جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Deep Learning
• Neural Networks
• +۷ مورد دیگر

Robert Jenssen is a Professor in the Machine Learning Research Group at UiT The Arctic University of Norway and serves as the Director of Visual Intelligence , an 8-year Research Council of Norway-funded SFI center. His research focuses on solving societal challenges in healthcare, marine mapping, energy, and Earth observation through collaborations with industry and public stakeholders. Director, Visual Intelligence (SFI) Center Professor, UiT Adjunct Professor, Pioneer Centre for AI (University of Copenhagen) and Norwegian Computing Center His methodological expertise spans neural networks, information-theoretic learning, self-learning, and explainable AI (XAI). Recent work emphasizes multimodal learning, uncertainty estimation, and medical image analysis. Scientific awards include: Best Paper, Pattern Recognition Letters (2024) Dissertation Award, Norwegian AI Society (2023) Best Paper, Color and Visual Computing Symposium (2022) IEEE GRS Society Letters Prize (2013) Prize for Young Researchers, University of Tromsø (2007) He contributes to international leadership as a member of the Scientific Advisory Board (SAB) for the Max Planck Institute for Intelligent Systems, France's SequoIA AI Excellence Cluster, and Denmark's DIREC center.

Ji Zhuمشاهده پروفایل

استاد

• Statistical Machine Learning
• Statistical Network Analysis
• Health Sciences
• +۳ مورد دیگر

Ji Zhu is the Susan A. Murphy Collegiate Professor of Statistics at the University of Michigan, Department of Statistics. He holds affiliations with the Michigan Institute for Data Science (MIDAS) and the Michigan Integrated Center for Health Analytics and Medical Prediction (MiCHAMP). His research focuses on statistical machine learning, network analysis, and health science applications. Education: B.Sc. in Physics (Peking University, 1996), M.Sc. and Ph.D. in Statistics (Stanford University, 2000 and 2003). Notable awards include the NSF CAREER Award (2008), Fellowships from the ASA (2013) and IMS (2015), and recognition as a Web of Science Highly Cited Researcher (2014–2020). Research interests span statistical methodologies for networks, survival analysis, and high-dimensional data. He co-authored influential papers on community detection, network cross-validation, and latent space models. Current editorial roles include Editor-in-Chief of the Annals of Applied Statistics and Action Editor for the Journal of Machine Learning Research. Advising: Supervised over 50 students and postdocs, many now in academia and industry. Notable former advisees include Tianxi Li (University of Minnesota), Yuan Zhang (Ohio State University), and Weijing Tang (Carnegie Mellon University). Labs/Teams: Active in interdisciplinary projects at MIDAS and MiCHAMP, focusing on healthcare analytics and predictive modeling for diseases like hepatitis and cardiovascular outcomes.

Simo Särkkäمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Bayesian Filtering
• +۷ مورد دیگر

Professor Simo Särkkä holds a position in Sensor Informatics and Medical Technology at the Department of Electrical Engineering and Automation (EEA), Aalto University. His research focuses on multi-sensor data processing, Bayesian filtering, machine learning, and their applications in medical technology, brain imaging, and inverse problems. He leads research groups including the Helsinki Institute for Information Technology (HIIT) and Sensor Informatics and Medical Technology. His work bridges theoretical advancements in probabilistic methods with practical implementations in healthcare and engineering. Key research interests include Gaussian processes, stochastic differential equations, quantum machine learning, and signal processing. He has contributed to advancements in algorithms for nonlinear state-space models, parallel computing techniques, and medical imaging technologies such as scatter correction in CT scans. His methodologies are applied across domains like autonomous systems, robotics, and bioengineering. Notable publications span topics like quantum-assisted Gaussian regression, physics-informed machine learning for industrial processes, and parallel-in-time numerical methods. His work emphasizes computational efficiency and robustness in high-dimensional and real-time systems.

• Causal Inference
• Representation Learning
• Generative Models
• +۵ 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.

Henry Mossمشاهده پروفایل

پژوهشگر

• Machine Learning
• Bayesian Optimization
• Theoretical Physics
• +۳ مورد دیگر

Dr Henry Moss is a Researcher at the Department of Applied Mathematics and Theoretical Physics within the School of Physical Sciences at the University of Cambridge. His work focuses on machine learning applications in climate modeling, Bayesian optimization, and Gaussian processes, bridging computational mathematics with environmental science and chemistry. His research interests include: Bayesian optimization for environmental and chemical systems Reinforcement learning in climate modeling Gaussian processes for molecular property prediction High-throughput machine learning in scientific domains Interpretable AI for coastal flooding prediction Hybrid ML-physics modeling Dr Moss's publications highlight his contributions to federated learning for climate models, sparse Gaussian process techniques, and multi-objective optimization frameworks. These works span applications in weather prediction, chemical engineering, and oceanography. Email: hwm26@cam.ac.uk

• Machine Learning
• Deep Learning
• Theoretical Foundations of AI
• +۱۱ مورد دیگر

Mikhail (Misha) Belkin is a Professor at the Halicioglu Data Science Institute (HDSI) at the University of California San Diego , with an affiliated appointment in the Department of Computer Science and Engineering . He is also an Amazon Scholar , reflecting his impactful industry collaboration. Since January 2024, he has served as the Editor-in-Chief of the SIAM Journal on Mathematics of Data Science (SIMODS) . Research Interests: Belkin's research centers on the theoretical foundations of machine learning, particularly the mathematical understanding of modern deep learning. His work investigates interpolation , over-parameterization , and feature learning in neural networks. He is renowned for introducing the double descent risk curve, which reconciles classical bias-variance trade-offs with the success of overfitted models. His recent work identifies the Average Gradient Outer Product (AGOP) as a fundamental mechanism of feature learning, applicable across architectures like CNNs and transformers. Scientific Contributions and Trends: His recent publications, appearing in Science , PNAS , and NeurIPS , demonstrate a strong trend toward unifying theories of generalization and optimization in over-parameterized systems. He explores how interpolating models can be statistically optimal, how gradient descent converges in non-convex landscapes via the PL* condition, and how kernel methods can be enhanced to perform feature learning. ACM Fellow (2023) Editor-in-Chief, SIAM Journal on Mathematics of Data Science (2024–present) Advising and Grants: Belkin actively mentors students and collaborators such as Adityanarayanan Radhakrishnan , Daniel Beaglehole , and Chaoyue Liu , who are frequent co-authors. He is a Principal Investigator (PI) in the Collaboration on the Theoretical Foundations of Deep Learning , funded by the NSF and Simons Foundation. He is also an external collaborator with the Eric and Wendy Schmidt Center at the Broad Institute and part of the NSF-funded TILOS AI Institute . Laboratories and Teams: While not explicitly named, his research group at UCSD is deeply involved in theoretical machine learning, focusing on the intersection of statistics, optimization, and deep learning. His work often involves large-scale collaborations and is closely tied to initiatives like SIMODS and TILOS.

Siva Balakrishnanمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Causal Inference
• Public Policy & Social Sciences
• +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.

David Duvenaudمشاهده پروفایل

دانشیار

• Machine Learning
• Generative Models
• Bayesian Inference
• +۵ مورد دیگر

David Duvenaud is an Associate Professor at the University of Toronto , holding a Canada Research Chair in Generative Models and a Schwartz Reisman Chair in Technology and Society . He is cross-appointed to the Department of Computer Science and Department of Statistical Sciences . A Sloan Research Fellow and founding member of the Vector Institute , his work bridges deep probabilistic models , AI safety , and scientific computing . PhD in Machine Learning (University of Cambridge, 2014) Postdoc in Hyperparameter Optimization (Harvard University, 2016) Co-founded Invenia (energy forecasting company) His research spans foundational Neural Ordinary Differential Equations (NeurIPS 2018 Best Paper) and Automatic Chemical Design (ACS Central Science 2018) to recent work on AGI governance (2025) and AI safety (2024). Key contributions include stochastic variational inference , implicit differentiation frameworks , and antisymmetrization layers for quantum Monte Carlo. Recent publications (2024-2025) focus on systemic existential risks from AI , many-shot jailbreaking attacks , and epistemic uncertainty quantification . His group trains energy-based models with scalable MCMC samplers and develops invertible neural architectures (e.g., Residual Flows NeurIPS 2019). He also explores human-AI alignment through LLM Processes (NeurIPS 2024) and Sycophancy in Language Models (ICLR 2024). Canada Research Chair (2025) NSERC Grant (2025) Sloan Research Fellow (2021) Schwartz Reisman Chair (2021) Best Paper Award (NeurIPS 2018) Distinguished Paper Award (ICFP 2021) His students include James Requeima , Jesse Bettencourt , and Raymond Douglas . He teaches courses on Statistical Methods for Machine Learning and Differentiable Inference . Current work (2025) investigates systemic human disempowerment through incremental AI capabilities and sabotage risk mitigation via hyperparameter-aware evaluations.

Joan Brunaمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Signal Processing
• +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.

• Computational Physics
• Machine Learning
• Applied Mathematics
• +۷ مورد دیگر

Andrew M. Stuart is a Professor at the California Institute of Technology's Division of Engineering and Applied Science. His research bridges computational mathematics, machine learning, and physical modeling, focusing on inverse problems, partial differential equations, and multiscale systems. He has pioneered methodologies integrating Gaussian processes, Kalman inversion, and neural operators for scientific computing. His recent publications highlight innovations in competitive protein dimerization networks, nonlinear Bayesian inference, and operator learning. Articles span applications in materials science, geophysics, and biochemical signal processing, emphasizing data-driven discovery of differential equations and scalable algorithms for high-dimensional problems. Stuart's work addresses challenges in structural error modeling, uncertainty quantification, and graph-based learning, with implications for climate modeling and dynamical systems. Despite extensive contributions, the scraped data does not specify students, awards, or contact details.

Scott Sissonمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Statistics
• Big Data
• +۴ 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.

Anna Gottardمشاهده پروفایل

دانشیار

• Multivariate Statistical Models
• Graphical Models
• Statistical Machine Learning
• +۵ مورد دیگر

Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.

Peng Zhaoمشاهده پروفایل

استادیار

• High-dimensional statistics
• Statistical network models
• Multivariate statistics
• +۷ 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.

Heather Batteyمشاهده پروفایل

استاد

• Statistical Theory
• High-Dimensional Inference
• Sparsity in Statistics
• +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.

Daniel M. Kaneمشاهده پروفایل

استاد

• Number Theory
• Combinatorics
• Complexity Theory
• +۳ مورد دیگر

Daniel M. Kane is a Professor at the University of California, San Diego (UCSD), holding a joint appointment in the Department of Mathematics and the Department of Computer Science and Engineering (CSE). His research spans mathematics and theoretical computer science, with a focus on number theory, combinatorics, complexity theory, and computational statistics. He earned a Ph.D. in Mathematics from Harvard University (2011) and dual BS degrees in Mathematics with Computer Science and Physics from MIT (2007). Prior to UCSD, he was a postdoctoral researcher at Stanford University (2011–2014) on an NSF fellowship. His research interests include robust statistics, machine learning, polynomial threshold functions, and algorithmic methods for high-dimensional data. Notable achievements include co-authoring the book Algorithmic High-Dimensional Robust Statistics (Cambridge University Press, 2023) and receiving the Best Paper Award at the Conference on Computational Complexity (2013), as well as gold medals at the International Mathematical Olympiad (2002 and 2003). Current teaching includes courses such as Math 96 (Putnam Seminar), Math 154 (Graph Theory), CSE 101 (Algorithms), and CSE 203A (Randomized Algorithms). He has consulted for companies like CASPER Labs and AIble, and his work extends to cryptographic protocols, including quantum money schemes based on quaternion algebras. Key contributions include breakthroughs in robust mean estimation, list-decodable learning, and the development of efficient algorithms for statistical problems. His research often bridges foundational theory with practical applications in machine learning and data analysis.