جست‌وجوی استاد و پژوهشگر

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +۱۲ مورد دیگر

Bryon Aragam is an Associate Professor and Topel Faculty Scholar at the Booth School of Business , University of Chicago . His work bridges causality , statistical machine learning , and probabilistic modeling , with applications in AI systems like ChatGPT and DALL-E. Key research themes include: Causal Structure Learning : Extracting latent causal graphs from multimodal data using nonparametric methods. Deep Generative Models : Analyzing overparametrization and variational inference for representation learning. Latent Variable Discovery : Using Markov boundaries and convex subset lattices to uncover hidden dependencies. Algorithm Design : Developing scalable methods like DAGMA for DAG learning and theoretical guarantees for GES/PC algorithms. His paper trends reveal a focus on nonparametric statistics , graphical models , and neural network theory , with recent work on transformer memory dynamics and identifiability in deep latent models . Papers frequently appear in top venues like NeurIPS , JMLR , and AOS , emphasizing theoretical rigor and practical validation.

Serge Provostمشاهده پروفایل

استاد

• Multivariate statistical analysis
• Probabilistic models in environmental sciences
• Serial correlation
• +۷ مورد دیگر

Serge Provost is a Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. His office is located in WSC 280, and he can be contacted via phone (519-661-2111 x83624) or email (provost@uwo.ca). Research Focus: Professor Provost specializes in multivariate statistical analysis, distribution theory, and probabilistic modeling with applications in environmental sciences and actuarial science. His work emphasizes: Development of approximation methods for complex distributions Innovations in density estimation techniques Statistical inference for incomplete data Serial correlation analysis Functional linear modeling frameworks Publication Trends: His recent articles (2011-2016) predominantly focus on theoretical and computational statistics, with recurring themes in distribution theory, orthogonal polynomial approximations, and specialized probability models. Research frequently intersects with actuarial mathematics, time series diagnostics, and nonparametric estimation methods. Graduate Supervision: Currently advises three PhD students: Cong Nie Zhaoqi Yang Yishan Zang

Valentin Blomerمشاهده پروفایل

استاد

• Number Theory
• Analytic Number Theory
• Automorphic Forms
• +۲ مورد دیگر

Valentin Blomer is a Professor of Mathematics at the University of Bonn, where he has been employed since 2019. Previously, he held professorships at the University of Göttingen (2009-2019) and the University of Toronto (2005-2009). His primary research focuses on number theory, particularly analytic number theory, automorphic forms, L-functions, and quadratic forms. He leads the research group 'Analytic Number Theory and Automorphic Forms' at the University of Bonn and organizes the Oberseminar Analytic Number Theory and Automorphic Forms, which features regular talks by leading researchers in the field. Blomer's research has significantly advanced our understanding of density theorems for GL(n), bounds for Kloosterman sums, the Manin-Peyre conjecture for spherical Fano varieties, and spectral theory of automorphic forms. His work demonstrates deep connections between analytic number theory, harmonic analysis, and algebraic geometry. Heinz Maier-Leibnitz Preis (2005) André Aisenstadt Prize (2009) Ribenboim Prize (2010) Elected to Academia Europaea (2019) Gauß lecture (2021) Teaching award at University of Bonn (2022) ICM Speaker (2026) His research has been supported by numerous prestigious grants including an ERC Advanced Grant (2022), ERC Starting Grant (2010), Sloan Fellowship (2008), and multiple DFG-SNF collaborative grants. He serves on the editorial boards of International Mathematics Research Notices, Journal of the Institute of Mathematics of Jussieu, and Essential Number Theory. Notably, beyond his mathematical research, Blomer is also an accomplished classical pianist with professional training and numerous performances across Europe and North America.

• Numerical Linear Algebra
• Partial Differential Equations
• Integral Equations
• +۷ مورد دیگر

Difeng Cai is an Assistant Professor in the Department of Mathematics at Southern Methodist University. His research focuses on developing efficient algorithms for numerical linear algebra, partial differential equations, and integral equations, with applications spanning physics, statistics, machine learning, and deep learning. University: Southern Methodist University Department: Mathematics Academic Rank: Assistant Professor Professor Cai's work addresses computational challenges in scientific computing and data science by leveraging mathematical properties to design scalable algorithms. His recent interests involve integrating deep learning techniques, such as generative models, with traditional numerical methods to solve complex scientific problems. His research trends highlight interdisciplinary applications of numerical linear algebra and machine learning in scientific computing. Publications appear in venues like Numerical Linear Algebra and Its Applications , Numerische Mathematik , and SIAM Journal on Scientific Computing , emphasizing algorithm design, error estimation, and kernel matrix approximation.

• Probability Theory
• Random Spatial Processes
• Last Passage Percolation
• +۶ مورد دیگر

Prof Nicos Georgiou is a Professor of Probability Theory and Applications at the University of Sussex, within the School of Mathematical and Physical Sciences. He holds a PhD in Mathematics from the University of Wisconsin-Madison and has held academic positions including Reader, Senior Lecturer, and Lecturer at Sussex, as well as a postdoctoral role at the University of Utah. His research focuses on Probability Theory, with emphasis on random spatial processes, last passage percolation, and applications in queueing theory, epidemiology, and econophysics. Education: PhD in Mathematics, University of Wisconsin-Madison (2011) MA in Mathematics, University of Wisconsin-Madison (2008) BSc in Mathematics, National and Kapodistrian University of Athens (2006) Research Interests: Probability Theory fundamentals and applications Last Passage Percolation (LPP) and its connections to particle systems like TASEP Queueing models with Mittag-Leffler inter-event times Epidemic modeling on complex networks Continuum and thermodynamic limits for wealth-distribution models Key Publications (2020–2025) span topics like exact social contagion dynamics, Mittag-Leffler queuing systems, and TASEP on Galton-Watson trees. His work bridges theoretical insights with real-world applications in traffic modeling, economics, and public health. Grants include EPSRC funding for studying last passage percolation and LMS support for random media research. He has organized conferences and presented at international venues such as BIRS and INI. Teaching responsibilities include modules like Discrete Mathematics and Introduction to Probability, with supervision of undergraduate and graduate projects on topics like sequence alignment and traffic flow models.

Rocío Melissa Riveraمشاهده پروفایل

استاد

• Reproductive Physiology
• Epigenetics
• Assisted Reproductive Technologies
• +۳ مورد دیگر

Rocío Melissa Rivera is a Professor of Reproductive Physiology and Epigenetics in the Division of Animal Sciences at the University of Missouri. She received her Ph.D. from the University of Florida and completed postdoctoral training at the University of Pennsylvania. Her research investigates epigenetic disruptions in gametes and embryos caused by assisted reproductive technologies (ART). Key projects characterize Large Offspring Syndrome in ruminants and Beckwith-Wiedemann Syndrome in humans—both loss-of-imprinting overgrowth conditions linked to ART. She explores how superovulation and maternal aging alter oocyte DNA methylation patterns and gene expression. Dr. Rivera employs bovine and murine models to identify molecular triggers of epigenetic syndromes, using transcriptomic and chromatin analysis. Current work examines dietary interventions to mitigate ART-associated epigenetic errors. She mentors doctoral students in projects ranging from IGF2R regulation in fetal overgrowth to retroelement control in oocytes. As a Fulbright Senior Scholar at Spain's University of Murcia, she contributed to reproductive biology education. Her laboratory combines developmental biology, epigenetics, and molecular analysis to improve ART safety.

Ron Evansمشاهده پروفایل

استاد

• Number Theory
• Finite Fields
• Character Sums
• +۴ مورد دیگر

Ronald J. Evans is a Professor of Mathematics at the University of California, San Diego (UCSD), affiliated with the Department of Mathematics within the Division of Physical Sciences. His academic career includes roles such as Graduate Vice Chair (2011-2013). He holds a Ph.D. in Mathematics from the University of Illinois (1974). His research focuses on Number Theory, Finite Fields, and Character Sums, with significant contributions to Gauss and Jacobi Sums, as detailed in his influential book *GAUSS AND JACOBI SUMS* (1998). His work integrates algebraic, analytic, and combinatorial methods, addressing topics like hypergeometric functions over finite fields, Pfaffians, and Toeplitz matrices. Evans has authored over 120 publications, including studies on Kloosterman sums, hypergeometric identities, and combinatorial matrix theory. His book has been reviewed in leading journals like *Mathematical Reviews* and *Zentralblatt*. Beyond academia, he is a violist with the Fine Arts Quartet, collaborating with musicians like Ralph Evans. He advocates against academic boycotts of Israel, emphasizing the importance of scientific collaboration over political strife. Key contributions include resolving sign ambiguities in Jacobi sums, analyzing character sums in finite fields, and exploring algebraic structures in quadratic forms. His recent work includes studies on nullities of Toeplitz matrices and class field theory applications. Despite no explicitly listed students or grants, his research has impacted algebraic number theory, combinatorics, and linear algebra.

Evgenia Smirniمشاهده پروفایل

استاد

• Performance Evaluation
• Reliability and Dependability
• Applied Machine Learning
• +۸ مورد دیگر

Evgenia Smirni is the Sidney P. Chockley Professor of Computer Science at the College of William and Mary, leading research in performance evaluation, dependability, and applied machine learning. She holds prestigious titles as an IEEE Fellow and ACM Distinguished Scientist. Research Interests : Her work spans performance analysis, reliability engineering, analytical modeling, HPC systems, and cloud computing. Notable projects include GeoSpread for epidemic modeling and strategic resilience evaluation of neural networks in autonomous systems. Awards : Recognized as an IEEE Fellow (202X) and ACM Distinguished Scientist (202X), she also serves as an Associate Editor for IEEE Transactions on Computers and IEEE Transactions on Parallel and Distributed Systems. Service & Leadership : Recent roles include General Chair of SIGMETRICS 2023, Keynote Speaker at DSN 2023, and co-chair of multiple international conferences including DSN 2017 and HPDC 2019. She has organized panels on future research directions in performance evaluation. Software Tools : Developed widely-used tools like KPC ToolBox for modeling and MAMSolver for matrix-analytic solutions. Advising : Supervised over 20 graduate students, many now in prominent academic and industry roles. Notable advisees include Lishan Yang (George Mason), Feng Yan (University of Houston), and Giuliano Casale (Imperial College London).

Bo Friis Nielsenمشاهده پروفایل

استاد

• Queueing Theory
• Matrix Analytic Methods
• Stochastic Processes
• +۳ مورد دیگر

Bo Friis Nielsen is a Professor and Head of Section in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU). His research focuses on queueing theory, particularly matrix-analytic methods, with applications in telecommunication systems and service optimization. He currently supervises PhD students working on projects with Danske Bank (call center efficiency) and DSB S-tog (passenger counting for transport planning). His teaching includes probability theory and stochastic simulation. Education/Background: Formal academic qualifications not explicitly listed in the text. Affiliations: Head of the Mathematical Modelling Section since 2003, affiliated with DTU Compute. Research interests span theoretical advancements in queueing models and practical applications in transportation, healthcare, and industrial systems. He has collaborated with companies like Danske Bank and DSB S-tog, addressing real-world operational challenges. Recent work includes predictive maintenance strategies for multi-component systems and railway defect analysis. Publications emphasize stochastic modeling, maintenance optimization, and transportation analytics. Projects involve developing tools for public transport efficiency and reliability engineering in wind turbines and power plants.

José R. Herreroمشاهده پروفایل

دانشیار

• High Performance Computing
• Parallel Computing
• Dense Linear Algebra Algorithms
• +۳ مورد دیگر

José R. Herrero is an Associate Professor in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC), affiliated with the Barcelona School of Informatics (FIB). His primary research area is High Performance Computing (HPC), focusing on parallel algorithms, dense linear algebra, and multicore processor optimization. He has held administrative roles including Vice-Dean for Academic Studies (2010–2013) and Vice-Dean for Institutional and International Relations (2013–2015) at FIB. Education: PhD in Computer Science from UPC (2006), Postgraduate in Training of University Professors (1997), and a Computer Science degree (1993). Teaching experience includes over 15 courses in Computer Architecture, Operating Systems, and Parallel Programming at both undergraduate and graduate levels. Research interests span HPC, parallel computing, and algorithm design. His work emphasizes optimizing matrix computations and linear algebra libraries for modern architectures. Notable contributions include advancements in sparse Cholesky factorization, task-parallel algorithms, and energy-efficient computing. Recognition includes UPC's Teaching Mention for Outstanding Quality (2009, 2014, 2019) and a Certificate of Merit for contributions to scientific computing workshops. He advises numerous PhD students and collaborates internationally, including visiting roles at GeorgiaTech and École Polytechnique de Montréal.

John C. Bowmanمشاهده پروفایل

استاد

• Statistical closures in turbulence
• Spectral Reduction
• 3D vector graphics
• +۱۰ مورد دیگر

John C. Bowman is a Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He has maintained an active teaching career with courses spanning undergraduate to graduate levels, including Honours Calculus (Math 117/118), Real Variables (Math 417), Coding Theory (Math 422), Linear Algebra II (Math 225), and Statistical Theories of Turbulence (Math 655). His office is located in CAB 521, and he has consistently provided regular office hours for student consultation across multiple courses. Dr. Bowman earned his BS in Engineering from the University of Alberta, followed by an MA and PhD from Princeton University. His educational background has informed his diverse research trajectory spanning mathematical physics, computational mathematics, and scientific visualization. His teaching philosophy emphasizes deep conceptual understanding, as evidenced by his development of comprehensive hyperlinked 3D lecture notes for Honours Calculus. His primary research focuses on statistical theories of turbulence, particularly his development of Spectral Reduction as a reduced statistical description of turbulence. His work shows remarkable agreement with full numerical simulations, even in flows containing long-lived coherent structures. Additional research interests include 3D vector graphics (notably the Asymptote language he developed), implicit dealiasing of convolutions, exponential integrators, and exactly conservative integrators for numerical simulations. He has been involved with the University of Alberta's Geophysical Fluid Dynamics Research Group, where his work bridges theoretical mathematics with practical computational applications. Dr. Bowman's publication record demonstrates consistent contributions to computational mathematics and fluid dynamics over several decades. His recent work (2015-2024) shows continued innovation in computational methods for turbulence simulation, with particular attention to dealiasing techniques, efficient convolution algorithms, and novel approaches to spectral methods. His research has practical applications in assessing dissipation mechanisms in large-eddy simulations and modeling high-Reynolds number turbulence. As an educator, Dr. Bowman has developed comprehensive teaching materials and maintains an active engagement with students through office hours and detailed course websites. His commitment to mathematical education extends to high school outreach through contributions to the "Pi in the Sky" mathematics magazine. His work on the Asymptote vector graphics language has created a valuable tool for mathematical visualization and scientific publishing that integrates seamlessly with TeX.

Anurag Sahayمشاهده پروفایل

استادیار مهمان

• Number Theory
• Mathematical Analysis
• Combinatorics
• +۳ مورد دیگر

Anurag Sahay is a Golomb Visiting Assistant Professor of Mathematics at Purdue University, affiliated with the Department of Mathematics within the College of Science. His contact information includes the phone number 765-494-0481 and office MATH 402. He holds the academic rank of Visiting Assistant Professor and is not part-time. His research interests span a broad range of mathematical disciplines, with a focus on analytic number theory, mathematical analysis, and combinatorics. Sahay explores topics such as zeta functions (Riemann and Hurwitz), function fields, quadratic residues, hypergraph Turán problems, and geometric configurations. His work combines theoretical insights with computational methods, addressing questions related to moments of zeta functions, paucity problems in number theory, and distinct distances in combinatorial geometry. Recent publications (2023–2025) highlight his contributions to understanding zeta function moments, shifted convolution problems, and VC dimension applications. These articles reflect a consistent theme of advancing knowledge in analytic number theory and its intersections with algebra, combinatorics, and geometry. No scientific awards or grants are explicitly mentioned in the provided texts. Sahay has no listed advisees or master’s/PhD students at this time. While no specific laboratories or research teams are detailed, his affiliations with Purdue University’s Department of Mathematics suggest involvement in collaborative research environments typical of such institutions.

• Algebraic Geometry
• Mathematical Biology
• Phylogenetics
• +۳ مورد دیگر

Jesús Fernández Sánchez is a Researcher at the Department of Mathematics, Polytechnic University of Catalonia (UPC), associated with the GEOMVAP (Geometry of Varieties and Applications) research group. He holds an affiliation with the Faculty of Mathematics and Statistics (FME) and the Institute of Mathematics of UPC-BarcelonaTech (IMTech). His work focuses on algebraic geometry, mathematical phylogenetics, and computational algebra, with applications to evolutionary biology and stochastic processes. Research Interests: Fernández Sánchez's research bridges algebraic geometry with biological applications, particularly in phylogenetics. He explores topics such as Markov matrix embeddability, phylogenetic invariants, equivariant models, and algebraic methods for evolutionary analysis. His work includes developing semi-algebraic methods for quartet reconstruction and analyzing rank conditions in phylogenetic networks. Grants & Projects: He has led or contributed to several competitive research projects, including 'GEOMETRIA DE VARIETATS I APLICACIONS' (GEOMVAP group), the María de Maeztu Excellence Program, and collaborations on phylogenetic model development. His projects emphasize multidisciplinary approaches in geometry, algebra, and topology. Advising: He has supervised doctoral students like Marina Garrote-López and Jordi Roca-Lacostena in topics such as phylogenetic reconstruction methods and Markov matrix analysis. His lab investigates geometric and algebraic tools for evolutionary biology, with a focus on model validation and computational techniques. Affiliations: Besides his primary role at UPC, he collaborates with international research networks in algebraic phylogenetics and stochastic processes. His work integrates pure mathematics with applied problems in biology and statistics.

• Functional Analysis
• Convex Geometry
• High-Dimensional Phenomena
• +۳ مورد دیگر

Grigoris Paouris is a Professor in the Department of Mathematics at Texas A&M University, specializing in High-Dimensional Phenomena at the intersection of Analysis, Probability, and Geometry. His research focuses on Asymptotic Functional Analysis, Convex Geometry, and Random Matrix Theory. Key research areas include: Concentration of measure phenomena Functional inequalities in high dimensions Random matrix theory applications Computational algebraic geometry Distribution of volume in convex bodies He has been awarded prestigious fellowships including the Sloan Fellowship (2011) and Simons Fellowship (2021). His research is funded by NSF and Simons Foundation grants. Recent publications explore affine isoperimetric inequalities, geometric probability, and computational aspects of polynomial systems. Professor Paouris teaches graduate and undergraduate courses in functional analysis and probability theory. He serves on editorial boards of major mathematics journals and regularly organizes international workshops on convex geometry and high-dimensional probability.

Matiyasevich Yuriمشاهده پروفایل

استاد

• Diophantine equations
• Computability theory
• Algorithms
• +۱ مورد دیگر

Yuri Matiyasevich is a Russian mathematician and computer scientist affiliated with the Steklov Institute of Mathematics (Leningrad/St.Petersburg Branch) since 1980, where he serves as head of the Laboratory of Mathematical Logic. He also holds part-time professorships at Leningrad/St.Petersburg State University (since 2003) and previously at Polytechnical Institute (1980-1981). His research focuses on Diophantine equations, Computability theory, Algorithms, and Decidability problems. Full member, Russian Academy of Sciences (2008) Corresponding member, Bavarian Academy of Sciences (2007) Docteur Honoris Causa, Université Pierre et Marie Curie (2003) Humboldt Research Award (1997) Markov Prize, Academy of Sciences of the USSR (1980) His recent work involves computational approaches to Riemann's zeta function, probabilistic reformulations of graph theory problems, and algorithmic analysis of Diophantine representations. He has published extensively on connections between number theory and computational models, including studies on exponential Diophantine equations and their applications. Notable contributions include definitive solutions to Hilbert's Tenth Problem through Diophantine representations of recursively enumerable sets, and computational experiments supporting the Riemann Hypothesis. His publications span multiple languages and cover intersections between mathematical logic, number theory, and theoretical computer science.