جست‌وجوی استاد و پژوهشگر

Azeem Shaikhمشاهده پروفایل

استاد

• Econometric theory
• Partial identification
• Multiple testing
• +۶ مورد دیگر

Azeem M. Shaikh is the Ralph and Mary Otis Isham Professor of Economics at the University of Chicago's Kenneth C. Griffin Department of Economics and serves as Department Chair. He also holds the Thornber Research Fellowship and co-directs the Becker Friedman Institute’s Big Data Initiative. His research focuses on econometric theory, including multiple testing, resampling methods (e.g., bootstrap), and partially identified models. He has contributed to applications in policy evaluation and causal inference, with notable work on randomized experiments and imperfect compliance. Educated at Duke University (B.S. in Mathematics) and Stanford University (Ph.D. in Economics), Shaikh has been affiliated with the University of Chicago since 2007. His research has received NSF funding and prestigious awards such as the Dennis J. Aigner Award for Applied Econometrics and Alfred P. Sloan Fellowship. He is an elected fellow of the Econometric Society and International Association for Applied Econometrics, and serves on editorial boards of the Econometrics Journal and Journal of Econometrics . Shaikh’s work bridges theoretical econometrics with practical policy challenges, emphasizing methodological rigor in addressing real-world problems like education program evaluation and health survey biases. His recent studies explore nonrepresentative sampling in population health research and novel variance estimators for stratified experiments. Awards: Hoover Fellowship, Sloan Fellowship, Econometric Society Fellowship Grants: NSF support for econometric methods in clustered data and covariate-adaptive randomization Key Contributions: Randomization inference frameworks, multiple testing adjustments, and causal effect identification under monotonicity

Fang Hanمشاهده پروفایل

دانشیار مدعو

• Econometrics
• Statistical Inference
• Nonparametric Statistics
• +۲ مورد دیگر

Fang Han is Adjunct Associate Professor of Economics at the University of Washington, with joint affiliation in Statistics. He holds a PhD in Biostatistics from Johns Hopkins University and served as Google PhD Fellow during his doctoral studies. His methodological research develops novel statistical tools for dependence measurement, high-dimensional inference, and time series analysis. Research contributions include: Rank-based correlation measures for complex data structures Bootstrap methods for dependence testing Manifold-adaptive statistical techniques Semiparametric regression methods Han serves as Associate Editor for Bernoulli and editorial board member for Dependence Modeling. His NSF-funded work advances statistical learning theory with applications in econometrics and biostatistics.

Suojin Wangمشاهده پروفایل

استاد

• Biostatistical Inferences
• Missing Data Modeling
• Nonparametric Methodology
• +۳ مورد دیگر

Suojin Wang is a Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. His research focuses on biostatistical methodologies, including missing data modeling, nonparametric and semi-parametric techniques, and survey sampling. His work addresses challenges in variance analysis, resampling methods, and small sample asymptotics. Key research areas include developing robust statistical frameworks for handling non-ignorable missing responses, creating efficient estimation methods for functional data, and applying machine learning to healthcare and environmental problems. Recent studies examine opioid treatment program effectiveness, public transit impacts on aging populations, and shale gas reservoir predictions using clustering algorithms. Wang has contributed to over 80 peer-reviewed articles since 2010, focusing on methodological advancements in statistics with applications in health sciences, environmental studies, and energy research. He holds a leadership role in the Texas A&M Department of Statistics, guiding academic and research initiatives.

Charisios Grivasمشاهده پروفایل

استادیار

• Econometrics
• Statistical Methods
• Non-parametric Econometrics
• +۷ مورد دیگر

Charisios Grivas is an Assistant Professor in the Department of Mathematical Sciences at the Faculty of Engineering and Science, Aalborg University, Denmark. His research lies at the intersection of econometrics, statistics, and environmental modeling, with a strong emphasis on methodological rigor and robust inference. His primary research interests include Econometrics , Statistical Methods , Non-parametric Econometrics , Resampling , Robust Estimation , and Measurement Error Models . He develops and applies advanced statistical techniques to address challenges in linear and time-varying models, particularly in the presence of data imperfections such as measurement errors. The recent articles demonstrate a consistent focus on improving inference in econometric models—especially through automated bandwidth selection, testing for nonlinear dependence, and robust estimation under measurement error. These works span applications in both theoretical econometrics and environmental research, notably in estimating the carbon dioxide airborne fraction. Scientific Awards: No scientific awards mentioned in the provided text. Charisios Grivas actively collaborates with researchers such as Z. Psaradakis and J.E. Vera-Valdés. While no formal advisees are listed, his publications and ongoing research output suggest active supervision and mentorship. There is no mention of specific grants, but his work on measurement error and environmental statistics may be supported by external funding. His research contributes to methodological advancements with practical implications in climate science and economics.

Omer Bobrowskiمشاهده پروفایل

استاد

• Topological Data Analysis
• Stochastic Topology
• Applied Topology
• +۴ مورد دیگر

Omer Bobrowski is a Professor in Mathematical Data Science at Queen Mary University of London, affiliated with the School of Mathematical Sciences. His research focuses on stochastic topology, topological data analysis (TDA), and their applications in signal processing and natural language processing. He explores theoretical aspects like phase transitions in stochastic topology and noise distribution in TDA tools, while developing statistical methods for practical applications. His work has been supported by grants from the EPSRC (£396,018, 2024–2027) and the Leverhulme Trust (£330,778, 2024–2027). Collaborators include Primoz Skraba and others in the Centre for Probability, Statistics, and Data Science. Research Interests include: Random Topology, Applied Topology, Stochastic Geometry, and Probability Theory. His lab includes Research Staff Dr. Shu Kanazawa, Dr. Uzu Lim, and Dr. Duncan Parker. Bobrowski’s publications span foundational TDA theory and applied methodologies across diverse datasets.

Ciprian Crainiceanuمشاهده پروفایل

استاد

• Biostatistics
• Functional Data Analysis
• Wearable and Implantable Technology
• +۱۵ مورد دیگر

Ciprian Crainiceanu is a Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. His research spans biostatistical methodology and applications in public health, with a focus on high-dimensional data from wearable devices and medical imaging. Education: PhD, Cornell University, 2003 MS, University of Bucharest, 1998 His research interests include functional data analysis, measurement error, longitudinal modeling, Bayesian inference, and nonparametric statistics, with applications in sleep, aging, multiple sclerosis, Alzheimer’s disease, and cancer. He develops statistical tools tailored to complex data from accelerometers, neuroimaging (MRI, CT, SPECT), and surgical monitoring. His recent work involves dynamic prediction models, step-counting algorithms for NHANES and ARIC data, and methods for high-dimensional functional and imaging data. The most recent publications highlight advancements in wearable data analysis, medical imaging platforms like Neuroconductor, and novel resampling methods such as the upstrap. His work integrates statistical theory, software development, and interdisciplinary collaboration. Scientific Awards: Fellow of the American Statistical Association (ASA) Chair, Statistics in Imaging Section of ASA (two terms) Chair, Biostatistics Methods and Research Design (BMRD) NIH review section Crainiceanu is actively involved in mentoring, teaching, and collaborative research. He co-founded the SMART (Statistical Methods and Applications for Research in Technology) research group and Neuroconductor, fostering interdisciplinary innovation. His work emphasizes scalable, software-backed methods and close collaboration with domain scientists. He has led methodological developments in variance components testing, functional regression, population value decomposition, and dynamic prediction, applied to real-world health challenges. Labs and Research Groups: Co-founder, SMART (Statistical Methods and Applications for Research in Technology) Co-founder, Neuroconductor (open-source platform for medical imaging in R) Wearable and Implantable Technology (WIT) group MAGIC (Methods and Applications Group for Imaging in the Clinic)

David Ruppertمشاهده پروفایل

استاد

• Functional data analysis
• Nonparametric regression
• Statistical machine learning
• +۷ مورد دیگر

David Ruppert is the Andrew Schultz Jr. Professor of Engineering at Cornell University's School of Operations Research and Information Engineering, and Professor of Statistics and Data Science. He holds dual appointments and has been a faculty member since 1987. His education includes a B.A. in Mathematics from Cornell University (1970), M.A. in Mathematics from the University of Vermont (1973), and Ph.D. in Statistics and Probability from Michigan State University (1977). Research Interests: His work spans functional data analysis, astrostatistics, neuroimaging (fMRI/ICA), environmental statistics, and semiparametric regression. He has pioneered methods in measurement error models, splines, and Bayesian statistics. His research has been continuously funded by NSF, NIH, and EPA since 1978. Publications: Over 130 refereed articles and 5 books, including foundational texts like Measurement Error in Nonlinear Models and Statistics and Data Analysis for Financial Engineering . Recent work includes astrostatistical modeling of galaxy spectral energy distributions and neuroimaging analysis. Awards/Honors: Wilcoxon Prize (1986), ASA/IMS Fellowships, Highly Cited Researcher (ISI), and Distinguished Alumni Award (2014). Teaching: Courses include Financial Engineering, Bayesian Statistics, and Functional Data Analysis. He co-developed four graduate/undergraduate courses at Cornell. Service: Editor of Journal of the American Statistical Association , Director of the MPS Program in Data Science and Statistics (DSS). Impact: 29 PhD students trained, many now leading researchers in academia and industry.

Cosma Shaliziمشاهده پروفایل

دانشیار

• Nonparametric prediction of time series
• Learning theory
• Nonlinear dynamics
• +۱۵ مورد دیگر

Cosma Shalizi is an Associate Professor in the Statistics Department and Machine Learning Department at Carnegie Mellon University, and an External Professor at the Santa Fe Institute. His work bridges statistics, machine learning, and complex systems theory, with applications spanning neuroscience, statistical mechanics, and social networks. Shalizi's research focuses on nonparametric prediction of time series, learning theory, information theory, and causal inference. He has made significant contributions to computational mechanics, developing algorithms like CSSR (Causal State Splitting Reconstruction) for identifying optimal predictive states in complex systems. His work extends to heavy-tailed distributions, network analysis, and the statistical foundations of complex systems. He has pioneered methods for quantifying self-organization and developing nonparametric approaches to spatio-temporal prediction. His recent publications reveal a trend toward increasingly interdisciplinary work, connecting network science with causal inference, statistical learning theory with macroeconomic forecasting, and information theory with biomedical applications. His work consistently emphasizes rigorous statistical methodology applied to complex, dependent data structures across diverse scientific domains. Winner of the Best Student Paper and Best Poster awards Shalizi has advised students including Georg Goerg, who extended spatio-temporal prediction techniques to continuous-valued fields, and George Montañez, who developed fast approximate algorithms for prediction and explored information-theoretic explanations for machine learning. His collaborative network spans statistics, physics, neuroscience, and social sciences, reflecting his interdisciplinary approach to complex systems. His work with collaborators has led to significant contributions in network analysis, causal inference in social networks, and the development of nonparametric methods for complex data structures. He maintains active research programs in statistical network modeling, time series analysis, and the application of information-theoretic approaches to diverse scientific problems.

Konrad Menzelمشاهده پروفایل

استاد

• Econometrics
• Labor Economics
• Causal Inference
• +۳ مورد دیگر

Konrad Menzel is a Professor of Economics at New York University's College of Arts & Science. He holds a Ph.D. in Economics from the Massachusetts Institute of Technology (2009) and a Diplom in Economics from the University of Mannheim (2004). His research focuses on econometrics, causal inference, and network formation, with a particular emphasis on large-population asymptotics and strategic interactions. Education : Ph.D., Economics, Massachusetts Institute of Technology (2009) Diplom, Economics, University of Mannheim (2004) Research Interests : Dr. Menzel's work bridges econometrics, labor economics, and game theory. His recent publications address causal inference in equilibrium models, cluster-robust bootstrap methods, and strategic network formation. He develops novel statistical approaches for high-dimensional and heterogeneous data structures. Scientific Contributions : His research includes methodological advancements in causal bootstrap techniques, inference for large games, and structural models for matching markets. He has authored influential papers in top journals like Econometrica, Annals of Statistics, and Review of Economic Studies. External Affiliation : Member, Econometric Society Contact : konrad.menzel@nyu.edu

• Multiple Testing
• False Discovery Rate
• Asymptotic Statistics
• +۵ مورد دیگر

Thorsten-Ingo Dickhaus is a Professor of Mathematical Statistics at the Institute for Statistics , Faculty of Mathematics, University of Bremen. He holds a PhD in Natural Sciences (Mathematics) from Heinrich-Heine-University Düsseldorf (2008) and has held academic positions at Humboldt-University Berlin, Technische Universität Berlin, and the Weierstrass Institute for Applied Analysis and Stochastics. Education: Diploma in Technomathematics (FH Aachen, 2003), Master of Science (Heinrich-Heine-University Düsseldorf, 2005), Dr. rer. nat. (Düsseldorf, 2008) His research focuses on multiple testing methodologies, particularly false discovery rate control and resampling techniques, with applications in life sciences. He also investigates asymptotic statistics , nonparametric test theory , and computational statistics . Key application areas include statistical genetics, neuroeconomics, and biomedical data analysis. He has been awarded multiple research grants from the German Research Foundation (DFG) for projects on multi-stage studies, behavioral genetics, and dependency structures in multiple hypothesis testing. His editorial roles include Executive Editor for the Journal of Statistical Planning and Inference and Associate Editor for the Biometrical Journal and Annals of the Institute of Statistical Mathematics . Thorsten Dickhaus has organized conferences like the 12th International Conference on Multiple Comparison Procedures (MCP 2022 Bremen) and the Spring School 2014 of Research Unit FOR 1735 . He is involved in professional societies including the Bernoulli Society and the Institute of Mathematical Statistics.

Andrew Woodمشاهده پروفایل

استاد

• Non-Euclidean Statistics
• Directional Statistics
• Statistical Shape Analysis
• +۵ مورد دیگر

Professor Andrew Wood is a faculty member at the Research School of Finance, Actuarial Studies and Statistics, Australian National University. His research spans non-Euclidean statistics, theoretical statistics, computational methods, and applied statistics in sciences and medicine. Research Interests : Non-Euclidean statistics, directional statistics, statistical shape analysis, asymptotic theory, computational statistics, stochastic differential equations, and applications in science/medicine. Grants : Funded by Engineering and Physical Sciences Research Council (UK), Biotechnology and Biological Sciences Research Council (UK), and Australian Research Council Discovery Projects. Editorial Roles : Former Joint Editor of Journal of the Royal Statistical Society, Series B and current Associate Editor of Biometrika . Research Trends : Recent publications focus on robust statistical methods for non-Euclidean data, computational approaches for complex distributions, principal component analysis for high-dimensional datasets, and geometric inference on manifolds. Applications include microbiome analysis, surface fractal dimension estimation, and spherical regression models. Supervision & Collaboration : Registered as a supervisor at ANU, with collaborations across disciplines including molecular biology, environmental science, and computational mathematics.

Matúš Maciakمشاهده پروفایل

دانشیار

• nonparametric regression
• change-points
• structural breaks
• +۵ مورد دیگر

Matúš Maciak is an Associate Professor in the Department of Probability and Mathematical Statistics at the Faculty of Mathematics and Physics, Charles University . His research spans nonparametric regression , change-point detection , and statistical modeling in financial and biological contexts . Key research areas: Nonparametric smoothing and robustness Structural break analysis Shape-constrained inference Sparsity and LASSO-based methods Bootstrapping dependent data Recent publications focus on exogenous market changes , longitudinal data modeling , and biological applications like diel movement patterns in fish. Technical reports include contributions to ecological assessment methods and intercalibration processes for water quality. Contact: Email: Matus.Maciak@mff.cuni.cz , maciak@karlin.mff.cuni.cz Office: K151, Sokolovská 83, Prague 8

Caroline Fossatiمشاهده پروفایل

استاد

• Signal Processing
• Hyperspectral Imaging
• Medical Imaging
• +۲ مورد دیگر

Caroline Fossati is a Full Professor at Ecole Centrale de Marseille and a member of the Institut Fresnel - GSM Signal Processing lab. Her work bridges signal processing, hyperspectral imaging, and medical diagnostics, with a focus on Alzheimer's disease detection using PET imaging and tensor-based noise reduction techniques. Current Affiliations: Ecole Centrale de Marseille, Institut Fresnel Past Affiliations: Université Paul Cézanne (1996-1997) Her research spans: Medical Imaging: Developing advanced algorithms for Alzheimer's diagnosis through 18F-FDG PET scans and multi-level feature extraction. Hyperspectral Processing: Innovating tensor-based methods for noise reduction, dimensionality reduction, and target detection in complex imaging environments. Optical Engineering: Investigating CMOS sensor design, photolithography simulations, and defect characterization in optical materials. Publications demonstrate expertise in multidimensional signal processing, array modeling, and biomedical applications. While no specific awards are documented in the provided text, her extensive co-authorship network and technical contributions across disciplines underscore her academic impact.

• Actuarial Science
• Stochastic Processes
• Mathematical Finance
• +۵ مورد دیگر

Xiaoming Liu is an Associate Professor in the Department of Statistical and Actuarial Sciences at Western University's Faculty of Social Science. His research focuses on actuarial science, stochastic processes, and mathematical finance, particularly in mortality modeling and interest rate risk analysis. Ph.D., University of Toronto (2008) Research interests include stochastic mortality modeling, regime-switching dynamics, and guaranteed annuity option pricing. His work integrates comonotonicity theory, Markov processes, and probability measure transformations to address correlated financial and demographic risks. Notable awards include the Associateship of the Society of Actuaries (ASA, 2016). He has supervised graduate students in actuarial research and published extensively in top-tier journals like Insurance: Mathematics and Economics and Stochastics.

Vasilis Syrgkanisمشاهده پروفایل

استادیار

• Machine Learning
• Causal Inference
• Econometrics
• +۵ مورد دیگر

Vasilis Syrgkanis is an Assistant Professor at the Department of Management Science and Engineering , Stanford University, with courtesy appointments in Computer Science and Electrical Engineering. He leads the Stanford Causal AI Lab and is an Associated Director of the Stanford Causal Science Center . His research spans machine learning, causal inference, econometrics, online learning, reinforcement learning, game theory/mechanism design, and algorithm design . Education PhD in Computer Science, Cornell University (advised by Eva Tardos) Diploma in EECS, National Technical University of Athens Research Focus Develops methods for causal machine learning , including courses on Applied Causal Inference and Foundations of Causal ML. Works on treatment effect estimation , instrumental variable regression , and robust policy learning under unobserved heterogeneity. Explores intersections of game theory and machine learning , particularly in auction design and strategic exploration. Scientific Contributions Proposes neural causal partial identification and adaptive instrument design frameworks. Develops doubly robust learning and minimax IV regression algorithms with theoretical guarantees. Introduces incentive-aware synthetic control and structure-agnostic causal effect estimation methods. Awards & Recognition 2023 Bodossaki Distinguished Young Scientist Award in Applied Sciences 2022 Amazon Research Award in Machine Learning Algorithms and Theory Best Paper Awards at COLT 2019, EC 2015, NeurIPS 2015, and others PhD Advisees Ravi Sojitra (MS&E, co-advised with Guido Imbens) Hui Lan (ICME) Jikai Jin (ICME) Jiyuan Tan (MS&E, co-advised with Jose Blanchet) Keertana Veeramony Chidambaram (MS&E) Contact Email: vsyrgk@stanford.edu Office: Huang Engineering Center, Room 252, Stanford, CA 94305