جستجوی استاد راهنمای دانشگاهی

Dmitriy Drusvyatskiyمشاهده پروفایل

استاد

• Continuous Optimization
• Data Science Applications
• Convex Optimization
• +۵ مورد دیگر

Dmitriy Drusvyatskiy is a Professor in the Department of Mathematics at the University of Washington and holds the Paul Tseng Faculty Fellow title. He also serves as an Adjunct Professor in the Department of Statistics. His research focuses on continuous optimization, particularly in large-scale problems arising in data science, leveraging tools from convex/nonlinear optimization, variational analysis, semi-algebraic geometry, and high-dimensional probability/statistics. Education: PhD in Operations Research from Cornell University (2013). Research Interests: His work emphasizes optimization theory and applications, including stochastic optimization, nonsmooth analysis, matrix recovery, and statistical learning. He explores foundational aspects like convergence guarantees, algorithm design, and applications in machine learning and data-driven decision-making. Notable Trends in Publications: Recent work highlights advancements in stochastic algorithms with geometric step decay, low-rank matrix recovery, and generalization properties of flat minima. His studies often bridge theoretical guarantees with computational efficiency in high-dimensional settings. Awards: Paul Tseng Faculty Fellow (2020+). Advising & Grants: No explicit student advisee list provided. His research is supported by grants focusing on nonsmooth optimization’s structure, complexity, and conditioning. Collaborations span mathematical theory and interdisciplinary applications in data science.

Thomas Trogdonمشاهده پروفایل

استاد

• Applied Linear Algebra
• Asymptotic Analysis
• Nonlinear Waves and Coherent Structures
• +۴ مورد دیگر

Thomas Trogdon is the Chair and Professor of Applied Mathematics at the University of Washington. He holds a Ph.D. in Applied Mathematics from the University of Washington (2013). His research focuses on numerical analysis, random matrix theory, nonlinear waves, and scientific computing. Key interests include Riemann-Hilbert problems, numerical linear algebra, and the application of these methods to problems in mathematical physics and data science. He has organized major conferences such as the Conference on Random Matrix Theory and Numerical Linear Algebra (2022, 2025). His work bridges theoretical developments with computational implementation, exemplified by collaborations on algorithms for orthogonal polynomials and inverse scattering transforms. He leads a team within the Department of Applied Mathematics, contributing to both research and education. Education: Ph.D., Applied Mathematics, University of Washington, 2013 Grants: NSF CAREER Award (DMS-1945652) Collaborators: Includes researchers like Sheehan Olver, Percy Deift, and Xiucai Ding His recent work emphasizes the convergence of numerical methods, stability analysis of algorithms, and applications to high-dimensional data and physical systems. A co-author of the book Riemann-Hilbert Problems, Their Numerical Solution and the Computation of Nonlinear Special Functions (SIAM, 2016), he also develops software tools such as the RHPackage for Riemann-Hilbert problem computations.

Hongkai Zhaoمشاهده پروفایل

استاد

• Computational and Applied Mathematics
• Numerical Analysis
• Scientific Computing
• +۴ مورد دیگر

Hongkai Zhao is the Ruth F. DeVarney Distinguished Professor of Mathematics and Chair of the Department of Mathematics at Duke University's Trinity College of Arts & Sciences. He holds a Ph.D. in Mathematics from UCLA (1996) and has held prior positions including Chancellor's Professor at UC Irvine and Gábor Szegő Assistant Professor at Stanford University. His research focuses on computational and applied mathematics with applications in inverse problems, imaging, PDEs, and scientific computing. Key research areas include numerical methods for wave propagation, radiative transfer, and shape analysis. Zhao has led NSF-funded projects on PDE learning, radiative transfer, and analysis training. He has received prestigious awards including the Feng Kang Prize (2007) and SIAM Fellowship (2022). Teaching includes advanced courses on stochastic processes, numerical linear algebra, and mathematical analysis. His work bridges theory and practice, contributing to medical imaging algorithms, computational physics, and geometric modeling.

Partha Deyمشاهده پروفایل

دانشیار

• Probability Theory
• Statistical Physics
• First and Last Passage Percolation
• +۶ مورد دیگر

Partha Dey is an Associate Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign (UIUC), where he also serves as Director of the NetMath Program. He holds affiliations in both Mathematics and Statistics departments. His research focuses on Probability Theory and its intersections with Statistical Physics, emphasizing First/Last Passage Percolation, Random Growth Models, Stein’s Method, Spin Glasses, and Random Matrix Theory. Education: Ph.D. in Statistics from UC Berkeley (2010), supervised by Sourav Chatterjee and Steve Evans. Prior to UIUC, he was a Courant Instructor/Simons Fellow at NYU (2010-2013) and a Harrison Early-Career Assistant Professor at the University of Warwick (2013-2014). Undergraduate and Master’s studies at Indian Statistical Institute Kolkata (Mathematical Statistics & Probability). Research interests include analyzing stochastic processes in complex systems, with recent work on fluctuation phenomena in percolation models, spin glasses under external fields, and Stein’s method applications. His publications span high-impact journals like ALEA , Annals of Probability , and Communications in Mathematical Physics . Awards/Funding: No explicitly listed honors, though his positions suggest sustained academic recognition. Grants/Advising: No detailed grant info provided; no advisee names listed in texts. Labs/Teams: Leads NetMath Program, a distance-learning initiative in mathematics education. Collaborates widely on interdisciplinary projects in probability and statistical physics.

Miklos Z Raczمشاهده پروفایل

استادیار

• Probability
• Statistics
• Computer Science
• +۷ مورد دیگر

Miklos Z Racz is an Assistant Professor of Statistics and Data Science and Computer Science at the Department of Statistics and Data Science, Northwestern University, within the Weinberg College of Arts & Sciences. He holds a PhD in Statistics from UC Berkeley (2015). His research focuses on the intersection of probability, statistics, computer science, and information theory, with an emphasis on combinatorial statistics, discrete probability, and applied probability. He studies statistical inference on large random discrete structures such as random graphs, including topics like inferring latent geometry in networks, community detection, and dynamics on networks. He also explores applications in social networks, voting systems, sequence reconstruction, and DNA data storage. His educational background includes a PhD in Statistics from UC Berkeley (2015), reflecting expertise in theoretical and applied statistical methods. His work often bridges theoretical foundations with practical challenges in data science and network analysis. Racz's research interests span a broad range of topics, including the analysis of random graphs, community detection, graph matching, and the development of algorithms for high-dimensional data. His contributions to DNA data storage and sequence reconstruction highlight his interdisciplinary approach to solving real-world problems through statistical and computational methods. His recent publications explore themes such as graph isomorphism analysis, efficient polymer synthesis, and reducing polarization in social networks. These works demonstrate a focus on advancing both foundational theory and applied technologies. He has not explicitly mentioned any scientific awards, but his prolific publication record reflects significant scholarly activity. While no specific grants or advising roles are detailed in the provided text, his research on DNA data storage and network dynamics suggests involvement in cutting-edge projects at the intersection of computer science and biotechnology. His work contributes to advancing methodologies for handling complex data structures and improving computational efficiency in network analysis.

• Bayesian Inference
• Markov Chain Monte Carlo Methods
• Nonparametric Estimation
• +۷ مورد دیگر

Martin A. Tanner is a Professor of Statistics and Data Science at Northwestern University's Weinberg College of Arts & Sciences. He holds a Ph.D. from the University of Chicago (1982). His research focuses on Bayesian and frequentist inference methods, including Markov chain Monte Carlo (MCMC), nonparametric hazard estimation for censored data, ecological inference, and interrater agreement models. He has contributed to applications in public health, including studies on lead exposure and methylmercury neurodevelopmental effects. His work bridges statistical theory and practice, with notable advancements in data augmentation techniques and Gibbs posterior frameworks. Recent research includes critiques of pandemic modeling accuracy during the COVID-19 crisis and methodological innovations in ecological regression with partial identification. Tanner has authored influential textbooks like Tools for Statistical Inference (Springer) and pioneered hierarchical mixtures-of-experts models for regression analysis. Key contributions span statistical computing, econometric modeling, and interdisciplinary studies. His publications address challenges in model robustness, variable selection, and Bayesian computational methods. Despite no listed scientific awards, his methodological work underpins modern statistical practice in academia and applied fields.

Karl Oskar Ekvallمشاهده پروفایل

استادیار

• Multivariate Analysis
• Mixed Models
• Latent Variable Models
• +۴ مورد دیگر

Karl Oskar Ekvall is an Assistant Professor in the Department of Statistics at the University of Florida and an affiliated researcher at Karolinska Institutet. He serves as an Associate Editor of Statistics and Probability Letters . His research focuses on multivariate analysis, mixed and latent variable models, statistical computing, and applications in biomedical sciences and economics. Education: PhD in Statistics (University of Minnesota), M.Sc. in Finance and B.Sc. in Economics (University of Gothenburg). Former roles include Assistant Professor in Biostatistics at Karolinska Institutet and post-doctoral researcher at TU Wien. Research interests emphasize methodological advancements in statistical theory, with a focus on high-dimensional data analysis and model estimation techniques. His work bridges computational methods with practical applications in healthcare and economic systems. Active in academic service, he contributes to editorial work and has publications spanning statistical theory, biostatistical applications, and computational methodologies. Collaborations involve interdisciplinary projects in biomedical sciences and environmental health studies.

Kshitij Khareمشاهده پروفایل

استاد

• High dimensional covariance estimation
• Graphical models
• Bayesian VAR
• +۳ مورد دیگر

Kshitij Khare is a Professor in the Department of Statistics at the University of Florida, affiliated with the College of Liberal Arts and Sciences. His research focuses on high-dimensional statistical methods, Bayesian computation, and graphical models. He holds a Ph.D. in Statistics from Stanford University (2009), and prior degrees from Indian Statistical Institute (B.Stat. 2002, M.Stat. 2004, and M.Math. Finance 2009). His work spans covariance estimation, Bayesian VAR models, MCMC convergence analysis, and applications in genomics, neurosurgery, and dairy science. He teaches advanced courses including Introduction to Probability (STA4321/5325), Theoretical Statistics I/II (STA 6326/6327), and specialized topics like covariance estimation. His grants include NSF-funded projects on Bayesian model selection, MCMC algorithm analysis, and high-dimensional temporal data methods. Notable collaborations include work on genomic prediction using Gaussian concentration graph models and Bayesian approaches for mixed-frequency data. Khare's research emphasizes scalable Bayesian methods, posterior consistency, and algorithmic efficiency. His lab develops novel techniques for estimating complex statistical structures in high-dimensional settings, with applications to real-world problems in biology, finance, and engineering. Current projects explore sparse Cholesky-based covariance estimation and Bayesian shrinkage priors for high-dimensional regression.

Adam Kolkiewiczمشاهده پروفایل

استاد

• Financial Mathematics
• Risk Management
• Stochastic Modeling
• +۳ مورد دیگر

Dr. Adam Kolkiewicz is a Professor Emeritus in the Department of Statistics and Actuarial Science at the University of Waterloo. His research focuses on financial mathematics, risk management, and computational methods for actuarial science. He develops stochastic models for pricing financial derivatives like Bermudan options and reverse mortgages. His work includes numerical techniques for high-dimensional problems in finance, such as low-discrepancy mesh methods and Monte Carlo simulations. Dr. Kolkiewicz maintains consulting activities with financial institutions and has professional recognition as a Chartered Professional Engineer. His publications appear in Quantitative Finance, Insurance Mathematics and Economics, and Journal of Computational Finance.

Bing Liمشاهده پروفایل

استاد

• Dimension Reduction
• Machine Learning
• Graphical Models
• +۴ مورد دیگر

Bing Li is the Verne M. Willaman Professor of Statistics at The Pennsylvania State University, within the Eberly College of Science and the Department of Statistics. His work focuses on advancing statistical methodologies with applications in diverse fields. He has held academic positions since joining Penn State and maintains active research and teaching roles. Education: Ph.D. in Statistics (1992), The University of Chicago M.Sc. in Statistics (1989), University of British Columbia, Vancouver M.Sc. in System Sciences (1986), Beijing Institute of Technology B.Sc. in Automatic Control (1982), Beijing Institute of Technology Research Interests: Bing Li specializes in dimension reduction techniques for high-dimensional data, with a focus on nonlinear methods and their applications in machine learning. He explores graphical models to represent statistical networks and has contributed to estimating equations , semiparametric estimation , and asymptotic theories . His work integrates longitudinal data analysis and addresses challenges in functional data regression and causal inference through innovative frameworks like envelope models and functional additive regression operators. Research Trends: His recent publications emphasize nonlinear and functional extensions of sufficient dimension reduction, causal graph learning, Bayesian statistical methods, and kernel-based testing. Notable themes include leveraging optimal transport for graphical models, developing ensemble neural networks for dimension reduction, and advancing statistical inference for complex data structures like tensor-valued observations. Awards/Honors: No specific honors or awards are explicitly listed beyond his endowed professorship. Advising & Grants: No advising records or grant information are provided in the text. His professional activities likely include mentoring through his departmental role, though explicit details are unavailable. Labs/Teams: Not explicitly mentioned; however, his research is conducted through the Department of Statistics at Penn State, possibly collaborating with interdisciplinary teams given his focus on biological and functional data applications.

Weixing Songمشاهده پروفایل

استاد

• Measurement Error Models
• Nonparametric Smoothing
• Bioassay
• +۱ مورد دیگر

Dr. Weixing Song is Professor of Statistics at Kansas State University, specializing in methodological innovations for measurement error and nonparametric estimation. His research develops statistical tools for bioassay validation and high-dimensional data analysis. Teaching emphasizes theoretical foundations including stochastic processes and probability theory. As graduate program contributor, he mentors students in computational statistics and experimental design applications for biological systems.

• Statistical Inference
• Mathematical Physics
• Online Mathematics
• +۳ مورد دیگر

Carlos Rodríguez is an Associate Professor in the Department of Mathematics and Statistics at the State University of New York at Albany (SUNY Albany). He has maintained an academic server since 1993, hosting research papers, teaching materials, and software tools. His research focuses on statistical inference, mathematical physics, and online mathematics, with notable contributions to Bayesian nonparametrics, density estimation, and maximum entropy methods. He teaches courses such as calculus, linear algebra, and machine learning, and has developed software like Mapleman the Math Bot. His research interests emphasize geometric interpretations of statistical concepts, including entropic priors, Bayesian networks, and model selection criteria like CIC. He has explored applications in neutron depth profiling and probabilistic graphical models. Rodríguez's work bridges theoretical foundations with computational methods, as seen in his papers on MCMC algorithms and cross-validated Bayesianism. His server archives over two decades of publications, including seminal works on optimal recovery, ignorance theory, and likelihood principle critiques. While not explicitly listed, his contributions to statistical education and software development highlight his commitment to advancing both research and pedagogy in mathematics and statistics.

• Bayesian Computation
• Monte Carlo Methodology
• Causal Inference
• +۳ مورد دیگر

Yu-Bo Wang is an Associate Professor in the Department of Mathematical and Statistical Sciences at Clemson University. His research focuses on Bayesian computation, causal inference, and statistical methodologies with applications in health sciences and ecology. He holds a PhD in Statistics from the University of Connecticut (2016), and MS and BS degrees in Statistics from National Chengchi University, Taiwan (2009 and 2006). Education: PhD, Statistics, University of Connecticut, 2016 MS, Statistics, National Chengchi University, Taiwan, 2009 BS, Statistics, National Chengchi University, Taiwan, 2006 Research Interests: His work spans Bayesian computation, Monte Carlo methods, causal inference, and applications in mortality projection. Notable contributions include advancements in marginal likelihood estimation, mediation analysis, and high-dimensional regression techniques. Publications: Recent work includes studies on Bayesian regularized mediation analysis in health outcomes, statistical methods for causal inference in clustered data, and computational innovations in phylogenetics and ecology. His research bridges theoretical statistics with practical applications in dentistry, environmental science, and public health. Awards: No specific awards listed, but his prolific publication record reflects sustained academic excellence. Advising & Grants: Advises graduate students in statistical methodology and collaborates on grants related to Bayesian computation and health data analysis. No specific grant details provided in the text. Labs/Teams: Engages in interdisciplinary teams within Clemson’s College of Science, though specific lab affiliations are not detailed here.

Jialin Zhangمشاهده پروفایل

استادیار

• Entropic Statistics
• Nonparametric Estimation
• Data Analytics
• +۲ مورد دیگر

Jialin Zhang is an Assistant Professor of Statistics at Mississippi State University. He received his Ph.D. in Statistics from the University of North Carolina at Charlotte in 2019. His research focuses on entropic statistics, including nonparametric estimation of entropy, mutual information, tail probabilities, and biodiversity indices, with applications in machine learning and data science. His work addresses high-dimensional and non-ordinal data challenges using information-theoretic frameworks, developing tools like R packages for tail classification and entropic statistics. Research emphasizes theoretical foundations and practical implementations in computational biology and healthcare analytics.

Daniel Huangمشاهده پروفایل

استادیار

• Quantum Computing
• Machine Learning
• Probabilistic Programming
• +۳ مورد دیگر

Dr. Daniel Huang is an Assistant Professor in the Department of Computer Science at San Francisco State University. His research focuses on quantum computing, probabilistic programming, machine learning, and theoretical computer science. He explores interdisciplinary areas such as hybrid classical-quantum systems, Gaussian process optimization, and computational chemistry modeling. His work bridges algorithmic design with practical applications, including quantum circuit simulation and molecular geometry optimization. Dr. Huang’s recent publications highlight advancements in GPU-based quantum computing, gradient-constrained neural networks, and probabilistic programming languages like Push. He emphasizes the integration of physical priors into machine learning models and explores disruptive technologies like quantum visualization tools. His research often involves collaborative projects, as seen in works on meta-Gaussian processes and data-parallel inference algorithms. His academic contributions span over a decade, with notable papers in probabilistic program semantics, logic in linear spaces, and compiler optimizations for probabilistic models. Though no awards or grants are explicitly listed, his active publication record reflects sustained scholarly engagement. Contact: danehuang@sfsu.edu , Thornton Hall 906.