جست‌وجوی استاد و پژوهشگر

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.

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.

Lingzhou Xueمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Statistics
• Statistical Learning
• +۹ مورد دیگر

Lingzhou Xue is a Professor of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds dual roles as a faculty member and the Associate Director of the National Institute of Statistical Sciences (NISS). His research focuses on high-dimensional statistics, nonparametric methods, statistical learning, and optimization, with applications in biomedical, environmental, and social sciences. He leads the SLDM (Statistical Learning and Data Mining) Lab and MDS (Microbiome Data Science) Lab. Education: B.Sc. in Statistics from Peking University (2008), Ph.D. in Statistics from the University of Minnesota (2012), postdoctoral training at Princeton University (2012–2013). Professional roles include Associate Editorships at the Journal of the American Statistical Association, Annals of Applied Statistics, and ACM Transactions on Probabilistic Machine Learning. Research Interests: Federated learning, causal inference, graphical models, reinforcement learning, optimal transport, and large-scale optimization. Recent work emphasizes theoretical guarantees for sparse PCA and federated Q-learning algorithms. Awards: IMS Fellow (2024), ASA Fellow (2023), COPSS Emerging Leader Award (2021), and Bernoulli Society New Researcher Award (2019). He has mentored 17 Ph.D. students and 3 postdocs, with four students securing tenure-track faculty positions. Service: Organized multiple NISS writing workshops, co-chaired the Ingram Olkin Statistics Serving Society Forum on Gun Violence, and contributed to the ASA whitepaper 'Discovery with Data' (2014).

Wouter Den Haanمشاهده پروفایل

استاد

• Macroeconomics
• Dynamic Stochastic Equilibrium Models
• Heterogeneous Agents
• +۴ مورد دیگر

Wouter Den Haan is a Professor of Economics at the London School of Economics and Political Science (LSE), where he has held the position since 2011. He previously served as a professor at the University of Amsterdam (2006–2011), London Business School (2003–2007), and UC San Diego (2001–2004). His research focuses on macroeconomics, dynamic stochastic equilibrium models, heterogeneous agents, and numerical solution techniques. Den Haan has held editorial roles at journals like Quantitative Economics and the Economic Journal and serves as a research consultant for the European Central Bank. He holds a Ph.D. in Economics from Carnegie Mellon University (1991) and an MSc in Economics (cum laude) from Erasmus University Rotterdam (1986). His awards include multiple Excellence in Education Awards (2018–2021) and the Alexander Henderson Dissertation Award (1991). Den Haan’s work has been published in leading journals such as the Journal of Monetary Economics, Journal of Economic Dynamics and Control, and the American Economic Review. His research emphasizes frictions in financial and labor markets, business cycle models with heterogeneous agents, and computational economics. Notable contributions include studies on uncertainty in search and matching models, agnostic structural disturbances in macroeconomic models, and the role of debt and equity finance in business cycles. Den Haan has advised numerous PhD students and holds grants from organizations like the ESRC and the Dutch Science Council (NWO). He has been affiliated with institutions such as the Centre for Economic Policy Research (CEPR) and the Centre for Macroeconomics (CFM), contributing to policy debates on quantitative easing, central bank independence, and financial innovation. His teaching evaluations reflect consistently high ratings across undergraduate and graduate courses in macroeconomics.

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.

• Partial Differential Equations
• Numerical Analysis
• Mathematical Biology
• +۵ مورد دیگر

Charles M. Elliott is a Professor of Mathematics at the University of Warwick. His research focuses on mathematical and numerical analysis of partial differential equations (PDEs), particularly in evolving domains, manifolds, and surfaces. Key areas include free boundary problems, geometric evolution equations, biomembranes, and phase transitions. He has contributed to finite element methods for surface PDEs and holds editorial roles at journals like the IMA Journal of Numerical Analysis and Interfaces and Free Boundaries . Notable awards include the Humboldt Prize (2010) and the Royal Society Wolfson Research Merit Award (2016). His work bridges theoretical mathematics and applications in biology, materials science, and continuum mechanics. Educations and career details: While specific educational qualifications are not explicitly stated in the text, his professional trajectory includes leadership in computational mathematics and extensive editorial contributions. His teaching responsibilities include advanced modules like Maths-in-Action and Topics in Partial Differential Equations . Research Interests: Elliott’s work emphasizes PDEs in complex environments, with applications to biomembranes, cell motility, and material phase transitions. His methods integrate evolving surface finite elements and stochastic processes. Recent trends in his publications highlight evolving domains, coupled bulk-surface systems, and geometric PDEs. He addresses challenges in numerical analysis and modeling of evolving interfaces. Awards and Recognition: In addition to the Humboldt Prize and Royal Society award, he is a Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2015) and received an honorary doctorate from the University of Sussex (2022). Advising and Grants: While specific student names or grant details are not listed, his research programs involve collaborations on topics like biomembrane dynamics and phase field models. He has pioneered numerical methods for evolving surface PDEs, impacting fields from materials science to mathematical biology. Labs and Teams: His work is conducted within Warwick’s Department of Mathematics, collaborating on interdisciplinary projects involving computational PDEs and their applications. The research often involves evolving geometries and multiscale modeling approaches.

• Ergodic Theory
• Dynamical Systems
• Probability Theory
• +۶ مورد دیگر

Professor Ian Melbourne is a Professor of Mathematics at the University of Warwick's Mathematics Institute. His research focuses on ergodic theory, dynamical systems, and their links with probability and stochastic analysis. He holds editorial roles at *Ergodic Theory and Dynamical Systems* and *Probability Theory and Related Fields*. His work explores complex systems such as fast-slow dynamics, Lévy processes, mixing rates, billiards, and statistical limit laws. Research Interests: Ergodic theory, dynamical systems, stochastic processes, Lévy processes, mixing rates, billiards, and statistical laws. His contributions span theoretical frameworks for deterministic homogenization, superdiffusive limits, and large deviations in non-equilibrium systems. Publications highlight advancements in understanding systems with singularities, such as Lorentz gases and intermittent maps. His recent work emphasizes convergence to Lévy processes and polynomial decay of correlations in nonpositively curved surfaces. He has advised numerous PhD students (not listed here) and contributes to the academic community through editorial work and collaborative research. No specific grants or labs are detailed in the provided texts.

Vladimir Kaishevمشاهده پروفایل

استاد

• Actuarial Science
• Risk Theory
• Reinsurance Modeling
• +۷ مورد دیگر

Prof. Vladimir Kaishev is a Professor of Actuarial Science at the Faculty of Actuarial Science and Insurance (FASI), Bayes Business School, City, University of London. He holds a PhD in Statistics and Information Theory from Moscow Technical University and has held visiting positions at Kyoto University, the University of Melbourne, and Heriot-Watt University. His research focuses on actuarial mathematics, risk theory, reinsurance modeling, and the application of spline functions in finance and insurance. He has supervised numerous PhD students and contributed to over 30 journal articles, conference papers, and software packages such as StMoMo and KSgeneral . His work includes advancements in ruin probability modeling, copula functions, and operational risk assessment, alongside consultancy roles for institutions like Swiss Reinsurance and the Bulgarian Actuarial Society. He is an Associate Editor of Economic Quality Control and a member of professional societies including the American Mathematical Society. Education: MSc and PhD in Statistics from Moscow Technical University, followed by postdoctoral research at the University of Wisconsin-Madison and UCLA. Research Interests: Multivariate Lévy processes, ruin theory, optimal reinsurance, spline functions, and stochastic modeling. His work bridges actuarial science with financial mathematics and statistical methods. Publications: Over 40 peer-reviewed articles and book chapters, with recent focus on generalized linear models, survival analysis, and actuarial software development. His 2023 paper in Applied Mathematics and Computation introduced novel variable knot spline methods. Awards: Received the Cass Business School Certificate for Excellence in Research in 2009 and 2013. Consultancy & Grants: Projects include stochastic mortality modeling for the UK Office for National Statistics and economic scenario generator development for Aon-Benfield. His research has been supported by grants from the Leverhulme Trust and the Swiss Re. Labs/Teams: Leads the Actuarial Research Center and collaborates internationally on projects such as the StMoMo R package for stochastic mortality modeling.

Juan Restrepoمشاهده پروفایل

استاد

• Applied Mathematics
• Computational Mathematics
• Data Science
• +۱۲ مورد دیگر

Juan Restrepo is a Professor of Mathematics with courtesy appointments in Statistics, EECS, and Physical Oceanography at Oregon State University, where he holds a Courtesy Faculty position in the Department of Mathematics within the College of Science. He is also affiliated with the University of Tennessee and Oak Ridge National Laboratory, where he holds appointments in the Computer Science and Mathematics Division. He serves as Co-Director of the Dynamics and Data Science Institute (D2SI), a leading interdisciplinary research center. Ph.D. in Physics, Pennsylvania State University, 1992 M.S. in Engineering (Acoustics), Pennsylvania State University, 1987 B.S. in Music, New York University, 1983 Restrepo's research lies at the intersection of data science and dynamics, with two primary tracts: (1) applying statistical physics and data science to complex non-equilibrium systems such as climate and financial markets; and (2) studying ocean dynamics and transport in climate and nearshore processes. His work emphasizes uncertainty quantification, adaptive time series analysis, extreme events, and data assimilation. His recent publications reflect a strong focus on modeling geophysical systems under uncertainty, using advanced computational and statistical techniques. Themes include climate sensitivity, oil spill dispersion, sediment transport, wave breaking, and high-performance computing for large-scale simulations. His work integrates machine learning, stochastic modeling, and dynamical systems theory. SIAM Fellow SIAM Geosciences Career Award ORISE Distinguished Post-doctoral Fellow DOE Young Investigator Award Ruth Homeyer Graduate Student Award Restrepo has secured over $30 million in research funding from NSF, DOE, NASA, and GoMRI. He has advised numerous students in applied mathematics and computational sciences, and has held leadership roles in professional societies, including President of the Nonlinear Geophysics Section at the American Geophysical Union. He is actively involved in promoting diversity in science and mentoring underrepresented groups. He leads interdisciplinary research teams at D2SI and collaborates with national labs such as Argonne and Los Alamos. His work bridges applied mathematics, climate science, oceanography, and computational engineering, making significant contributions to both theory and real-world applications.

Oliver M Jenkinsonمشاهده پروفایل

استاد

• Ergodic Theory
• Dynamical Systems
• Mathematical Analysis
• +۳ مورد دیگر

Professor Oliver M Jenkinson is a faculty member at the School of Mathematical Sciences, Queen Mary University of London. He holds the academic rank of Professor of Mathematics and is affiliated with the Centre for Complex Systems. His research focuses on ergodic theory, dynamical systems, and related areas such as fractal geometry and statistical mechanics. Jenkinson has contributed to topics like Lyapunov exponents, invariant measures, and Hausdorff dimension estimation. His work spans theoretical and computational aspects of dynamical systems, including studies of transfer operators, spectral radii, and entropy optimization. Notable contributions include rigorous bounds on fractal dimensions, analysis of continued fractions, and investigations into ergodic optimization problems. He collaborates with institutions globally and maintains an active research profile with over 40 publications since 2001. Research trends in his articles emphasize interdisciplinary applications of ergodic theory, with recurring themes in mathematical physics and nonlinear dynamics. His papers often bridge pure mathematics with computational methods, addressing questions in chaos theory, measure theory, and operator spectra. Jenkinson’s work is supported by grants and collaborations, though specific funding details are not listed. He is associated with the Centre for Complex Systems, reflecting his focus on complex, multi-component systems in mathematics. No formal awards are mentioned in the provided texts, though his prolific publication record indicates significant scholarly impact.

• Applied Mathematics
• Partial Differential Equations
• Material Science
• +۵ مورد دیگر

Ulisse Stefanelli is a full Professor at the Department of Mathematics, University of Vienna, within the Faculty of Mathematics. His research focuses on applied mathematics, partial differential equations, and material science, with a particular emphasis on nonlinear dynamics, elasticity, and variational methods. He has contributed significantly to the study of gradient flows, energy dissipation principles, and phase-field modeling in complex materials. Affiliations: University of Vienna (Faculty of Mathematics, Department of Mathematics) His research interests include the mathematical analysis of continuum mechanics problems, such as viscoelasticity, plasticity, and accretive growth in solids. He has pioneered methods like the Weighted Energy-Dissipation principle and contributed to topology optimization and multiscale modeling. Stefanelli has led numerous research projects, including those on elastic-fluidic biomembranes and geometric boundary conditions in solids. Awards: Vinti Prize (2015) His work bridges pure and applied mathematics, addressing challenges in material science and geophysics. Current activities include studies on planetary accretion models and stochastic evolution equations. Collaborations span international institutions, focusing on interdisciplinary applications of variational principles.

• Earthquake Source Studies
• Strong Motion Seismology
• Seismic Wave Propagation
• +۴ مورد دیگر

Ralph Archuleta is a Professor of Earth Science at the University of California, Santa Barbara (UCSB), affiliated with the Earth Research Institute (ERI). His primary research focuses on Earthquake Source Studies and Strong Motion Seismology, emphasizing the dynamics of fault rupture, ground motion prediction, and seismic hazard assessment. He has advised numerous PhD students and collaborated with postdoctoral researchers, contributing to advancements in seismological modeling and field data analysis. Education: B.S., University of Wyoming, 1969 Ph.D., UC San Diego, 1976 Research Interests: Earthquake source mechanics and rupture dynamics Strong ground motion simulation and site effects Seismic wave propagation in heterogeneous media Development of numerical models for fault systems His work integrates field observations, laboratory experiments, and computational simulations to address seismic hazard challenges. Publications (Recent Trends): Archuleta’s recent work explores fault heterogeneity, nonlinear site response, and supershear earthquakes. Key themes include stochastic modeling of slip distributions, radiated seismic energy analysis, and improvements to ground motion prediction methods. Scientific Contributions: While no explicit awards are listed, his extensive publications and leadership in seismology reflect significant scholarly impact. He has mentored over 20 graduate students and supervised postdocs globally, advancing careers in academia and industry. Labs/Teams: Active in the ERI at UCSB, contributing to collaborative projects like the COSMOS Virtual Data Center for strong motion data dissemination.

Francesca Pariseمشاهده پروفایل

استادیار

• Multi-Agent Systems
• Network Theory
• Control Systems
• +۶ مورد دیگر

Francesca Parise is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. Her research focuses on multi-agent systems, particularly analyzing large networks of autonomous decision-makers in transportation, finance, and social systems, employing tools from control theory, game theory, and optimization. She holds a PhD from ETH Zurich and was a Postdoctoral Research Fellow at MIT's Laboratory for Information and Decision Systems under Prof. Asuman Ozdaglar. Her work bridges theoretical foundations with practical applications in networked systems and interventions. Education: Bachelor's and Master's from University of Padua, Italy PhD in Control Engineering from ETH Zurich (2016) Her research interests span game-theoretic models for network analysis, learning dynamics in time-varying systems, and optimal intervention strategies. Key themes include synchronization in power grids, epidemic control via testing, and dynamic resource allocation. Recent work emphasizes graphon-based frameworks for large-scale network games and computational methods for equilibrium analysis. She explores both theoretical guarantees and real-world applications in transportation and energy systems. Publications highlight contributions to networked game theory, stochastic systems, and control applications, with a focus on scalable solutions for complex systems. Her work often intersects with interdisciplinary challenges in systems biology and socio-technical networks. She currently holds an office at Frank H. T. Rhodes Hall, Cornell University.

Grzegorz Graffمشاهده پروفایل

استاد

• Fixed and periodic point theory
• Topological methods in dynamical systems
• Mathematical methods in medicine
• +۲ مورد دیگر

Prof. Grzegorz Graff is a Full Professor at Gdańsk University of Technology, serving as Vice-Dean for Research at the Faculty of Technical Physics and Applied Mathematics and Head of the Department of Differential Equations and Applications of Mathematics. His academic career includes roles as Assistant (1991–1999), Assistant Professor (2000–2010), Associate Professor (2010–2019), and Full Professor since 2020. Research focuses on Fixed Point Theory, Dynamical Systems, and applications in cardiology. Key awards include the Silver Cross of Merit and multiple Rector Awards. Education: PhD (2010) and habilitation (2010) from Adam Mickiewicz University Leadership: Head of Division of Differential Equations, Chair of the Scientific Field Board of Exact Sciences, and editorial board member of Mathematica Applicanda and "BioPhysMath" . Research emphasizes mathematical methods in cardiology, entropy-based analysis of heart rate variability, and topological data analysis. Notable projects include the Center for Applications of Mathematics and studies on dynamical systems and Morse-Smale diffeomorphisms.

• Statistics
• Econometrics
• Machine Learning
• +۷ مورد دیگر

Marcel Scharth is a Lecturer in Business Analytics at the University of Sydney Business School and affiliated with the Centre for Translational Data Science. He holds a Ph.D. from VU University Amsterdam (2012) and has conducted postdoctoral research at the University of New South Wales. His expertise spans Bayesian methods, computational statistics, machine learning, and financial econometrics. Research focuses include high-dimensional stochastic volatility models, Monte Carlo methods, and Bayesian machine learning applied to time series and longitudinal data. He teaches courses like QBUS2810 (Statistical Modelling), QBUS2820 (Predictive Analytics), and QBUS5001 (Quantitative Methods for Business), emphasizing statistical reasoning and computational skills. His work has been published in top journals such as the Journal of Econometrics and the Review of Economics and Statistics. He actively engages in AI discourse, contributing to media articles on prompt engineering for generative AI and the limitations of models like ChatGPT. Professional activities include Effective Altruism collaborations and open-source contributions via GitHub repositories like forecasting and Machine Learning for Business .