جستجوی استاد راهنمای دانشگاهی

Qiyang Han is an Associate Professor in the Department of Statistics at Rutgers, The State University of New Jersey, within the School of Arts and Sciences. His research lies at the theoretical interface of statistics, probability, and algorithms, with a strong focus on foundational and high-dimensional problems. His research interests include mathematical statistics , high-dimensional probability , empirical process theory , nonparametric and shape-restricted inference , and Bayesian nonparametrics . He also investigates high-dimensional statistics , convex optimization , and large-scale iterative algorithms , with increasing attention to gradient descent dynamics and message passing methods. His recent publications reveal a consistent focus on precise asymptotic analysis, universality phenomena, and inference under geometric constraints. Themes across his work include robustness, adaptivity, and the theoretical underpinnings of modern statistical learning in overparameterized regimes. Ph.D. in Statistics, University of Washington, 2018 Supervised by Professor Jon A. Wellner He has collaborated with prominent researchers such as C.-H. Zhang, Bodhisattva Sen, Kengo Kato, and Richard J. Samworth. His work is published in top journals including The Annals of Statistics , Journal of the Royal Statistical Society Series B , and IEEE Transactions on Information Theory . Although no formal advising list is provided, his collaborative output suggests active mentorship and research leadership. He is involved in theoretical research with implications for machine learning, signal processing, and statistical inference. His lab or research group focuses on developing rigorous mathematical frameworks for understanding complex statistical algorithms and models, particularly in high-dimensional settings.

• Mathematical Statistics
• High Dimensional Probability
• Empirical Process Theory
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Georgina Hall serves as Assistant Professor of Decision Sciences at INSEAD, holding the prestigious Patrick and Valentine Firmenich Fellowship for Business and Society. She officially joined the institution in September 2019 after completing a postdoctoral position at INRIA's DYOGENE team from January to September 2019. Her educational background includes a PhD from Princeton University's Department of Operations Research and Financial Engineering (2018), where she was a Gordon Y.S. Wu fellow under Professor Amir Ali Ahmadi's supervision. She earned both her Bachelor of Science (2011) and Master of Science (2013) from Ecole Centrale Paris, where she was valedictorian. Dr. Hall's research focuses on optimization theory, particularly polynomial optimization , semidefinite programming , and convex relaxations of NP-hard problems . Her work bridges theoretical mathematics with practical applications in business decision-making. She has made significant contributions to shape-constrained regression using sum of squares polynomials, demonstrating how semidefinite programming hierarchies can effectively fit shape-constrained polynomials to noisy data. Her recent publications reveal a research trajectory that increasingly connects optimization theory with machine learning applications and supply chain decision-making. The 2025 Operations Research paper on shape-constrained regression represents her theoretical contributions, while the Management Science paper demonstrates practical business applications of her optimization framework. Her scientific recognition includes: Médaille de l'Ecole Centrale from the French Académie des Sciences Princeton School of Engineering and Applied Sciences Award for Excellence 2016 Informs Computing Society Prize for Best Student Paper Multiple teaching awards from Princeton University Patrick and Valentine Firmenich Fellowship for Business and Society Dr. Hall teaches Probability and Statistics at the PhD level and has received significant recognition for her teaching excellence, including the Princeton University's Engineering Council Teaching Award and the Excellence in Teaching Award from the Princeton Graduate School. Her research has practical applications in areas including optimal transport maps for color transfer tasks and estimating optimal value functions for conic programs, with real-time applications in inventory management contract negotiation.

• Polynomial Optimization
• Semidefinite Programming
• Convex Relaxations
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Zheng Fang is an Associate Professor in the Department of Economics at Emory University. His research focuses on econometrics, with expertise in statistical inference, nonparametric methods, and hypothesis testing. He holds a Ph.D. in Economics from the University of California, San Diego (2015), an MS in Statistics from the same institution (2013), and a BA and MA in Economics from Tsinghua University (2008–2010). His work emphasizes methodological advancements in econometrics, including quantile regression, shape-restricted inference, and large-scale linear system analysis. Recent contributions address topics like matrix rank testing and convex cone frameworks for hypothesis testing. Fang's research bridges theoretical developments with practical applications, often implemented in statistical software (e.g., Stata). His articles cover diverse subfields, including nonparametric estimation, bootstrapping, and concavity testing, reflecting his commitment to advancing statistical tools for economic analysis. No specific awards or grants are listed, but his CV highlights ongoing contributions to econometric theory and methodology.

• Econometrics
• Statistical Inference
• Quantile Regression
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Roles & Affiliations Professor at Department of Statistics and Actuarial Science , University of Waterloo since 2012. Serves as Associate Editor for Canadian Journal of Statistics and Metrika . Previously held roles at University of Alberta (2008-2011) and University of British Columbia (2008 postdoc). Education PhD in Statistics (2007) - University of Waterloo MA in Statistics (2004) - Nankai University BSc in Statistics (2001) - Nankai University Research Focus Specializes in statistical methodologies including finite mixture models, empirical likelihood, experimental design, biased sampling problems, capture-recapture data, and density ratio models. Current interests include statistical inference under constraints and analysis of non-probability survey samples. Awards CRM-SSC Prize in Statistics (2022) Institute of Mathematical Statistics Fellow (2022) Multiple awards for teaching and research excellence from University of Waterloo Students & Grants Advised over 10 PhD/Master’s students. Active grants include NSERC funding since 2009 and startup grants from University of Waterloo. Supervises postdocs in statistical methodology development.

• Finite Mixture Models
• Empirical Likelihood
• Experimental Design
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Brian Ling is an Assistant Professor in the Department of Mathematics and Statistics at Queen's University. His office is located in Jeffery Hall, Room 405. He holds a Ph.D. from Columbia University, an M.Phil. from The Chinese University of Hong Kong, and a B.Sc. from the same institution. His research focuses on statistical inference for incomplete and event history data, with current projects involving biased samples, cross-sectional sampling data, and process data. Methodological interests include nonparametric and semiparametric statistics, latent variable models, and shape-constrained inference. No specific awards, grants, or advising records are listed. His work is centered on advancing statistical methodologies applicable to complex data structures.

• Latent variable models
• Survival analysis
• Event history analysis
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Hammou Elbarmi is a Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, CUNY. His academic journey includes a PhD in Statistics from the University of Iowa (1993), an MS in Statistics (1989), a DEA in Applied Mathematics from University Mohamed V, Morocco, and a BS in Applied Mathematics from the same institution. PhD, Statistics, University of Iowa (1993) MS, Statistics, University of Iowa (1989) DEA, Applied Mathematics, University Mohamed V, Morocco BS, Applied Mathematics, University Mohamed V, Morocco Elbarmi specializes in Order Restricted Inference , Survival Analysis , and Categorical Data Analysis . His research focuses on statistical methods for comparing survival and cumulative incidence functions under stochastic ordering constraints, with applications to competing risks and biased sampling. He has developed novel nonparametric estimation techniques for distributions with type I/II bias and pioneered empirical likelihood approaches for hypothesis testing under inequality constraints. His work spans over two decades of funded research projects from PSC-CUNY , including grants for "Nonparametric estimation under stochastic precedence" (2020-2022) and "Consistent estimation of survival functions under uniform stochastic ordering" (2023-2024). He has delivered over 30 presentations at institutions like Rice University, University of Iowa, and Joint Statistical Meetings. Elbarmi has received the Faculty Scholarship and Creative Achievement Award at Baruch College annually from 2003-2011 and served as a Co-Chair for the Seminar Series in Statistics & Operations Research. His editorial and peer-review roles include associate editorships for journals like Statistica Sinica and Journal of Nonparametric Statistics .

• Order Restricted Inference
• Survival Analysis
• Categorical Data Analysis
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Natalya Pya Arnqvist is an Associate Professor in Mathematical Statistics at Umeå University, Sweden. She is affiliated with the Department of Mathematics and Mathematical Statistics and has active roles in research groups focused on Functional Data Analysis, Semiparametric Regression, and Statistical Learning for Spatio-Temporal Data. Her career includes prior positions at Nazarbayev University (2015-2020), University of Bath (2011-2015), and KIMEP University (2000-2005). Education : PhD in Statistics (University of Bath, UK, 2007-2010), CSc in Physical and Mathematical Sciences (Institute for Mathematics, Kazakhstan, 2000-2005). Her research centers on statistical regression modelling and functional data analysis , with significant contributions to shape constrained additive models (SCAMs) and model-based functional clustering . She has developed R packages such as scam , fdaMocca , and nilde for these methodologies. Recent publications highlight applications in applied demography , defect detection , and nonlinear state space modeling , reflecting her focus on bridging statistical theory with industrial and ecological challenges. She teaches undergraduate and graduate courses in probability, regression, and statistical learning.

• Statistical Regression Modelling
• Functional Data Analysis
• Shape Preserving Smoothing
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Yining Chen is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). He holds a PhD in Statistics from the University of Cambridge (2014). His research focuses on statistical methodology, particularly in change-point detection, nonparametric estimation, and computational aspects of statistical methods. He also explores shape-constrained estimation and time series analysis. Dr. Chen has taught courses such as ST444 Computational Data Science and ST304 Time Series and Forecasting at LSE. He has contributed to interdisciplinary research in medical statistics, including work on kidney and liver transplantation outcomes. His software contributions include R packages like 'not', 'scar', and 'Sshaped', which implement his methodological advancements in statistical computing. His research has been published in top-tier journals such as the Journal of the Royal Statistical Society and Biometrika. Key areas of application span econometrics, actuarial science, and biomedical research. He actively engages in collaborative projects addressing practical data challenges across disciplines.

• Statistical Methodology
• Change-Point Detection
• Nonparametric Estimation
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Farid Alizadeh is a Professor in the Department of Management Science and Information Systems at Rutgers School of Business, Rutgers University, and is affiliated with the Rutgers Center for Operations Research (RUTCOR). He pioneered semidefinite programming (SDP) in 1990, establishing its foundations and applications to combinatorial optimization. Education: PhD in Computer Science and Engineering, University of Minnesota (1991), Advisor: Ben Rosen Postdoctoral Associate, International Computer Science Institute (ICSI), Berkeley (1991-1993), working with Richard Karp His research spans optimization theory, including SDP, second-order cone programming (SOCP), and their applications in statistical learning (shape-constrained regression, density estimation), combinatorial optimization, and algebraic foundations. Current work focuses on extending simplex-like algorithms to SDP/SOCP for branch-and-bound methods and rule-augmented learning problems where examples incorporate feature-space regions. Scientific Awards: NSF CAREER Award (1995) INFORMS Optimization Society Farkas Prize (2014) Funded by the NSF CAREER award, he mentors graduate students in operations research at Rutgers. His teaching includes Linear Programming, Semidefinite Programming, and Statistical Methods of Business across PhD and Master's programs. As a core RUTCOR member, he collaborates on interdisciplinary optimization projects spanning theoretical computer science, control theory, and statistical applications, maintaining active research labs in both Piscataway (Livingston Campus) and Newark.

• Optimization
• Semidefinite Programming
• Combinatorial Optimization
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Shuang Zhao is an Associate Professor of Computer Science at UC Irvine, co-directing the Interactive Graphics & Visualization Lab (iGravi). He holds a PhD from Cornell University (2014) and a postdoc at MIT. His research focuses on physics-based computer graphics, scientific computing, and inverse rendering, with applications in material science, biomedicine, and robotics. Zhao's NSF CAREER Award (2023) supports his work on Physics-Based Differentiable and Inverse Rendering, enabling automated 3D reconstruction and medical imaging advancements. Education: Ph.D., Computer Science, Cornell University (2014); Postdoc at MIT. Research areas: Inverse rendering, differentiable rendering, Monte Carlo methods, and light transport modeling. Notable projects include Meta's digital twin creation for the Metaverse, non-line-of-sight imaging, and medical imaging applications. His lab develops algorithms for efficient inverse solutions and collaborates with industry (Meta, Nvidia, Adobe). Teaching includes advanced graphics courses like CS 114 and ICS 162. Awards include ACM programming contest championships and Best Paper recognitions. Students supervised include Cheng Zhang (Facebook Fellow), Kai Yan, and Zahra Montazeri. Hobbies include photography and Japanese anime/video games.

• Physics-Based Computer Graphics
• Scientific Computing
• Inverse Rendering
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Guillaume Tochon is an Assistant Professor at EPITA since 2016 and an Invited Researcher at the Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE, Observatoire de Paris) since 2022. He holds a M.Sc. and Ph.D. in electrical engineering and signal/image processing from Grenoble Institute of Technology and Université Grenoble Alpes, with postdoctoral experience at Carnegie Institution for Science and UCLA. His research focuses on mathematical morphology, statistical signal processing, and machine/deep learning, applied to space imagery (e.g., astrometry) and satellite data analysis. He leads projects on morphological neural networks, Sentinel-2 time series dynamics, and meteor/satellite detection using 1-second exposure imagery. Education: M.Sc. in Electrical Engineering, Grenoble Institute of Technology (2012) Ph.D. in Signal and Image Processing, Université Grenoble Alpes (2015) Visiting Scholar, UCLA Department of Mathematics (2013) Research Interests: Combines mathematical morphology with machine learning to address challenges in remote sensing and space imagery. Key areas include hierarchical representations, noise analysis, and applications in astrometry and hyperspectral unmixing. Current projects involve neural Koopman operators for forecasting, Sentinel-2 time-series assimilation, and meteor detection in night-sky imagery. Teaching: At EPITA, he teaches Mathematics for Signal Processing, Data Compression, Image Processing, Convex Optimization, Machine Learning, and Constrained Optimization across undergraduate and graduate levels. Collaborations: Works with researchers at IMCCE (e.g., V. Lainey, J. Vaubaillon), IMT-Atlantique, and Grenoble-INP. Supervises PhD students in topics such as noise estimation, spectral dynamics, and Cassini image analysis. Labs/Teams: Active in EPITA Research Laboratory's Image Processing and Pattern Recognition team and IMCCE's PEGASE team.

• Mathematical Morphology
• Statistical Signal Processing
• Machine Learning
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Shakeel Gavioli-Akilagun is a Research Fellow in the Department of Statistics at the London School of Economics and Political Science (LSE), part of the Time Series and Statistical Learning research group. He holds a BSc in Economics and Econometrics from the University of York and an MSc in Statistics (Research) from LSE, where he completed an ESRC studentship. His primary research focuses on changepoints, feature detection, multiscale statistics, and causal inference, with applications in time series analysis and machine learning. His academic contributions include developing algorithms for non-standard change point problems and has published in journals like Electronic Journal of Statistics and Journal of the Royal Statistical Society Series B . He teaches courses such as Distributed Computing for Big Data and Graph Data Analytics at LSE. Recent invited talks include presentations at the Institute of Mathematical Statistics Asia Pacific Rim Meeting (2026) and the EcoSta conference on change point detection (2025).

• Changepoints
• Feature Detection
• Multiscale Statistics
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Rémi CARMINATI is a Professor at ESPCI Paris, affiliated with the Institut Langevin. His research focuses on wave propagation in complex media, light scattering dynamics, and nano-optics. He has contributed to understanding time-varying disorder, polarization depolarization mechanisms, and thermal radiation at micro/nanoscales. Key Research Themes : Wave Theory, Light Scattering in Correlated Media, Nano-optics, Anderson Localization, and Photonic Hyperuniformity. The 15 most recent articles demonstrate expertise in wave dynamics across temporal/spatial disorder, nonlinear photon diffusion, polarization statistics, and applications in thermal imaging, sensing, and metamaterial design. His work bridges theoretical optics with experimental validation using digital holography, photon Doppler velocimetry, and near-field microscopy. Rémi CARMINATI is actively involved in Institut Langevin research, collaborating on projects spanning electromagnetic field correlations, plasmonic nanostructures, and coherence-based imaging through scattering media. His methodological innovations include dwell-time operators for energy storage, reciprocity-based LDOS mapping, and diagrammatic approaches for photon echo analysis.

• Wave Theory
• Scattering and Transport of Light in Complex Media
• Nano-optics

Professor Harry Zheng is a Professor of Mathematics at Imperial College London's Faculty of Natural Sciences, Department of Mathematics. He is affiliated with the CFM-Imperial Institute of Quantitative Finance and Mathematical Finance. His research focuses on stochastic control, optimization, and financial mathematics with applications in banking, numerical methods, and statistics. Key research areas include stochastic control theory, mean field games, financial derivative pricing, and machine learning integration in financial systems. His work addresses optimal investment strategies, risk management, and systemic risk modeling in dynamic markets. Notable contributions include applying deep learning to solve high-dimensional stochastic control problems and analyzing governance dynamics using mean field frameworks. Recent publications span innovations in neural network-based solvers for Hamilton-Jacobi-Bellman equations, robust optimization under uncertainty, and behavioral finance models incorporating transaction costs and prospect theory. Professor Zheng collaborates with institutions like the CFM-Imperial Institute to advance quantitative finance methodologies. His research bridges theoretical advancements with practical applications in portfolio management, risk assessment, and computational finance.

• Stochastic Control
• Financial Mathematics
• Optimization
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Dr. David Mguni is a Lecturer in Computer Science at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science. His research focuses on multi-agent systems, reinforcement learning, game theory, and decision-making under uncertainty. He holds a PhD in applied mathematics from University College London and prior degrees from the University of Cambridge and the University of London. Before academia, he was a Principal Scientist at Huawei, leading the Multi-Agent Systems team in London. His work bridges theoretical foundations with practical applications, particularly in strategic decision-making and autonomous systems. He teaches the ECS656U/ECS796P Distributed Systems course and supervises students in reinforcement learning, game theory, and optimization. Research interests include non-zero-sum game frameworks, safe reinforcement learning (e.g., SAUTE RL), and reward shaping techniques. His publications span venues like AAMAS, NeurIPS, and IJCAI, addressing challenges in multi-agent coordination, adversarial environments, and algorithmic efficiency. Collaborations involve designing scalable methods for large systems and integrating language models into decision-making processes. David is part of Queen Mary’s Centre for Multimodal AI, contributing to projects on socially-attentive policies, risk-aware systems, and distributed optimization. His recent work emphasizes safety in multi-agent learning (e.g., SAUTE RL), variance reduction in estimator models, and frameworks for managing adversarial scenarios. He actively seeks PhD candidates to explore reinforcement learning and game-theoretic applications.

• Multi-Agent Systems
• Reinforcement Learning
• Game Theory
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