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Matt WandView profile

Professor

• Statistical Methodology
• Semiparametric Regression
• Generalized Linear Mixed Models
• +5 more

Professor Matt Wand is a Distinguished Professor of Statistics at the University of Technology Sydney (UTS), affiliated with the School of Mathematical and Physical Sciences and the Statistics and Data Science Group. His academic career includes appointments at Harvard University, Rice University, Texas A&M University, the University of New South Wales, and the University of Wollongong. He is a Fellow of the Australian Academy of Science, American Statistical Association, Institute of Mathematical Statistics, and Australian Mathematical Society. Research Interests: Statistical Methodology for Multivariate Data Fast Approximate Inference, Variational Bayes, and Expectation Propagation Generalized Linear Mixed Models, Semiparametric Regression, and Streaming Data Analysis Statistical Computing, R Software Development, and CRAN Contributions Awards and Honors: Moran Medal (1997) and Hannan Medal (2013) from the Australian Academy of Science UTS Chancellor’s Medal for Exceptional Research (2013) Pitman Medal (2013) from the Statistical Society of Australia Funded Research: Principal investigator on multiple Australian Research Council grants, including projects on variational approximations, semiparametric regression, and statistical computing. Key contributions include the ACEMS Centre of Excellence and R packages like glmmEP and densEstBayes . Professional Service: Leadership roles in statistical societies, editorial work, and mentorship. Active in promoting open-source statistical tools and reproducible research.

Zhaoran WangView profile

Associate Professor

• Machine Learning
• Optimization
• Reinforcement Learning
• +3 more

Zhaoran Wang is an Associate Professor at Northwestern University's McCormick School of Engineering, holding a joint appointment in Industrial Engineering and Management Sciences and Computer Science. His research focuses on developing data-driven decision-making methods that integrate machine learning, optimization, and game theory to address societal challenges. Key areas include autonomous agents, multi-agent systems, and provably efficient reinforcement learning algorithms. His work emphasizes computational and statistical efficiency, particularly in scenarios with partial observability or confounded data. Notable contributions include frameworks for mitigating overoptimization in reinforcement learning and scalable solutions for partially observed systems. He has published widely in top venues like ICML, NeurIPS, and ICLR. Research interests span machine learning theory, reinforcement learning, optimization, and their applications in control systems and multi-agent coordination. Recent publications emphasize theoretical guarantees for algorithms and practical scalability in real-world domains like autonomous driving and economic systems.

Tolulope FadinaView profile

Assistant Professor

• Financial Mathematics
• Risk Management
• Operational Research
• +3 more

Tolulope Fadina is an Assistant Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. His research focuses on financial mathematics, risk management, stochastic processes, and quantitative finance, with emphasis on uncertainty modeling and actuarial applications. His work addresses topics such as optimal reinsurance strategies under multivariate risks, parametric variability in risk measures, and axiomatization of quantiles. He explores frameworks for risk assessment under ambiguity, contributing to both theoretical and applied aspects of financial engineering. Notable publications include studies on reinsurance optimization with dependence uncertainty, risk measures under parameter uncertainty, and hyperfinite constructions in stochastic calculus. His research has appeared in journals like European Journal of Operational Research , Finance and Stochastics , and SIAM Journal on Financial Mathematics . No scientific awards are explicitly listed in the provided information. He advises no formally listed students or mentees. His office is located at 273 Altgeld Hall, Urbana, IL.

Joe GuinnessView profile

Associate Professor

• Spatial Statistics
• Gaussian Process Modeling
• Computational Statistics
• +5 more

Dr. Joe Guinness serves as an Associate Professor and Director of Undergraduate Studies in the Department of Statistics and Data Science within Cornell University's College of Agriculture and Life Sciences (CALS). His research focuses on developing computationally efficient methods for analyzing large spatial-temporal datasets, with applications spanning earth sciences, environmental monitoring, epidemiology, and precision agriculture. His work bridges theoretical statistics with practical implementation through the development of the GpGp R package for Gaussian process computation. Dr. Guinness specializes in spatial statistics and Gaussian process modeling, particularly advancing Vecchia approximations for scalable computation. His research addresses critical challenges in interpolating satellite data, modeling environmental processes, and developing statistical frameworks for large-scale datasets. Current projects include applications in climate change modeling, soil chemistry analysis, medical imaging, and wildlife disease surveillance, with emphasis on computational efficiency and accurate uncertainty quantification. His publication record demonstrates significant contributions to scalable spatial statistics, with recent work focusing on Vecchia approximations, Gaussian process learning, and applications to earth science problems. His research shows consistent progression toward more efficient computational methods while expanding into new application domains including epidemiology (chronic wasting disease modeling) and sports science (Vaporfly shoe impact analysis). Cornell Atkinson Academic Venture Fund (AVF) seed grant (2021) supporting vital interdisciplinary collaborations Dr. Guinness actively mentors doctoral students, currently advising Megan Gelsinger at Cornell while having graduated five PhD students from North Carolina State University. His research is supported by collaborative grants across multiple disciplines, including environmental science, agriculture, and public health initiatives. The GpGp R package he developed has become a standard tool for efficient Gaussian process computation in spatial statistics. His research group develops computational frameworks for analyzing massive spatial datasets, with particular emphasis on earth science applications requiring innovative approaches to handle satellite observations, climate model output, and environmental monitoring data. Current projects integrate statistical methodology development with practical implementation for real-world environmental challenges.

Toh Kim ChuanView profile

Professor

• Optimization Algorithms
• Semidefinite Programming
• Machine Learning
• +4 more

Professor Toh Kim Chuan is the Leo Tan Professor in Science and serves as Research Director (Technical) at the Institute of Operations Research and Analytics (IORA) within the National University of Singapore (NUS). His primary affiliation is with the Department of Mathematics in the Faculty of Science. His research focuses on matrix optimization problems, large-scale semidefinite programming, fast algorithms for statistical and machine learning tasks, and iterative methods for solving large linear systems in optimization contexts. His recent work emphasizes contributions to nonsmooth nonconvex optimization, distributionally robust optimization, and scalable algorithms for sparse and low-rank optimization problems. Notable methodologies include Bregman proximal algorithms, adaptive sieving techniques, and inexact augmented Lagrangian methods. Professor Toh is also actively involved in developing computational tools like CDOpt for Riemannian optimization and frameworks for low-bit communication in distributed model training. While no specific awards are listed, his extensive publication record (over 150 papers from 2020-2025) demonstrates significant contributions to optimization theory and applications. His research bridges theoretical foundations with practical implementations, addressing challenges in large-scale systems, machine learning, and operations research.

Deep RayView profile

Assistant Professor

• Applied Mathematics
• Numerical Methods
• Machine Learning
• +3 more

Deep Ray is an Assistant Professor of Mathematics at the University of Maryland, College Park (UMD), with a joint appointment at the Institute for Physical Science and Technology (IPST). His research focuses on the intersection of numerical analysis and machine learning, particularly in developing robust numerical methods for partial differential equations (PDEs) and enhancing computational efficiency through AI integration. He has expertise in entropy-stable schemes, shock-capturing algorithms, and operator learning for surrogate modeling. Education and Teaching: Ray has taught advanced courses in computational methods, data science, and numerical analysis at institutions including UMD, University of Southern California (USC), and École Polytechnique Fédérale de Lausanne (EPFL). He has also developed specialized courses like Machine Learning and Computational Physics and contributed to workshops on scientific computing and deep learning. Research Interests: His work spans scientific machine learning, hyperbolic conservation laws, and physics-informed AI. Recent projects include fusion of wildfire models with satellite data, entropy-stable schemes for fluid dynamics, and Bayesian inference via generative adversarial networks (GANs). He actively collaborates on open-source tools, including a GitHub repository for deep learning tutorials. Labs and Teams: Ray leads a research group exploring AI-driven numerical methods and interdisciplinary computational challenges. His lab emphasizes practical applications in fluid dynamics, inverse problems, and reduced-order modeling.

N Sri NamachchivayaView profile

Professor

• Multi-scale Modeling
• Stochastic Dynamical Systems
• Stability Theory
• +3 more

Dr. N. Sri Namachchivaya is Professor of Applied Mathematics at the University of Waterloo with PhD from University of Waterloo. His research develops mathematical frameworks for stochastic dynamical systems, focusing on stability analysis, bifurcation theory, and multi-scale modeling. Recipient of NSF Presidential Young Investigator Award and multiple distinguished professorships, he has secured $8M+ in research funding and published over 250 scholarly works. Research examines noise-induced phenomena in nonlinear systems using asymptotic methods, dimensional reduction, and computational techniques. Current projects investigate stochastic bifurcations in fluid-structure systems, data assimilation for chaotic systems, and filtering algorithms for multiscale dynamics. Publications demonstrate theoretical advances in stochastic stability analysis and practical algorithms for engineering systems. Recent work focuses on Hopf bifurcations in turbulent flow models, particle filtering for chaotic systems, and stability of infrastructure in turbulent conditions. Scientific Awards: NSF Presidential Young Investigator (1990) Russell Severance Springer Distinguished Professor (2011) MSRI Distinguished Professor (2007) Xerox Research Award (1989, 1993) ASME Outstanding Service Award (2007) Supervised 20 doctoral students and 21 master's students. Lectures internationally on stochastic dynamics and nonlinear systems theory. Directs the Fields-CQAM Laboratory for Inference & Prediction, developing mathematical tools for complex system analysis across physics, engineering, and environmental science domains.

• Bayesian Statistics
• Computational Statistics
• Machine Learning
• +3 more

Dr. Matt Moores is a Lecturer in Statistical Science at the University of Wollongong (UOW), affiliated with the School of Mathematics and Applied Statistics within the Faculty of Engineering and Information Sciences. He joined UOW in 2018 and holds a Docent title in Computational Statistics from LUT University (2021) and is an Elected Member of the International Statistical Institute (2022). He leads research in Bayesian computation, uncertainty quantification, and spatial statistics, with applications in energy infrastructure, environmental modeling, and spectroscopy. His academic roles include Academic Program Director for the Bachelor of Data Science & Analytics and Subject Coordinator for courses such as DSAA301 and MATH205. He co-leads a 2023 Learning & Teaching Innovation Grant focusing on work-integrated learning (WIL) and serves as an Associate Editor for Statistics & Computing . Moores is a Chief Investigator in the ARC Research Hub for Transforming Energy Infrastructure (TIDE), contributing to digital engineering advancements. Research interests span Bayesian inverse problems in oceanography, combining neural networks with Bayesian methods, sequential Monte Carlo, and approximate Bayesian computation (ABC). His publications reflect expertise in Gaussian processes, spatial statistics, and computational algorithms for intractable likelihoods. Awards include the 2023 L&TIG and recognition for contributions to statistical methodology. Teaching and supervision focus on integrating practical industry experiences with theoretical foundations, supported by grants advancing pedagogical innovation. Collaborations span academia and industry, emphasizing interdisciplinary problem-solving in energy, environment, and data science.

• Machine Learning
• Causal Inference
• High-Dimensional Statistics
• +2 more

Malte Kurz is a researcher affiliated with the University of Hamburg Business School, specifically within the Department of Statistics. His primary role was as a Researcher under the Professorship of Statistics with Applications in Business Administration. He holds a PhD in Statistics from Ludwig-Maximilians-Universität München (2018), an M.Sc. in Statistics from LMU Munich (2013), and a B.Sc. in Mathematical Finance from Universität Konstanz (2011). His research interests focus on Machine Learning , Causal Inference , High-Dimensional Statistics , Financial Econometrics , and Dependence Modelling & Copulas . He has contributed to advancements in vine copula theory, distributed machine learning frameworks, and state space models. Key publications include works on vine copula simplifications (2019), low-dimensional Kalman smoothers (2018), and distributed double machine learning architectures (2021). He has developed influential software tools like the pacotest R package for copula hypothesis testing and the DoubleML framework for Python/R. Kurz has also contributed to open-source projects such as the VineCopulaMatlab toolbox and the SSMwLS MATLAB package for state space modeling. His work bridges statistical theory and computational implementation, with applications in finance and econometrics.

• Statistics
• Machine Learning
• Bayesian Inference
• +2 more

Sean Plummer is an Assistant Professor in the Department of Mathematical Sciences at the University of Arkansas, affiliated with the Fulbright College of Arts & Sciences. His research focuses on statistical methodology, computational statistics, and Bayesian inference techniques. He holds a PhD in a relevant field (details pending) and contributes to advancing variational inference methods and their applications in complex models. His work bridges theoretical foundations and computational implementation, with recent emphasis on probabilistic modeling and algorithmic behavior analysis. Education background includes advanced training in mathematical sciences or related fields. His research interests emphasize statistical guarantees, computational properties of inference algorithms, and applications in areas like Ising models. Recent publications explore variational inference dynamics, evidence bounds in singular models, and methodological advancements in implicit models. No specific grants or awards are listed in the provided information. He advises students through the Mathematical Sciences program and collaborates on interdisciplinary projects within the department. Office located at 850 West Dickson Street, Fayetteville, AR.

Saurabh SihagView profile

Assistant Professor

• Machine Learning
• Graph Neural Networks
• Network Neuroscience
• +2 more

Saurabh Sihag is an Assistant Professor in the Department of Electrical & Computer Engineering at the University at Albany's College of Nanotechnology, Science, and Engineering. He holds a PhD from Rensselaer Polytechnic Institute and completed postdoctoral research at the University of Pennsylvania. His research develops theoretical foundations for graph-structured data processing, with applications in network neuroscience. Specific interests include covariance neural networks, graph signal processing, and explainable AI for neuroimaging analysis. Current projects focus on brain age prediction and neural network interpretability. Publications center on machine learning theory with neuroinformatics applications. Recent work demonstrates innovations in graph neural network architectures, structural equation modeling for brain networks, and transfer learning methodologies. Awards: J. Baliga Fellowship Charles M. Close '62 Doctoral Prize Laboratory Focus: Network Inference and Statistical Learning Lab Computational Neuroscience Group

• Psychometrics
• Bayesian Statistical Methods
• Educational Assessment
• +3 more

Alfonso J. Martinez is an Assistant Professor of Psychology at Fordham University's Department of Psychology. His research focuses on advanced statistical methodologies in psychometrics, particularly Bayesian approaches, latent variable modeling, and diagnostic classification systems. He contributes to improving measurement techniques in educational and psychological assessments through innovative model development and validation. His work addresses challenges in personality assessment, rapid guessing behavior in testing, and the application of structural equation modeling (SEM) across diverse contexts. He actively publishes in high-impact journals, with a focus on methodological advancements and their practical implications for assessment design. While no specific academic awards are listed, his prolific publication record reflects dedication to advancing statistical methodologies in psychology. His teaching and professional affiliations include involvement with academic organizations central to psychometric research and educational measurement.

Daniel Irving BernsteinView profile

Assistant Professor

• Combinatorics
• Discrete Geometry
• Algebraic Statistics
• +2 more

Daniel Irving Bernstein is Assistant Professor in Tulane University's Department of Mathematics. His research spans combinatorics, discrete geometry, and algebraic statistics, with particular focus on matroid theory, rigidity theory, and geometric combinatorics. He teaches undergraduate and graduate courses in linear algebra, topology, and geometric combinatorics. His research explores: Combinatorial structures in algebraic statistics Geometric rigidity and reconstructibility problems Matroid representations and lifts Algebraic methods in machine learning Recent work develops connections between combinatorial geometry and statistical learning theory. His publications demonstrate: Applications of matroid theory to statistical thresholds Geometric approaches to matrix completion Combinatorial foundations of deep learning Tropical geometric methods in phylogenetics He maintains active collaboration with computational biology and machine learning researchers.

Natalia StepanovaView profile

Professor

• High-Dimensional Statistical Inference
• Nonparametric Estimation
• Nonparametric Hypothesis Testing
• +3 more

Natalia Stepanova is a Professor in the School of Mathematics and Statistics at Carleton University. Her research focuses on high-dimensional statistical inference, nonparametric estimation, and hypothesis testing. She holds an office in Herzberg Laboratories (5229HP) and can be reached via email at nstep@math.carleton.ca . Her research interests emphasize modern challenges in statistical theory, particularly in developing adaptive methods for sparse data analysis, nonparametric models, and signal recovery. Recent work includes advancements in sup-functional analysis for empirical processes and efficient kernel-based density estimation. Stepanova’s publications (2010–2025) consistently address themes in nonparametric methods, high-dimensional data, and statistical efficiency. Key contributions include variable selection techniques, goodness-of-fit testing, and adaptive algorithms for sparse additive models. No scientific awards are explicitly listed in the provided materials. Her academic contributions span graduate advising and collaborative research within the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS). No specific grants or lab affiliations are detailed in the text.

Ulrike StegeView profile

Associate Professor

• Computational Biology
• Parameterized Complexity
• Design of Heuristics
• +4 more

Ulrike Stege is an Associate Professor and Director of the Master of Engineering in Applied Data Science (MADS) at the University of Victoria's Faculty of Engineering and Computer Science. She holds a PhD from the Swiss Federal Institute of Technology (ETH Zurich). Her research spans computational biology, parameterized complexity, algorithm design, graph theory, and cognitive psychology. She leads initiatives in quantum computing frameworks and educational tools, including projects like SCOOP (quantum optimization) and QGrover (quantum algorithm visualization). Her work also addresses RNA pseudoknot structure prediction and the integration of quantum computing into combinatorial optimization software. Key educational contributions include developing browser-based quantum learning tools (e.g., QNotation and QuantumCrypto) and promoting computational thinking in K-12 education. Her research bridges theoretical computer science with practical applications in biology and quantum systems, emphasizing algorithmic innovation and interdisciplinary collaboration. Her publications focus on advancing quantum computing frameworks, optimizing bioinformatics algorithms, and creating accessible educational resources. Recent work addresses quantum annealing for constrained optimization, structural biochemistry of viral RNA, and hybrid quantum-classical problem-solving methods.