جست‌وجوی استاد و پژوهشگر

Masoud Asgharianمشاهده پروفایل

استاد

• Survival Analysis
• Changepoint Problem
• Nonparametric Bayesian Analysis
• +۵ مورد دیگر

Masoud Asgharian is a Professor in the Department of Mathematics and Statistics at McGill University. His research focuses on survival analysis, changepoint problems, nonparametric Bayesian methods, and data envelopment analysis. He has contributed to influential studies on dementia survival rates, censored data methodologies, and statistical efficiency measures. His work bridges biostatistics and operations research, with applications in public health and medical sciences. Key contributions include methodologies for prevalent cohort survival analysis, input relaxation efficiency measures in stochastic DEA, and causal inference techniques. Asgharian has collaborated extensively with researchers in epidemiology and biomedical engineering, as evidenced by his co-authored publications on topics ranging from tooth enamel properties to low-precision neural network quantization. His research has been published in high-impact journals such as New England Journal of Medicine , Journal of the American Statistical Association , and Biometrics . Current affiliations include leadership roles in statistical research at McGill, with ongoing projects in computational statistics and healthcare analytics.

• Machine Learning
• Probabilistic Inference
• Decision Making
• +۱ مورد دیگر

Professor Carl Edward Rasmussen is affiliated with the University of Cambridge , where he focuses on Machine Learning , Probabilistic Inference , Decision Making , and Reasoning Under Uncertainty . His work bridges theoretical advancements with practical applications in robotics, control systems, and computational biology. Academic Affiliation: University of Cambridge Academic Role: Professor His research emphasizes scalable Gaussian process methods, Bayesian system identification, and reinforcement learning. Recent projects include transfer learning for antibacterial discovery , graph neural processes for molecular functions , and efficient variational inference techniques . Key themes in his publications highlight uncertainty quantification , model generalization , and nonparametric approaches . Notable Scientific Contributions Advancements in sparse Gaussian process hyperparameter estimation Framework for Bayesian system identification in dynamic systems Hybrid models combining transformers and Gaussian processes

Xiaofeng Shaoمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Change-Point Analysis
• +۳ مورد دیگر

Xiaofeng Shao is a Professor of Statistics & Data Science at Washington University in St. Louis, with a joint appointment in the Department of Economics. He holds a PhD from the University of Chicago and previously served at the University of Illinois at Urbana-Champaign for 18 years. He is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. His research focuses on econometrics, time series analysis, change-point detection, high-dimensional statistics, nonparametric methods, and functional data analysis. Recent work emphasizes object-valued time series modeling and machine learning applications in high-dimensional and imaging data. Notable contributions include the dependent wild bootstrap method and self-normalization techniques for time series inference. Key awards include Fellowships from leading statistical societies. His publications span over 20 years, addressing topics like change-point detection in climate projections, statistical methods for COVID-19 infection trends, and high-dimensional dependence testing.

Tengyao Wangمشاهده پروفایل

استاد

• High-dimensional Statistics
• Change-point Analysis
• Sparse Signal Detection
• +۷ مورد دیگر

Tengyao Wang is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE), serving as the MSc Statistics (Financial Statistics) Programme Director. Prior to LSE, he held positions as a Lecturer at University College London and a Research Fellow at the Cantab Capital Institute for the Mathematics of Information, University of Cambridge. His research focuses on high-dimensional statistics, computational efficiency, and statistical limitations imposed by computational constraints. Education: PhD in Statistics under Prof Richard Samworth at the University of Cambridge, with earlier studies including a Part III Essay in Empirical Process Theory. Research interests include sparse signal detection, change-point analysis, dimension reduction, robust statistics, and applications in medical statistics, financial data analysis, and material discovery. Key contributions include methodologies for handling missing data, high-dimensional change-point detection algorithms, and statistical learning techniques. Publications span theoretical advancements and applied innovations, with recent work emphasizing deep learning with missing data, residual permutation tests, and semi-supervised learning via random projections. His work has been recognized with awards such as the Royal Statistical Society Research Prize (2019) and the Guy Medal in Bronze (2023). He is an Associate Editor of the Journal of the Royal Statistical Society, Series B (JRSS B), and actively contributes to open-source tools like the 'ocd' and 'MissInspect' R packages for changepoint detection and missing data analysis.

George H. Chenمشاهده پروفایل

دانشیار

• Trustworthy Machine Learning
• Time-to-Event Prediction
• Survival Analysis
• +۳ مورد دیگر

George H. Chen is an Associate Professor at Carnegie Mellon University , with dual affiliations in the Heinz College of Information Systems and Public Policy and the Machine Learning Department . His research focuses on trustworthy machine learning methods for temporal reasoning , particularly in health applications such as time-to-event prediction (survival analysis) and electronic health records analysis . He has extensive experience in nonparametric methods requiring minimal data assumptions. Educational Background PhD in Electrical Engineering and Computer Science, MIT (2015) SM in Electrical Engineering and Computer Science, MIT (2012) BS in Electrical Engineering and Computer Sciences & Engineering Mathematics and Statistics, UC Berkeley (2010) His work spans survival analysis , deep learning , and time series modeling , with applications in neurological prognostication , medical adherence , and health equity . He has developed self-contained educational resources including a 2024 monograph on deep survival analysis and tutorials at CHIL and SIGMETRICS. His 2025 course 95-865: Unstructured Data Analytics focuses on practical unstructured data analysis techniques. Notable projects include advising the AgriTech startup CoolCrop , which provides cold storage and market forecasts for Indian farmers serving 9,000+ farmers across 7 states. His Google Scholar publications reveal a strong focus on temporal modeling in healthcare, with recent advancements in neural survival analysis and fairness-aware temporal prediction.

Sonia Petroneمشاهده پروفایل

استاد

• Bayesian Statistics
• Predictive Modeling
• Bayesian Nonparametrics
• +۱ مورد دیگر

Dr. Sonia Petrone is a Full Professor of Statistics at Bocconi University's Department of Decision Sciences. She earned her PhD in Statistics from Bocconi University and has held academic positions at the University of Pavia and University of Insubria before joining Bocconi. Her extensive international experience includes research visits across North America, Latin America, Europe, India, and Russia. Her research specializes in Bayesian statistics, with contributions to foundational theory, predictive modeling, Bayesian nonparametrics, and stochastic processes. She currently directs the Bocconi Summer School in Advanced Statistics and Probability and previously led the PhD program in Statistics (2011-2018). Her research portfolio demonstrates consistent focus on Bayesian nonparametric methods, predictive modeling, and applications to complex data structures. Recent work explores urn processes, time series analysis, and network modeling using innovative Bayesian approaches. Awards & Honors: IMS Medallion Lecture Award (2018) ISBA Foundational Lecture Award (2016) Fellow of International Society for Bayesian Analysis Fellow of Institute of Mathematical Statistics Fellow of European Laboratory for Intelligent Systems Fellow of Bocconi Institute of Data Science She has held editorial leadership positions as Editor of Statistical Science (2020-2022) and Bayesian Analysis (2010-2014), and served as President of the International Society for Bayesian Analysis (2014).

Rina Foygel Barberمشاهده پروفایل

استاد

• Statistics
• Statistical Inference
• Distribution-Free Inference
• +۷ مورد دیگر

Rina Foygel Barber is the Louis Block Professor in the Department of Statistics at the University of Chicago, where she also serves as Co-chair of the Committee on Community, Diversity, and Inclusion (CCDI) and is a member of the Committee on Computational and Applied Mathematics (CCAM). Her educational background includes: PhD in Statistics, University of Chicago (2012), advised by Mathias Drton and Nati Srebro MS in Mathematics, University of Chicago (2009) ScB in Mathematics, Brown University (2005) NSF postdoctoral fellow, Stanford University Department of Statistics (2012-13), supervised by Emmanuel Candès Professor Barber's research focuses on the theoretical foundations of statistical problems in estimation, prediction, and inference, particularly in high-dimensional settings where classical methods may not be reliable. She specializes in distribution-free inference methods such as conformal prediction, multiple testing methods, algorithmic stability, and shape-constrained inference. Her work also extends to modeling and optimization problems in medical imaging reconstruction. Her recent publications demonstrate a strong focus on distribution-free inference, with particular emphasis on conformal prediction, false discovery rate control, and algorithmic stability. Her work bridges theoretical statistics with practical applications, especially in the medical imaging domain. Professor Barber has received numerous prestigious awards: Elected to National Academy of Sciences (2025) MacArthur Fellowship (2023) IMS Fellow (2023) COPSS Presidents' Award (2020) Peter Gavin Hall Early Career Prize (2020) She actively mentors students and collaborators, with many co-authored publications across statistics, machine learning, and medical imaging. Her research has been supported by significant grants that enable her work on theoretical foundations of statistical inference and practical applications in medical imaging. Professor Barber also co-organizes the International Seminar on Selective Inference. Her research group focuses on developing and analyzing estimation, inference, and optimization tools for structured high-dimensional data problems. They work on false discovery rate control, distribution-free inference, and applications in medical imaging reconstruction.

Debdeep Patiمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Statistics
• High Dimensional Data Analysis
• +۳ مورد دیگر

Debdeep Pati is a Professor in the Department of Statistics at the University of Wisconsin-Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on Bayesian methods, high-dimensional data analysis, machine learning, and computational statistics, with applications in health data and network analysis. He has contributed to approximate Bayesian computation, graphical models, and fair algorithms. Key research interests include Bayes theory in high dimensions, hierarchical modeling, efficient Bayesian computation, and real-time tracking algorithms. His work bridges theoretical advancements with practical applications in areas like electronic health records and nuclear physics constraints. Recent work emphasizes Wasserstein-guided nonparametric Bayes, fair clustering algorithms, and variational inference in singular models. He has developed software for covariate-dependent Gaussian graphical modeling, published in ACM Transactions on Mathematical Software . Grants: NSF proposal on Wasserstein-guided nonparametric Bayes, NIH R01/R21 grants on periodontal disease and diabetes comorbidity. Advising: No named advisees listed but actively supervising research in Bayesian computation and high-dimensional statistics. Awards: 2024 JASA reproducibility award for 'Covariate-Assisted Bayesian Graph Learning.' He is an Associate Editor for Journal of Computational and Graphical Statistics and has organized workshops at Banff International Research Station (BIRS) and the Institute for Mathematics and its Applications (IMSI).

Yuguo Chenمشاهده پروفایل

استاد

• Network Analysis
• Monte Carlo Methods
• Statistical Inference
• +۷ مورد دیگر

Yuguo Chen is a Professor in the Department of Statistics at the University of Illinois at Urbana-Champaign (UIUC), serving as Interim Department Chair and Director of the Illinois Statistics Office. He holds affiliations with the Department of Computer Science, Information Trust Institute, Coordinated Science Lab, and Illinois Informatics Institute. Chen earned his PhD in Statistics from Stanford University (2001) and a B.S. in Mathematics from the University of Science and Technology of China (1997). His research focuses on Monte Carlo methods, network data analysis, state space models, bioinformatics, and Bayesian inference. Key interests include scalable network estimation, community detection, and applications in public health, education, and computational biology. Recent work highlights include advancements in dynamic network modeling, Bayesian latent class models for cognitive diagnosis, and statistical methods for analyzing multi-layer networks. His contributions have been recognized through awards such as the American Statistical Association Fellowship (2018) and the Charles Edison Lectureship (2018). Editorial Roles: Associate Editor of Journal of the American Statistical Association , Journal of Computational and Graphical Statistics , and Journal of Algebraic Statistics . Grants & Consulting: Directs the Illinois Statistics Office, providing interdisciplinary research support. Active in collaborative projects involving healthcare, education, and computational infrastructure. Labs & Teams: Leads initiatives at the Coordinated Science Lab and Information Trust Institute, integrating statistical methods with cybersecurity and data-driven decision-making.

Hau-Tieng Wuمشاهده پروفایل

استاد

• Biomedical Signal Processing
• Applied Mathematics
• Machine Learning
• +۵ مورد دیگر

Hau-Tieng Wu is a Professor in the Department of Mathematics at the Courant Institute of Mathematical Sciences, New York University. Originally from Kaohsiung, Taiwan, he holds an MD from National Yang-Ming University (2003) and a PhD in Mathematics from Princeton University (2011). His research focuses on developing mathematical foundations for biomedical signal analysis, particularly in high-frequency and heterogeneous physiological signals such as ECG, EEG, and PPG. He leads the MISTA Lab, which bridges theoretical advancements with clinical applications in areas like sleep dynamics, surgical monitoring, and wearable device data analysis. Key academic roles include tenured positions at Duke University (2017–2023) and the University of Toronto (2014–2017). Notable awards include the Sloan Research Fellowship (2015) and PIMS Early Career Award (2017). His lab actively collaborates with physicians and engineers to advance interpretable medical AI systems. Research interests span nonlinear time-frequency analysis, manifold learning, and spatiotemporal data processing. Over 100+ journal publications and 10 conference proceedings highlight contributions to signal processing theory and clinical applications. The lab is recruiting PhD students/postdocs with backgrounds in applied math, statistics, or biomedical engineering.

Ismael Castilloمشاهده پروفایل

استاد

• Mathematical Statistics
• Bayesian Nonparametrics
• High-Dimensional Inference
• +۴ مورد دیگر

Ismael Castillo is a Professor of Statistics at Sorbonne Université , affiliated with the Laboratoire de Probabilités, Statistique et Modélisation (LPSM) and its Statistics, Data, Algorithms team. He serves as Associate Editor for Annals of Statistics , Bernoulli , and co-Editor for Bayesian Analysis . Research Interests : Mathematical statistics with emphasis on Bayesian nonparametrics , inference in high-dimensional structures , uncertainty quantification , and applications in signal processing and life sciences . Recent Work spans deep neural networks with heavy-tailed weights , posterior and variational inference , fractional posteriors in semiparametric models , and deep Gaussian processes . His publications demonstrate expertise in multiple testing procedures , Spike and Slab priors , and nonparametric Bayesian methods . Awards : IMS Fellow , Honorary Fellow of Institut Universitaire de France , and Best Paper Prize (2021) for research on Pólya tree posterior distributions. Students : Supervised PhD candidates Paul Egels , Thibault Randrianarisoa , and co-supervised Bo Ning (FSMP postdoc) and Kweku Abraham (Hadamard postdoc). Grants : ANR BACKUP (2023-2027, coordinator) and ANR GAP (2021-2025, member).

• Econometric Theory
• Machine Learning/Nonparametrics
• Robust Inference for Clustered & Network Data
• +۶ مورد دیگر

Harold D. Chiang is an Assistant Professor in the Department of Economics at the University of Wisconsin-Madison. His research focuses on econometric theory and methods, particularly robust inference techniques for clustered and network data, machine learning applications, and causal inference frameworks like regression discontinuity/kink designs. He employs computational statistics and asymptotic theory to address methodological challenges in high-dimensional and complex datasets.

Don Towsleyمشاهده پروفایل

استاد

• Network Science
• Network Measurement
• Network Modeling
• +۴ مورد دیگر

Don Towsley is a Distinguished University Professor in the Department of Computer Science at the University of Massachusetts Amherst, within the College of Information and Computer Sciences. He has held visiting positions at AT&T Labs, IBM Research, INRIA, Microsoft Research Cambridge, and the University of Paris 6. He earned a B.A. in Physics and a Ph.D. in Computer Science from the University of Texas. Prof. Towsley's research spans network science, measurement, modeling, and analysis, with recent emphasis on quantum networking and wireless security. His work addresses foundational challenges in network tomography, entanglement distribution, and quantum communication protocols, contributing to efficient and secure next-generation networks. Analysis of his 2022-2025 publications reveals a dominant focus on quantum networking—including quantum internet architecture, entanglement distribution, and tomography—alongside continued contributions in classical networking areas such as DDoS detection and edge computing. His exceptional contributions have been recognized with numerous prestigious awards: 2007 IEEE Koji Kobayashi Computer and Communications Award 2007 ACM SIGMETRICS Achievement Award 2008 ACM SIGCOMM Award 2011 INFOCOM Achievement Award 1999 IEEE Communications Society William Bennett Award 2008 ACM SIGCOMM Test of Time Paper Award 2012 ACM SIGMETRICS Test of Time Award 2018 ACM MOBICOM Test of Time Award UMass Award for Outstanding Accomplishments in Research and Creative Activity University of Massachusetts Chancellor's Medal UMass Amherst Distinguished Graduate Mentor Award Outstanding Research Award from the College of Natural Science and Mathematics IBM Faculty Fellowship Award (twice) Fellow of the IEEE Fellow of the ACM Corresponding member of the Brazilian Academy of Sciences Prof. Towsley has mentored numerous graduate students, as evidenced by his Distinguished Graduate Mentor Award, and his research has been funded by significant grants including an NSF NeTS grant for quantum network design. He leads the Gaia research group at UMass Amherst, which has evolved from traditional networking research to pioneering quantum networking initiatives.

Axel Gandyمشاهده پروفایل

استاد

• Computational Statistics
• Statistics in Finance
• Survival Analysis
• +۳ مورد دیگر

Axel Gandy is a Professor of Statistics at the Department of Mathematics, Imperial College London. He serves as Director of the EPSRC CDT in Modern Statistics and Statistical Machine Learning , overseeing PhD supervision and advanced statistical training.

Lucas Jansonمشاهده پروفایل

دانشیار

• High-Dimensional Inference
• Statistical Machine Learning
• Reinforcement Learning
• +۴ مورد دیگر

Lucas Janson is an Associate Professor of Statistics and Affiliate in Computer Science at Harvard University. He leads the Harvard Statistical Consulting Service, supervising PhD students advising hundreds of researchers annually. His research focuses on high-dimensional inference, statistical machine learning, and applications in genetics, political science, and climatology. He teaches courses such as Statistical Inference I, Reinforcement Learning, and Statistical Machine Learning. His work bridges theoretical advancements with practical applications, including contributions to robotics motion planning and microbiome data analysis. Key research areas include variable importance inference, safe reinforcement learning, compositional data analysis, and robust paleoclimate reconstructions. His methodologies are implemented in software packages like Floodgate, EigenPrism, and Fast Marching Tree (FMT*). He advises a dynamic group of PhD students and has mentored alumni now in academia and industry roles. Notable contributions include the development of model-X knockoffs for controlled variable selection, conditional randomization tests, and optimization algorithms for adaptive control systems. His work emphasizes statistical rigor while addressing real-world challenges in healthcare, environmental science, and robotics.