جستجوی استاد راهنمای دانشگاهی

LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.

• Responsible AI
• Dynamic Decision-Making
• Neurocognitive Modeling
• +۳ مورد دیگر

Ali Feizmohammadi is an Assistant Professor, Teaching Stream (LTA) in the Department of Mathematics at the University of Toronto Mississauga, affiliated with the Mathematical and Computational Sciences division. His research focuses on inverse problems, partial differential equations, and geometric analysis. He holds a position emphasizing teaching excellence within the university's framework. His work addresses advanced mathematical challenges such as coefficient identification in subdiffusion equations, fractional Laplacian problems on Riemannian manifolds, and nonlinear elliptic equations on manifolds. Recent articles highlight contributions to the Calderón problem in various contexts, wave equation control, and spacetime finite element methods. No scientific awards or grants are explicitly listed in the provided information. He has not yet listed advisees in the available data. His research trends emphasize rigorous mathematical analysis of inverse problems in both classical and fractional PDE frameworks, with applications to geometric and control-theoretic questions. Dr. Feizmohammadi's work spans theoretical advancements in inverse problems, numerical methods for control systems, and the interplay between differential geometry and PDEs. His contributions address both fundamental theory and applied methodologies in mathematical physics and engineering.

• Inverse Problems
• Partial Differential Equations
• Mathematical Physics
• +۳ مورد دیگر

Heather Battey is a Professor in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. Her work bridges foundational statistical theory with practical scientific applications, focusing on parametrization effects, sparsity, and high-dimensional inference. Education PhD, University of Cambridge (2008-2011) Research Interests Battey's research examines how model structure and parametrization influence inferential procedures, particularly in high-dimensional settings. She investigates the equivalence between sparsity and reparametrization, and challenges traditional Fisherian statistical abstractions through modern practices. Her publications reveal a pattern of innovation in high-dimensional regression, covariance matrix analysis, and statistical methodology for complex data. Collaborations span disciplines including machine learning, economics, and biomedical research. Scientific Awards Fellow of the Institute of Mathematical Statistics (2023) EPSRC Early Career Research Fellowship (2020-2026) EPSRC Postdoctoral Research Fellowship (2017-2020) Advising and Grants Battey supervises PhD students Charlotte Edgar, Jakub Rybak, and Rebecca Lewis, with informal guidance to Henrique Hoeltgebaum. Over 15 pre-doctoral researchers have been mentored in topics ranging from support vector machines to spatial point processes. Current funding includes an EPSRC grant for theoretical foundations of inference with nuisance parameters and prior support for covariance matrix inference.

• Statistical Theory
• High-Dimensional Inference
• Sparsity in Statistics
• +۳ مورد دیگر

Rahul Sarkar is a Postdoctoral Fellow at the University of California, Berkeley, affiliated with the Department of Mathematics . He was previously a Ph.D. student in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University, graduating in 2022 under the advisement of Biondo Biondi and András Vasy. Research Interests : Quantum information theory, inverse problems, machine learning, microlocal analysis, and numerical methods for PDEs. Scientific Contributions : Developed novel quantum computing algorithms and numerical schemes for geophysical imaging, with applications in seismic tomography and quantum signal processing. Teaching : Taught courses at Stanford including Introduction to Quantum Computing and 3D Seismic Imaging , with roles as instructor and course assistant. Awards : Schlumberger Innovation Fellowship (2019-2020). His work bridges mathematical analysis and quantum computation , with a focus on solving real-world problems through interdisciplinary approaches. He has collaborated with institutions like IBM and Schlumberger to apply quantum algorithms to geoscience and financial optimization.

• Quantum Information Theory
• Microlocal Analysis
• Machine Learning
• +۳ مورد دیگر

Scientia Professor Gary Froyland is a Professor at the University of New South Wales (UNSW), affiliated with the School of Mathematics & Statistics. He leads the ARC Laureate Centre for Dynamical Systems and Data and holds an Einstein Visiting Fellowship from the Einstein Foundation Berlin. His academic credentials include a BSc (Hons 1, Medal) in Pure and Applied Mathematics from the University of Queensland and a PhD in Mathematics from the University of Western Australia. Professor Froyland's research spans two primary domains: dynamical systems and optimization. In dynamical systems, he investigates the interplay of probability and geometry in nonlinear and chaotic systems, employing tools from ergodic theory, functional analysis, and differential geometry. His work extends to applications in oceanography, atmospheric science, and granular flows. In optimization, he focuses on decision-making in complex systems with uncertain information, developing novel approaches in mathematical programming that have been applied to mining, logistics, and medical treatment planning. His recent publications demonstrate a strong focus on coherent structures in dynamical systems, linear response theory, and applications to geophysical phenomena. The research shows increasing interdisciplinary collaboration, particularly with climate scientists and data analysts, reflecting a trend toward applying advanced mathematical techniques to real-world problems in environmental science and engineering. J.D. Crawford Prize (2025) Elected Member of the Academy of Europe / Academia Europaea (2024) ARC Laureate Fellow (2024-2029) Fellow of the Society for Industrial and Applied Mathematics (SIAM) (2021) Fellow of the Australian Academy of Science (2020) Vice-Chancellor's Award for Teaching Excellence - Postgraduate Research Supervision (2015) Professor Froyland actively supervises PhD and honors students, with current advisees including Kevin Felipe Kühl Oliveira, Nicholas Peters, and Kathrin Völkner. His research is supported by multiple grants, including an ARC Laureate Fellowship (2024-2029) for "Breakthrough mathematics for dynamical systems and data," an Einstein Visiting Fellowship (2022-2026), and several ARC Discovery Projects. His work has practical applications in climate science, mining optimization, and medical treatment planning, particularly in radiotherapy. He leads the ARC Laureate Centre for Dynamical Systems and Data, which brings together researchers to develop new mathematical approaches for analyzing complex dynamical systems. The center focuses on creating methods to identify coherent structures in spatiotemporal data, with applications spanning environmental science, social science, health science, and engineering.

• Dynamical Systems
• Machine Learning
• Optimisation
• +۱۲ مورد دیگر

Anthony Man-Cho So is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He currently serves as Dean of the Graduate School and Deputy Master of Morningside College . With a BSE from Princeton University and a PhD in Computer Science from Stanford University, his career at CUHK began in 2007. Academic Leadership: Dean, Graduate School (2023–present); Deputy Master, Morningside College (2019–present) Education: BSE (Princeton), MSc/PhD (Stanford) His research focuses on optimization theory and its interdisciplinary applications in computational geometry, machine learning, signal processing, and statistics. Key projects include non-convex optimization for wireless networks, robust graph learning, and decentralized learning algorithms. His publications span high-impact journals like Mathematical Programming , SIAM Journal on Optimization , and conferences such as NeurIPS and ICML . Recent work emphasizes dynamic regret analysis , low-rank matrix recovery , and stochastic beamforming . He has authored over 50 refereed papers and a monograph on semidefinite programming. Awards include IEEE Fellow (2023), CUHK Research Excellence Award (2016–17), and multiple IEEE/INFORMS best paper and teaching accolades. He has served on editorial boards of journals like Mathematical Programming and SIAM Journal on Optimization , and as Lead Guest Editor for IEEE Signal Processing Magazine . Teaching roles include courses on optimization, discrete mathematics, and machine learning. Scientific Awards IEEE Fellow (2023) CUHK Outstanding Fellow (2019) Multiple IEEE/INFORMS Best Paper Awards (2010–2022) IEEE/UGC Teaching Awards (2008–2022) His methodology integrates theoretical rigor with practical applications, particularly in wireless communication systems, sensor networks, and financial engineering. Collaborations span institutions in Hong Kong, mainland China, and the U.S., reflecting a global academic influence.

• Optimization
• Algorithm Design
• Data Analytics
• +۷ مورد دیگر

Associate Professor LIN Zhenhua serves as a Presidential Young Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), with additional affiliation at the Institute of Data Science since 2021. His research develops cutting-edge statistical methodologies for complex data structures across multiple domains. Dr. LIN completed his Ph.D. at the University of Toronto in 2017 under Fang Yao's supervision, following M.Sc. degrees from Simon Fraser University (2013, 2010) and a B.Sc. from Fudan University (2008). Ph.D., University of Toronto, 2017 (Advisor: Fang Yao) M.Sc., Simon Fraser University, 2013, 2010 B.Sc., Fudan University, 2008 His research program spans functional data analysis (developing techniques for curves and surfaces), non-Euclidean data analysis (statistical methods on manifolds), high-dimensional statistics (p > n problems), and constrained statistical modeling. LIN's work bridges theoretical statistics with practical applications through rigorous mathematical frameworks and computational implementations. Recent publications reveal strong emphasis on bootstrap methods for high-dimensional inference, Riemannian geometry approaches for manifold-valued data, and innovative functional data techniques. His research shows consistent output with multiple 2025 publications in top journals including Biometrika, Bernoulli, and Journal of the American Statistical Association. Professional Recognition Presidential Young Professor, NUS (2019-present) Associate Editor, Bernoulli (2022-2024) Associate Editor, Statistics (2023-present) Young Researchers Committee, Bernoulli Society (2020-2024) Professor LIN actively mentors graduate students as evidenced by numerous collaborative publications with trainees. He teaches advanced courses including ST5215 Advanced Statistical Theory, DSA4211 High-dimensional Statistical Analysis, and ST5223 Statistical Models across multiple academic years. His research group develops specialized software packages including hdanova, matrix-manifold, synfd, mcfda, and iRFDA, making advanced statistical methods accessible to practitioners.

• Functional data analysis
• Non-Euclidean data analysis
• High-dimensional data analysis
• +۳ مورد دیگر

Tomaso A. Poggio is the Eugene McDermott Professor in the Department of Brain and Cognitive Sciences at the Massachusetts Institute of Technology , an investigator at the McGovern Institute for Brain Research , a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) , and the director of both the MIT Center for Biological and Computational Learning (CBCL) and the multi-institutional Center for Brains, Minds and Machines (CBMM) . Research Interests Poggio’s research is fundamentally interdisciplinary, sitting at the intersection of computational neuroscience , machine learning , and computer vision . His work is driven by the conviction that learning is the core gateway to both biological intelligence and artificial systems. Current themes include: Mathematical foundations of statistical learning theory Engineering applications in computer vision, graphics, bioinformatics, and intelligent search Computational neuroscience of visual object recognition and the ventral stream of the visual cortex Scientific Awards & Honors Eugene McDermott Professorship, MIT Advising & Grants Over three decades, Poggio has advised a large cohort of doctoral and master’s students whose theses span machine learning, computer vision, neuroscience, and bioinformatics. Representative graduates include H. Jhuang, Stanley Bileschi, Jacob Bouvrie, Jennifer Louie, M. Kouh, Sayan Mukherjee, Ryan Rifkin, Alexander Rakhlin, Thomas Serre, Gene Yeo, and many others. His research has been continuously supported by major federal and private funding initiatives, most recently through the multi-institutional Center for Brains, Minds and Machines (CBMM) headquartered at the McGovern Institute since 2013. Laboratories & Teams Poggio directs the Poggio Lab (CBCL) at MIT, an interdisciplinary group comprising neuroscientists, computer scientists, mathematicians, and engineers. The lab collaborates closely with experimental neuroscientists to develop predictive computational theories of visual cortex function and to translate those insights into practical algorithms for computer vision and machine learning.

• Computational Neuroscience
• Machine Learning
• Computer Vision
• +۵ مورد دیگر

Hans-Georg Mueller is a Professor in the Department of Statistics at the University of California, Davis. His research spans multiple domains of modern statistical methodology, with groundbreaking contributions to functional data analysis, metric statistics, and nonparametric inference for random objects. Key research areas include Fréchet regression, distributional data analysis, network regression, and optimal transport Applications span longitudinal growth studies, brain development, aging and longevity, plant genomics Research Interests : He has pioneered methods for analyzing complex data structures such as functional data, manifold-valued data, and random objects. His work on the PACE approach for longitudinal data has become foundational in the field. Recent Publications demonstrate strong trends in Fréchet analysis, metric statistics, and distributional data modeling, with applications in both biomedical and environmental domains. Books and Edited Works : Author of the foundational monograph Nonparametric Regression Analysis for Longitudinal Data (1988), and co-editor of influential volumes including Change-point Problems (1994) and Mathematical Modeling in Experimental Nutrition (1998).

• Functional Data Analysis
• Metric Statistics
• Fréchet Regression
• +۸ مورد دیگر

Prof. Vladimir Spokoiny is a leading figure in stochastic algorithms and nonparametric statistics at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) and Humboldt University of Berlin . His work bridges mathematical statistics with practical applications in finance, medicine, and machine learning. Born in 1959 in Moscow, USSR PhD from Lomonosov Moscow State University (1988) Habilitation from Humboldt University (1996) Head of WIAS research group since 2000 Professor at Humboldt University since 2002 Spokoiny's research focuses on adaptive nonparametric methods, high-dimensional data analysis, and statistical finance. His innovations in local homogeneity testing and propagation-separation methods have advanced volatility modeling, image analysis, and manifold learning. He employs Bayesian optimization frameworks and stochastic control techniques for financial instrument pricing. Recent scientific contributions include generalized bootstrap procedures for Bures-Wasserstein barycenters (2024), dimension-free Laplace approximation bounds (2023), and structure-adaptive manifold estimation (2022). His 19+ PhD students and editorial roles in top journals like The Annals of Statistics demonstrate sustained academic impact. International Statistical Institute member American Statistical Association fellow Institute of Mathematical Statistics member Bernoulli Society member

• Adaptive nonparametric statistics
• High-dimensional data analysis
• Financial time series modeling
• +۵ مورد دیگر

Professor Alexander Tumanov is a faculty member in the Department of Mathematics at the University of Illinois at Urbana-Champaign, affiliated with the College of Liberal Arts & Sciences. He holds the rank of Professor and has been active in research and teaching since at least 2000. His current teaching includes Math 220 Calculus in Fall 2025. Education & Affiliations: His academic career is deeply rooted at the University of Illinois, with no indications of former roles or retirement status. Research Interests: Tumanov specializes in Several Complex Variables, Differential Geometry, and Partial Differential Equations. His work explores topics such as CR mappings, stationary discs, pseudoholomorphic curves, symplectic geometry, and nonlinear PDEs. Notable contributions include studies on automorphism groups of bounded domains, boundary value problems, and applications of Gromov's methods in symplectic topology. Publications: His recent work (2025–2013) focuses on advanced topics like non-linear Lie groups, q-convex manifolds analysis, outer billiards dynamics, and symplectic non-squeezing in infinite dimensions. Earlier contributions include boundary regularity for elliptic equations and minimal energy configurations on spheres. Awards & Grants: No specific awards are listed in the provided materials, though his extensive publication record reflects sustained scholarly impact. No grants or advising data are explicitly mentioned. Labs & Teams: Tumanov collaborates extensively with researchers like A. Sukhov, L. Baracco, and B. Coupet, as evidenced by co-authored papers. His work intersects pure mathematics with geometric analysis and mathematical physics.

• Several Complex Variables
• Differential Geometry
• Partial Differential Equations
• +۳ مورد دیگر

Katya Krupchyk is a Professor in the Department of Mathematics at the University of California, Irvine (UCI). Her research focuses on inverse problems, partial differential equations (PDEs), microlocal analysis, and spectral theory. She holds a position in the Analysis and Partial Differential Equations group at UCI. Her work often involves collaborations with leading institutions and researchers globally, addressing challenges in mathematical physics, geometric inverse problems, and nonlinear analysis. Dr. Krupchyk teaches advanced courses in real analysis, functional analysis, and partial differential equations. She has contributed to editorial boards for journals such as Journal of Spectral Theory , SIAM Journal on Mathematical Analysis , and Inverse Problems and Imaging . Her research spans theoretical and applied aspects of inverse problems, including studies on fractional operators, magnetic Schrödinger equations, and anisotropic media. Recent work emphasizes high-frequency analysis, nonlinear perturbations, and reconstruction algorithms for geometric inverse problems.

• Inverse Problems
• Partial Differential Equations
• Microlocal Analysis
• +۳ مورد دیگر

Agostino Capponi is a Professor of Industrial Engineering and Operations Research at Columbia University, affiliated with Columbia Engineering and the Data Science Institute (DSI). He holds academic fellowships at the Luohan Academy (Alibaba Group) and the Fintech@Cornell Center. His research focuses on systemic risk, financial technology, blockchain economics, and machine learning applications in finance. He has authored a best-selling book on machine learning in financial markets and received prestigious awards including the NSF CAREER Award and the JP Morgan AI Faculty Research Award. Education: Master's and PhD in Computer Science and Applied & Computational Mathematics from Caltech (2006-2009). Professional roles include Editor of Management Science , co-editor of Mathematics and Financial Economics , and leadership positions in the Bachelier Finance Society and INFORMS Finance Section. His research has been funded by NSF, DARPA, J.P. Morgan, Ethereum Foundation, and others. Research interests span blockchain governance, decentralized finance protocols, and systemic risk mitigation in financial networks. Notable contributions include work on liquidity risk, crypto-economic systems, and causal inference in financial modeling. Media coverage includes American Banker, Vox, and Chicago Booth Review. He holds a patent in military network tracking and served as a visiting scholar at the Federal Reserve Bank of New York.

• Systemic Risk
• Financial Technology
• Blockchain and Crypto Economics
• +۴ مورد دیگر

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +۳ مورد دیگر

Brian Street is a Professor in the Department of Mathematics at the University of Wisconsin-Madison . He specializes in partial differential equations, harmonic analysis, and geometric analysis, with a focus on subelliptic and multi-parameter singular integral theories. His work extends classical results like the Newlander-Nirenberg theorem and Frobenius theorem to quantitative and boundary settings. Research Highlights: Development of maximal subellipticity theory (2023), generalization of Calderón-Zygmund singular integrals to multi-parameter settings (2011–2013), and novel approaches to hypoelliptic and mixing flow problems. Publications: Authored a monograph on Multi-parameter Singular Integrals (Princeton University Press, 2012) and co-authored influential papers with leading mathematicians in functional analysis and PDEs. Methodologies: Employed techniques from ODEs, PDEs, and harmonic analysis, including wave equation propagation, non-isotropic Sobolev spaces, and diffeomorphism-invariant frameworks.

• Mathematics
• Partial Differential Equations
• Singular Integrals
• +۷ مورد دیگر