جستجوی استاد راهنمای دانشگاهی

Dr. Daniel J. Hsu is a Professor of Computer Science at Columbia University, affiliated with the Foundations of Data Science Center and TRIPODS Institute. His research focuses on algorithmic statistics, machine learning theory, and their applications in public health informatics. He has advised numerous students and postdocs, and his work bridges foundational theory with practical systems like foodborne illness detection via social media analysis. Key roles: Associate Editor (ACM Transactions on Algorithms), Program Chair (ICML 2025, COLT 2019) Research areas: Foundations of Data Science, Machine Learning Theory, Fairness, and High-dimensional Statistics His work on detecting foodborne illnesses using Yelp reviews has been deployed by NYC Health departments. Recent contributions include advancements in transformer architectures, group fairness algorithms, and multi-group learning frameworks. Scientific awards include the Sloan Fellowship and multiple NSF grants. He has pioneered interactive machine teaching methods and developed algorithms for robust parameter estimation in high-dimensional settings.

• Machine Learning
• Foundations of Data Science
• Theory of Computation
• +۴ مورد دیگر

Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.

• High-dimensional Statistics
• Causal Inference
• Machine Learning
• +۳ مورد دیگر

Trevor Campbell is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver. He holds a Ph.D. in Machine Learning and Statistics from MIT and a B.A.Sc. in Aerospace Engineering from the University of Toronto. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, and streaming data analysis. Campbell is a core contributor to probabilistic programming tools like Pigeons.jl and has developed influential methods for coreset-based Bayesian inference. Education : Ph.D. in Machine Learning and Statistics (2016), MIT M.S. in Aeronautics and Astronautics (2013), MIT B.A.Sc. in Aerospace Engineering (2011), University of Toronto Research Interests : His work emphasizes scalable Bayesian computation, including variational inference, MCMC optimization, and coresets. He develops algorithms that balance statistical accuracy with computational efficiency, particularly for large-scale datasets. His recent work explores adaptive samplers (e.g., AutoStep, autoMALA) and theoretical guarantees for coreset methods. Applications span astrophysics, materials science, and network analysis. Awards & Grants : NSERC Discovery Grant (2025) Blackwell-Rosenbluth Award (2021) PIMS Early Career Award (2023) Google Perception Academic Funding (2020) Advising & Labs : He supervises a team of PhD and M.Sc. students at UBC, focusing on Bayesian methodology and computational tools. His lab collaborates with institutions like SFU and MIT on projects involving distributed sampling and probabilistic modeling. He co-organizes workshops on Bayesian computation and serves on editorial boards for Bayesian Analysis and TMLR.

• Bayesian Inference
• Bayesian Nonparametrics
• Machine Learning
• +۵ مورد دیگر

Nancy Margaret Reid is a University Professor of Statistical Sciences at the University of Toronto, holding the Canada Research Chair in Statistical Theory and Applications. She has served as Scientific Director of the Canadian Statistical Sciences Institute (2015–2019) and led the Department of Statistical Sciences as Chair (1997–2002). Her research focuses on theoretical statistics, particularly likelihood inference and foundational aspects of statistical methodology. Reid earned her PhD from Stanford University (1979) under Rupert G. Miller, with Brad Efron and Vernon Johns on her committee. Reid's accolades include Fellowships from the Royal Society, Royal Society of Canada, and National Academy of Sciences, as well as the Guy Medal in Gold (2022) and David R. Cox Award (2023). She has authored influential books like *Theory of the Design of Experiments* and contributed to courses on mathematical statistics and likelihood inference. Active in academic service, she teaches graduate-level courses and has advised numerous students and postdocs in theoretical and applied statistical research.

• Theoretical Statistics
• Likelihood Inference
• Design of Studies
• +۳ مورد دیگر

Aditya Guntuboyina is an Associate Professor in the Department of Statistics at the University of California, Berkeley. He has held this position since January 2012, following a postdoctoral stint at the Wharton Statistics Department and a PhD in Statistics from Yale University (2011) under Professor David Pollard. He earned his B.Stat and M.Stat degrees from the Indian Statistical Institute, Kolkata. PhD: Statistics, Yale University (2011) B.Stat/M.Stat: Indian Statistical Institute, Kolkata His research focuses on nonparametric and high-dimensional statistics , particularly shape-constrained estimation and Bayesian/Empirical Bayes methods . Key themes include convex regression, isotonic regression, mixture models, and total variation denoising. Recent publications analyze multivariate scale mixtures, convergence rates, and suboptimality of least squares in constrained settings. Aditya has supervised multiple PhD students in the Berkeley Statistics and EECS programs. He teaches courses such as Time Series Analysis (Stat 153/248), Data, Inference, and Decisions (Data 102), and advanced probability (Stat 201A). His work often intersects with machine learning, optimization, and information theory. Scientific contributions include theoretical advances in shape-restricted regression, adaptation in log-concave density estimation, and risk bounds for convex-constrained models. He has published in top journals like Annals of Statistics , Journal of the Royal Statistical Society: Series B , and IEEE Transactions on Information Theory .

• Nonparametric statistics
• High-dimensional statistics
• Shape-constrained statistical estimation
• +۱ مورد دیگر

Professor Richard Samworth is a leading academic at the University of Cambridge , affiliated with the Department of Pure Mathematics and Mathematical Statistics and serving as Director of the Statistical Laboratory . His research focuses on Nonparametric and High-dimensional Statistics , addressing challenges in data analysis, statistical learning, and computational methods. Research Interests : Richard Samworth's work emphasizes Nonparametric Statistics , High-dimensional Data , and Statistical Learning . His research spans topics such as Missing Data , Changepoint Detection , Log-concave Density Estimation , and Subgroup Analysis , with applications in Machine Learning and Data Science . Key methodologies include Score Matching , Random Projections , and Minimax Estimation . Recent publications highlight advancements in Semi-Supervised Learning , Robust Statistical Testing , and High-dimensional PCA with heterogeneous missingness. His work bridges theoretical rigor with practical applications, particularly in Statistical Algorithms and Optimization .

• Nonparametric Statistics
• High-dimensional Statistics
• Machine Learning
• +۳ مورد دیگر

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +۷ مورد دیگر

Arthur Gretton is a Professor at University College London (UCL), leading the Gatsby Computational Neuroscience Unit and serving as director of the Centre for Computational Statistics and Machine Learning. He also works as a Research Scientist at Google DeepMind. His research focuses on causal inference, representation learning, and nonparametric hypothesis testing with applications in machine learning and computational neuroscience. Academic Affiliations Gatsby Computational Neuroscience Unit, UCL Centre for Computational Statistics and Machine Learning, UCL Google DeepMind Arthur's research spans several key areas in modern machine learning, including: Kernel methods for causal effect estimation and two-sample testing Deep learning architectures for proxy causal learning with complex confounding Adaptive gradient flows for generative modeling Nonparametric statistical tests with theoretical guarantees Applications in distributional reinforcement learning and Bayesian inference His work addresses both methodological advancements and practical implementations, particularly for high-dimensional data settings. Recent publications demonstrate his focus on causal inference with hidden confounders (AISTATS 2025), deep learning adaptivity in instrumental variable regression (ICLR 2025), and novel approaches to two-sample testing with adaptive kernel selection (NeurIPS 2024). He has also contributed extensively to distributional reinforcement learning and hypothesis testing frameworks. As an advisor, he supervises multiple PhD students including Jakub Wornbard, Zikai (Steve) Shen, and Zonghao (Hudson) Chen, while co-supervising others. His methodological contributions are implemented in various software packages available at the Gatsby Unit, including tools for kernel-based covariate shift correction, independence testing, and maximum mean discrepancy calculations.

• Causal Inference
• Representation Learning
• Generative Models
• +۵ مورد دیگر

Siva Balakrishnan is an Associate Professor at Carnegie Mellon University with a joint appointment in the Department of Statistics and Data Science and the Machine Learning Department . He holds an affiliation with the Dietrich College of Humanities and Social Sciences. Previously, he was a postdoctoral researcher at UC Berkeley's Department of Statistics, advised by Martin Wainwright and Bin Yu, and earned his Ph.D. in Computer Science from CMU's Language Technologies Institute under Jaime Carbonell. His research focuses on statistical machine learning, causal inference, and high-dimensional statistics, with notable contributions to domain adaptation, optimal transport, and robust statistics. Education: Ph.D. in Computer Science, Carnegie Mellon University (Language Technologies Institute) Postdoctoral Researcher, University of California, Berkeley (Department of Statistics) Research Interests: His work bridges theoretical foundations and algorithmic development, emphasizing robust statistical methods and their applications in causal inference, public policy, and machine learning. Key areas include nonparametric methods, optimization, and high-dimensional data analysis. He has pioneered techniques in domain adaptation, such as the RLSbench framework for relaxed label shift scenarios. Awards & Grants: Amazon Research Award (2021) Google Research Scholar Award (2021) NVIDIA Pioneer Award (2018) IMS Lawrence D. Brown Student Award (2020, 2022) National Science Foundation Grants (CCF-1763734, DMS-1713003, etc.) Professional Activities: He serves as an Associate Editor for JASA and on the editorial boards of Foundations and Trends in Statistics . His work has been featured in top venues like NeurIPS, ICML, and the Annals of Statistics. He currently holds a sabbatical at UC Berkeley's Department of Statistics (Spring 2025). Labs & Collaborations: He actively participates in the Statistics and Machine Learning Reading Group and the Causal Inference Working Group , fostering interdisciplinary research in CMU's vibrant academic community.

• Statistical Machine Learning
• Causal Inference
• Public Policy & Social Sciences
• +۵ مورد دیگر

Joan Bruna is a Full Professor of Computer Science, Data Science, and affiliated Mathematics at New York University's Courant Institute and Center for Data Science. He leads the CILVR group and co-founded the MaD group. His research focuses on mathematical foundations of machine learning, deep learning, signal processing, and their applications in computational science, climate modeling, and geophysics. He holds a Ph.D. in Applied Mathematics from École Polytechnique and has held roles at UC Berkeley, the Institute for Advanced Study, and the Flatiron Institute. Education: Ph.D. (2013) and M.Sc. (2005) in Applied Mathematics, École Polytechnique and ENS Cachan; dual B.Sc./M.Sc. in Telecommunications and Mathematics from UPC Barcelona (2002–2004). Awards include the NSF CAREER Award (2019) and Alfred P. Sloan Fellowship (2018). Research interests span theoretical aspects of deep learning, optimization, and statistical methods, with applications to climate science and inverse problems. He has advised numerous PhD students and postdocs, contributing to seminal work on scattering networks, geometric deep learning, and neural ODEs. Professional service includes editorial roles at TMLR, JMLR, and TPAMI, plus program chairs for major conferences. His work bridges mathematics and machine learning, emphasizing rigorous analysis and real-world impact.

• Machine Learning
• Deep Learning
• Signal Processing
• +۵ مورد دیگر

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

• Computational Statistics
• Bayesian Statistics
• Big Data
• +۴ مورد دیگر

Peng Zhao is a tenure-track Assistant Professor in the Department of Applied Economics & Statistics at the University of Delaware, with affiliations to UD's Data Science Institute. His academic career includes a postdoctoral research position at Texas A&M University's Department of Statistics (August 2020–June 2023), following his Ph.D. training at Florida State University. Education Ph.D. in Statistics, Florida State University, 2020 B.S. in Statistics, Beijing Institute of Technology, 2015 Dr. Zhao's research focuses on cutting-edge statistical methodologies, including: High-dimensional statistical modeling and inference Network-based statistical analysis Multivariate data processing techniques Scalable Bayesian computational methods Nonparametric Bayesian approaches Dependency-aware statistical learning frameworks Optimization algorithms for complex models Regularization mechanisms in statistical learning He teaches graduate-level courses in regression analysis (STAT611) and mathematical statistics (STAT602) at the University of Delaware. Office location: Room 214, Townsend Hall, 531 S. College Avenue, Newark, DE 19716.

• High-dimensional statistics
• Statistical network models
• Multivariate statistics
• +۷ مورد دیگر

Richard E. Turner is a Professor of Machine Learning at the University of Cambridge's Department of Engineering and Research Lead for AI for Weather Prediction at the Alan Turing Institute. He serves as Cambridge Lead for the EPSRC Probabilistic AI Hub and previously held roles including Visiting Researcher at Microsoft Research, Co-Director of the AI4ER CDT, and Course Director for the Machine Learning and Machine Intelligence MPhil program. Current research focuses on probabilistic machine learning fundamentals, environmental prediction (weather/climate), and spatio-temporal modeling combining deep learning with Bayesian methods Supervised 26 PhD students (13 graduated) and 7 research assistants/associates Secured over £30M in research funding from EPSRC, Microsoft, Toyota, Google, DeepMind, Amazon, and Improbable Featured in BBC Radio 5 Live's The Naked Scientist, BBC World Service's Click, and Wired Magazine His recent publications demonstrate expertise in diffusion models for PDE simulations, Gaussian Processes for environmental applications, and Bayesian methods for spatio-temporal forecasting. Key trends include climate modeling using ML, neural PDE solvers, and scalable probabilistic inference. Awards : Cambridge Students' Union Teaching Award for Lecturing; supervised Qualcomm Innovation Fellowship winner Collaborations : Microsoft Research (AI4Science), Alan Turing Institute, EPSRC Probabilistic AI Hub Turner leads the Turner Group within Cambridge's Machine Learning Group, focusing on uncertainty-aware ML for scientific applications. Current research assistants work on topics like meta-learning, Bayesian inference, and climate science applications.

• Probabilistic Machine Learning
• Environmental Prediction
• Spatio-temporal Modelling
• +۴ مورد دیگر

Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021). His research bridges optimization theory , dynamical systems , and control theory , focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming. Recent publications explore: Higher-order Newton methods for socially responsible investment Data-efficient learning of dynamical systems Computational complexity of local minima Robust-to-dynamics optimization frameworks Award highlights include: 2024 Egon Balas Prize in Optimization 2024 Princeton Engineering Council Teaching Award 2023 Distinguished Teaching Award (Princeton SEAS) 2019 NSF CAREER Award 2017 DARPA Young Faculty Award 2017 Sloan Fellowship in Computer Science He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.

• Optimization Theory
• Control Theory
• Dynamical Systems
• +۷ مورد دیگر

Subhashis Ghoshal is a Goodnight Distinguished Professor in the Department of Statistics at North Carolina State University (NCSU). He holds a Ph.D. in Statistics from the Indian Statistical Institute (1995). His research focuses on Bayesian nonparametrics, high-dimensional models, asymptotic theory, and functional data analysis. He has authored influential books like *Fundamentals of Nonparametric Bayesian Inference* (2017) and contributed to methodologies in image processing and statistical inference. Key awards include the Goodnight Distinguished Professorship (2021), Dr. Cavell Brownie Mentoring Award (2014-15), and the De Groot Prize (2019). He has held editorial roles in journals like *Statistical Science* and *Annals of Statistics*. His work bridges theory and applications, addressing challenges in modern statistical problems such as uncertainty quantification and causal inference. He advises on graduate programs and actively contributes to academic leadership at NCSU.

• Bayesian Inference
• Nonparametric Bayesian Inference
• High-Dimensional Models
• +۴ مورد دیگر