applications in data science, quantum physics, and computational statistics. She is the author of the textbook Numerical Matrix Analysis (SIAM, 2009) and serves as Editor-in-Chief of the SIAM Book Series on Data Science. The recent publications highlight a strong trend toward probabilistic numerical methods, uncertainty quantification, and randomized matrix algorithms. Themes include error analysis for inner products and summation, Bayesian solvers for linear systems, and scalable methods for trace and diagonal estimation. Applications span biobank data analysis, quantum physics, and PageRank computation. Scientific Awards: Fellow of the American Mathematical Society (AMS) Fellow of the Society for Industrial and Applied Mathematics (SIAM) Olga Taussky-Todd Lecture, ICIAM 2023 She has advised multiple graduate and undergraduate students, including J.T. Holodnak, T. Wentworth, and R. Rehman. Her collaborative research has been supported by interdisciplinary grants, particularly in quantum physics and data-intensive computing. She has served on editorial boards of leading journals such as SIAM Review , Acta Numerica , and Numerische Mathematik , and chaired SIAM activity groups, reflecting her leadership in the applied mathematics community. She is actively involved in the development of software and algorithms, including the MATLAB package kappa_SQ for randomized sampling, and has contributed to discussions on communicating applied mathematics through case studies.

Jason P. Smithمشاهده پروفایل

مدرس ارشد

• Combinatorics
• Algebraic Topology
• Topological Data Analysis
• +۷ مورد دیگر

Jason P. Smith is a Senior Lecturer in Mathematics within the Department of Physics and Mathematics at Nottingham Trent University. His research bridges combinatorics, algebraic topology, and neuroscience, focusing on the application of topological data analysis to understand neural connectivity and brain function. His educational background includes a Ph.D. in Computer and Information Science from the University of Strathclyde (2012–2015), supervised by Einar Steingrímsson, and an MMath from the University of Bath (2008–2012). Smith's research interests center on combinatorics, particularly permutation patterns and posets, and algebraic topology, especially persistent homology and directed flag complexes. He applies these mathematical tools to neuroscience, modeling cortical connectivity and neural coding strategies. His work often involves developing computational methods for analyzing complex networks. The recent publications reflect a strong trend toward interdisciplinary research combining topology, combinatorics, and neuroscience. His work explores how the structure of neural networks influences information processing, using tools like tournaplexes and flag complexes to extract topological features from connectomic data. Email: jason.smith@ntu.ac.uk Google Scholar GitHub ORCID CV He has developed several open-source software packages including Tournser, Flagser-count, and Deltser, which are used for computing persistent homology in directed networks. He previously held research positions at the University of Aberdeen (2018–2020) and the University of Strathclyde (2015–2018), working on topological analysis of neural systems and combinatorial algebraic topology. No formal advising of students is listed, nor are any grants or scientific awards mentioned in the provided text. Smith is actively involved in the neurotopology research community and contributes to computational tools for topological data analysis in neuroscience. He is part of collaborative projects involving institutions such as the University of Aberdeen and international neuroscience initiatives.

Hoi Nguyenمشاهده پروفایل

استاد

• Combinatorics
• Probability
• Random Matrices
• +۳ مورد دیگر

Hoi Nguyen is a Professor of Mathematics at The Ohio State University (OSU), specializing in Combinatorics, Probability, and Random Matrices. His research explores areas such as inverse Littlewood-Offord problems, random matrix universality, and stochastic processes. He holds a PhD from Rutgers University (2010) and has held postdoctoral positions at the University of Pennsylvania, Yale University, and the Institute for Advanced Study. His work is supported by NSF grants and a Simons Foundation Fellowship. Recent projects include studies on random matrix cokernels, eigenvalue gaps, and trigonometric polynomials' real roots. He teaches graduate courses in Probability and Combinatorics at OSU. Education: PhD in Mathematics, Rutgers University, 2010. Research interests focus on probabilistic combinatorics, random matrix theory, and stochastic models. Key themes include universality phenomena in random structures, anti-concentration inequalities, and applications to algebraic problems. His work bridges combinatorial methods with probabilistic analysis, addressing foundational questions in matrix theory and polynomial root distribution. Grants include NSF CAREER Award (2018-2024) and Simons Fellowship (2023-2024). His recent articles investigate universality in random matrix cokernels, rank fluctuations in matrix products, and exponential concentration in root counts. He organizes workshops on random polynomials and matrices, reflecting his leadership in these fields.

Alexander Glazmanمشاهده پروفایل

دانشیار

• Probability Theory
• Mathematical Physics
• Statistical Mechanics
• +۷ مورد دیگر

Alexander Glazman is an Associate Professor at the University of Innsbruck , leading the FWF-funded project on Order-disorder phase transitions in 2D lattice models . His research focuses on the intersection of Probability Theory and Mathematical Physics, particularly studying phase transitions and critical phenomena in models like the Loop O(n), Six-Vertex, Ashkin-Teller, and Ising models. Education : PhD in Mathematics, University of Geneva (2016), supervised by Stanislav Smirnov. Candidate of Physico-Mathematical Sciences, St. Petersburg State University (SPbU) and PDMI (under Dmitry Karpov). Master's degree in Mathematics, St. Petersburg. Research Interests : Glazman investigates statistical mechanics models, emphasizing rigorous analysis of phase transitions, loop models, and critical phenomena. Key areas include: Loop O(n) and Six-Vertex models on hexagonal/triangular lattices. FK-percolation, Ashkin-Teller model, and their connections to random-cluster representations. Height functions, interface fluctuations, and conformal invariance. Self-avoiding walks and their universality. Recent Work Trends : His articles address interface convergence in Potts models, delocalization in Lipschitz functions, and phase diagrams of Ashkin-Teller and six-vertex models. Collaborations emphasize techniques like coupling methods, renewal theory, and Benjamini-Schramm limits. Grants & Projects : Principal Investigator (PI) of FWF Stand-Alone Grant P 34713 (€399k), studying 2D lattice models' phase transitions. Labs/Teams : Active in the Department of Mathematics at Innsbruck, contributing to probabilistic and mathematical physics research groups.

Eric Millerمشاهده پروفایل

استاد

• Signal and Image Processing
• Tomographic Image Formation
• Inverse Problems
• +۴ مورد دیگر

Eric Miller is a Professor of Electrical and Computer Engineering at Tufts University's School of Engineering. He also holds adjunct professorships in Computer Science, Biomedical Engineering, and Mathematics. His academic roles include serving as Chair of the Electrical and Computer Engineering department and leading the Lab for Imaging Science Research (LaISR). Miller earned his SB, SM, and PhD in Electrical Engineering from MIT (1990–1994). His research focuses on signal and image processing, particularly inverse problems, tomographic imaging, and applications in medical imaging, environmental monitoring, and security screening. He has pioneered methods like the parametric level-sets (PaLEnTIR) for reconstruction and shape-based inversion algorithms. His work integrates physics-based modeling with computational techniques, addressing challenges in subsurface sensing, biomedical diagnostics, and materials science. Miller is a Fellow of IEEE and a member of honor societies like Tau Beta Pi and Phi Beta Kappa. Over 489 publications reflect his contributions to imaging science, with recent advancements in AI-driven pedestrian behavior analysis and X-ray anomaly detection.

Ida Friestad Pedersenمشاهده پروفایل

دانشیار

• Mathematics Didactics
• Exploratory Mathematics Teaching
• Formative Assessment
• +۴ مورد دیگر

Ida Friestad Pedersen is an Associate Professor in the Department of Mathematics and Statistics at UiT The Arctic University of Norway. She teaches beginner mathematics courses and supervises master's and PhD students in subject didactics. She serves as the program chair for Associate Professor in Science grades 8-13, demonstrating her commitment to teacher education across multiple educational levels. Her research focuses on mathematics didactics, with special emphasis on exploratory mathematics teaching , formative assessment , student-active teaching methods , and the transition from school to university . Her work bridges theory and practice, connecting classroom observations with educational research to improve mathematics instruction. She investigates how inquiry-based approaches affect students' mathematical beliefs and motivation, and examines the challenges students face when transitioning between educational levels. Her recent publications demonstrate a strong focus on the SUM project, which examines inquiry-based mathematics teaching and its effects on both teachers and students. Her research shows how exploratory teaching methods can be effectively implemented, how to assess students' mathematical beliefs and motivation, and how to improve the continuity between secondary school and university mathematics education. Her scientific contributions include: Development of survey instruments for assessing mathematical beliefs and motivation Analysis of exploratory teaching methods and their implementation Research on the transition challenges between school and university mathematics Studies on formative assessment and student-active learning approaches Investigations into students' choices of learning resources in STEM education Dr. Pedersen is an active member of the Mathematics Didactics research group and the Science Didactics in Higher Education group at UiT, where she contributes to advancing research in mathematics education. Her teaching portfolio includes courses for engineering students (Mat-1052, Mat-1060, Mat-2050), teacher education (MAT-6101), and preparatory programs (Tek-0002), demonstrating her versatility across different educational contexts and student populations.

• Large-scale Scientific Computing
• Computational Linear Algebra
• Parallel and Distributed Processing
• +۴ مورد دیگر

Professor Efstratios Gallopoulos is a faculty member at the Department of Computer Engineering & Informatics , University of Patras, where he holds the Division of Computer Software . He currently serves as Deputy Department Chair and Director of the High Performance Information Systems Laboratory (HPCLab) . His academic career spans multiple institutions including the University of Illinois at Urbana-Champaign, University of California Santa Barbara, and collaborations with INRIA Rennes and NASA Goddard Space Flight Center. Education : B.Sc. in Mathematics (First Class Honours) from Imperial College London (1979) Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (1985) Research Focus : His work centers on Large-scale Scientific Computing with emphasis on Computational Linear Algebra , Parallel/Distributed Processing , and Data Mining . Recent publications highlight innovations in Randomized Numerical Linear Algebra , Heterogeneous Cluster Scheduling , and GPU-Accelerated Inversion Techniques . Article Trends : His research bridges High-Performance Computing with Data Science , focusing on scalable algorithms for Matrix Computations , Recommender Systems , and Biomarker Analysis . The work spans theoretical advancements (e.g., Givens Rotations ) and practical implementations (e.g., pylspack library). Scientific Awards : NASA Group Achievement Award for Massively Parallel Processor (MPP) development ACM SIGWEB Hypertext Ted Nelson Newcomer Award (2012) Advising and Grants : He has advised numerous research projects funded by European Research Council , Hellenic Foundation for Research and Innovation (HFRI) , and international bodies like the US National Science Foundation. Notably, he co-organized the 2015 Gene Golub SIAM Summer School and served as Chair of the SIAM Gene Golub Summer School Committee (2020-24). Labs and Teams : He leads the High Performance Information Systems Laboratory (HPCLab) and co-directs the interdisciplinary graduate program Data Driven Computing and Decision Making . His teams have contributed to the Cedar vector multiprocessor project at UIUC and Text-to-Matrix Generator (TMG) tools for data mining.

• Mathematics
• Graph Theory
• Profinite Groups
• +۳ مورد دیگر

Amrita Acharyya is a Lecturer at the University of Toledo , affiliated with the College of Natural Sciences and Mathematics and the Department of Mathematics and Statistics . Her research focuses on abstract algebra, graph theory, and profinite structures. Full Name: Amrita Acharyya University: University of Toledo School: College of Natural Sciences and Mathematics Department: Department of Mathematics and Statistics Academic Rank: Lecturer Research Interests : Acharyya's work centers on cofinite graphs, profinite completions, and their applications to group theory. She explores connections between infinite graphs, topological structures, and algebraic systems. Her recent publications examine profinite groupoids and hybrid computational methods in rotor dynamics, suggesting interdisciplinary interests. Publication Trends : Her scholarly output spans pure mathematics (group theory, graph theory) and applied engineering domains (aerodynamics, computational fluid dynamics), with a focus on cofinite/profinite structures in theoretical frameworks. Advising & Grants : No formal advisees or grant funding are documented in available VIVO records.

• Mathematics Education
• Calculus Pedagogy
• Algebraic Reasoning
• +۴ مورد دیگر

Rob Ely serves as Professor in the Department of Mathematics and Statistical Science within the College of Science at the University of Idaho. His academic career focuses on advancing mathematics education through research connecting historical mathematical developments with contemporary pedagogical practices, particularly in calculus and algebra instruction. His educational qualifications include a PhD in Curriculum and Instruction (2007) and an MA in Mathematics (2003) from the University of Wisconsin-Madison, complemented by a BA in History and Mathematics (2000) from the University of Northern Colorado. This interdisciplinary background informs his approach to mathematical cognition and curriculum design. Professor Ely's research centers on student reasoning in calculus and algebra, with emphasis on covariational reasoning, infinitesimal approaches to calculus, and the development of proof concepts in middle grades. He investigates how historical perspectives on mathematical concepts like infinitesimals can enhance modern teaching methods, while exploring playful and activity-based approaches to deepen conceptual understanding. His work consistently addresses challenges in student interpretation of graphs, functions, and mathematical arguments. Analysis of his 2018-2025 publications reveals three dominant research strands: (1) innovative calculus pedagogy using infinitesimals and accumulation concepts, (2) algebraic reasoning through scaling and covariation in K-12 contexts, and (3) historical foundations of calculus intersecting with student cognition. His recent editorial work on definite integrals and playful math activities demonstrates commitment to translating research into practical teaching strategies. No scientific awards were documented in the source materials. Information regarding student advising, research grants, or laboratory affiliations was not provided in the available texts, though his publication record suggests active mentorship in mathematics education research.

• Mathematics Education
• Mathematical Problem Solving
• STEM Education
• +۵ مورد دیگر

Assoc. Prof. Dr. Okan KUZU is an academic at Kırşehir Ahi Evran University Faculty of Education, Department of Mathematics Education . Holding a Ph.D. in Mathematics Education from Atatürk University (2011) and Mathematics from Ahi Evran University (2014), he has served in various academic and administrative roles since 2011, including Dean Assistant (2025-), Research Center Director (2024-), and Department Head (2023-2025). Ph.D. in Mathematics Education (2011, Atatürk University) Ph.D. in Mathematics (2014, Ahi Evran University) M.Sc. in Mathematics (2011, Ahi Evran University) B.Sc. in Secondary Science and Mathematics Education (2009, Atatürk University) His research focuses on mathematics curriculum analysis, mathematical problem-solving, statistical reasoning, and educational technology. Recent work examines Türkiye Yüzyılı Maarif Modeli curricula, fraction problem-posing, and digital game-based learning. A 2025 article compares Turkey's 2018/2024 primary math curricula, while a 2023 study links teacher attitudes to emotional states. Scientific Recognition : TÜBİTAK UBYT Awards (2015, 2020) Co-authored 34 journal articles Supervised 14 theses (2019-2025)