جستجوی استاد راهنمای دانشگاهی

Yury Polyanskiyمشاهده پروفایل

استاد

• Information Theory
• Machine Learning
• Statistics
• +۳ مورد دیگر

Yury Polyanskiy is a Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT), affiliated with the Laboratory for Information and Decision Systems (LIDS), the Institute for Data, Systems, and Society (IDSS), and the MIT Statistics and Data Science Center. He holds a Ph.D. from Princeton University (2010) and an M.S. from the Moscow Institute of Physics and Technology (2005). His research focuses on information theory, machine learning, statistical inference, error-correcting codes, and wireless communication. He has contributed to fundamental limits of communication systems, finite-blocklength analysis, and applications of information theory to learning and signal processing. Notable awards include the 2020 IEEE Information Theory Society James Massey Award, the 2013 NSF CAREER Award, and the 2011 IEEE Information Theory Society Paper Award. His work spans theoretical advancements and practical applications, including the development of the SPECTRE toolbox for short-packet communication. He is also co-authoring a textbook on information theory. Recent research highlights include studies on quantization techniques for machine learning (e.g., NestQuant), transformer-based empirical Bayes methods, and novel approaches to massive random access in wireless networks (e.g., unsourced multiple access). His contributions bridge information theory and modern data science, addressing challenges in high-dimensional data representation, neural network dynamics, and efficient communication architectures.

Yuheng Buمشاهده پروفایل

استادیار

• Machine Learning
• Information Theory
• Signal Processing
• +۴ مورد دیگر

Yuheng Bu is an Assistant Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB). Previously, he held positions at the University of Florida and MIT. He earned his Ph.D. from the University of Illinois at Urbana-Champaign (2019) and B.S. from Tsinghua University (2014). His research focuses on foundational machine learning, integrating tools from information theory and signal processing to address challenges in fair learning, uncertainty quantification, and watermarking generative AI. Notable contributions include work on generalization error analysis, adaptive watermarking techniques, and fairness-aware algorithms. Affiliations: UCSB (current), University of Florida (2020-2023), MIT (Postdoc, 2019-2020) Education: Ph.D. Electrical & Computer Engineering, UIUC (2019); B.S. Electronic Engineering, Tsinghua University (2014) Research Interests: Machine Learning Theory, AI Ethics, Information-Theoretic Bounds, Signal Processing, and Applications in Generative AI Security. His work emphasizes rigorous theoretical foundations for trustworthy algorithms with guarantees on generalization, fairness, and robustness. Key Achievements: Developed adaptive watermarking schemes for LLMs, derived exact generalization error characterizations for Gibbs algorithms, and advanced understanding of fairness overfitting. His work has been recognized with awards including the Abraham Wald Prize (2023) and the MLSP Best Student Paper (2022). Grants & Collaborations: Collaborations with institutions like MIT, Turing Institute, and INRIA. Research supported by NSF and industry partnerships. Labs/Teams: Leads the Information-Theoretic Learning Group at UCSB, fostering interdisciplinary research on AI foundations.

Min Xuمشاهده پروفایل

استادیار

• Machine Learning
• High-dimensional Statistics
• Network Data Analysis
• +۲ مورد دیگر

Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2015) B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley Research Interests: Min Xu’s research lies at the intersection of machine learning , high-dimensional statistics , and network science . He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation , network analysis , and large-scale inference . His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences. Grants & Funding: NSF Grant DMS-2113671 NSF Grant DMS-2311299 Research Trends: Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability. Affiliations: Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.

John E. Kolassaمشاهده پروفایل

استاد

• Asymptotics
• Biostatistics
• Nonparametric Statistics
• +۳ مورد دیگر

John E. Kolassa is a Professor of Statistics at Rutgers, the State University of New Jersey. He is affiliated with the Department of Statistics, where he conducts research and teaching in asymptotics and biostatistics. His academic credentials include a Ph.D. from the University of Chicago, and he maintains an active research profile with numerous publications and contributions to statistical methodology. Ph.D., University of Chicago Dr. Kolassa's research is centered on asymptotic theory, nonparametric statistics, and biostatistical methods. His work includes the development and analysis of saddlepoint approximations, Edgeworth expansions, and inference techniques for complex data. He has a strong focus on theoretical statistics, with applications in medical and biological contexts. His expertise spans categorical data analysis, life data analysis, and regression models, as reflected in his teaching of graduate courses such as 960:555 (Nonparametric Statistics) and 960:583 (Methods of Inference). The 15 most recent publications, spanning from 2021 to 2013, demonstrate a consistent focus on statistical theory and methodology. Key themes include the refinement of approximation techniques (e.g., Edgeworth and saddlepoint), inference in complex models (e.g., posterior densities, penalized likelihood), and nonparametric methods. His work often addresses foundational issues in statistical inference, such as the validity of expansions, the reliability of p-values, and the handling of zero-event studies in meta-analysis. The research bridges theoretical development with practical application in biostatistics and health sciences. Fellow of the American Statistical Association (ASA) Fellow of the Institute of Mathematical Statistics (IMS) Elected member of the International Statistical Institute (ISI) Editor, Stat Dr. Kolassa is an active advisor and researcher, contributing to the academic community through his editorial role for the journal Stat . He has received significant recognition through his fellowships in the ASA and IMS, highlighting his impact on the field. His work has been supported through academic appointments and professional activities, though specific grant details are not provided in the source material. He is also involved in the development of statistical software, having created R packages for nonparametric methods and infinite estimates. Dr. Kolassa leads a research team focused on theoretical and applied statistics, with a particular emphasis on developing and validating statistical methodologies. He has mentored students and collaborated on interdisciplinary research, particularly in biostatistics and health outcomes. His laboratory or research group is centered on computational and theoretical statistics, utilizing tools like R for simulation and analysis.

Herbie Leeمشاهده پروفایل

استاد

• Bayesian statistics
• Computer models
• Spatial inverse problems
• +۷ مورد دیگر

Herbie Lee is a Professor in the Department of Statistics at the Jack Baskin School of Engineering, University of California, Santa Cruz. He also serves as the Interim Dean of Social Sciences. His faculty office is located in 537A Baskin Engineering, and he can be reached at 831-459-1655 (though email is preferred). Lee completed his educational journey with a strong foundation in mathematics and statistics: B.S. in Mathematics from Yale University with a specialty in statistics Ph.D. in Statistics from Carnegie Mellon University (1998), advised by Larry Wasserman Post-doctoral work at Duke University's ISDS department Professor Lee's research focuses on Bayesian statistics, particularly in the areas of computer models, spatial inverse problems, simulator emulation, adaptive experimental design, and optimization. His work bridges theoretical statistics with practical applications across diverse domains including cosmology (dark energy modeling), aircraft damage detection, electronic health records analysis, and environmental monitoring. He has made significant contributions to the understanding of neural networks within a statistical framework. An analysis of his recent publications reveals a strong emphasis on Gaussian process modeling, Bayesian optimization, and nonstationary spatial statistics. His work increasingly addresses complex real-world problems requiring sophisticated statistical approaches for mixed data types, constrained optimization, and real-time decision making. The interdisciplinary nature of his research is evident in collaborations with institutions like Lawrence Livermore National Laboratory and the Naval Postgraduate School. Among his notable achievements: Supervised PhD student Bobby Gramacy won the prestigious Savage Award Authored two books on Bayesian optimization and multiscale modeling Published over 50 peer-reviewed articles in top statistics and machine learning journals Professor Lee has supervised numerous graduate students, with his most recent PhD student graduating in 2024. His research has been supported by various grants, though specific funding sources aren't detailed in the provided text. His teaching portfolio includes courses on Bayesian statistics, design and analysis of computer simulation experiments, and statistical methods for biological, environmental, and health sciences. He is actively involved in several professional statistical societies including the International Society for Bayesian Analysis (ISBA), American Statistical Association (ASA), and Institute of Mathematical Statistics (IMS).

Paul Marriottمشاهده پروفایل

استاد

• Geometric Statistics
• Differential Geometry
• Mixture Models
• +۶ مورد دیگر

Paul Marriott is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research focuses on integrating geometric principles, particularly differential and convex geometry, into statistical methodologies, with a recent emphasis on mixture models and information geometry. He has published extensively across diverse journals such as Biometrika, Annals of Statistics, and Psychological Medicine, bridging theoretical and applied statistics. Education: PhD, University of Warwick (1989) MA, University of Oxford (1984) Research Trends: His work explores geometric frameworks for statistical inference, mixture model parameterization, and robustness analysis. Recent publications highlight causal modeling, neural spike train analysis, and high-dimensional data applications. Contact: Office: Mathematics & Computer Building (M3) 4204, Phone: 519-888-4911 x35545, Email: pmarriot@math.uwaterloo.ca

• Statistics
• Actuarial Science
• Markov chain Monte Carlo methods
• +۴ مورد دیگر

Iliopoulos Georgios is a Professor and Department Chair at the Department of Statistics and Actuarial Science, School of Finance and Statistics, University of Piraeus. He has held this position since 2015, following his progression from Assistant Professor (2003-2010) to Associate Professor (2010-2015) at the same institution. Education: 1993: B.A. in Mathematics, Department of Mathematics, University of Patras 1999: PhD in Statistics, Department of Mathematics, University of Patras Iliopoulos specializes in statistical theory and methodology, with particular expertise in Markov chain Monte Carlo methods, Statistical Decision Theory, and Scale parameter estimation. His research focuses on accurate inference under censorship and constrained inference arrangement, addressing fundamental challenges in statistical analysis of complex data structures. His work bridges theoretical statistics with practical applications in reliability analysis and survival analysis. His publication record shows consistent contributions to exact statistical inference methods, particularly for censored data and complex distributions like Laplace and Gamma. His research demonstrates expertise in both parametric and semiparametric approaches, with significant work on variance reduction techniques in computational statistics and solutions to the label switching problem in Bayesian mixture models. Iliopoulos teaches undergraduate courses including Linear Algebra, Statistics II: Hypothesis Testing, and Special Topics in Statistics (Bayesian Statistics), as well as the postgraduate course Computational Statistical Techniques for the Master of Science in Applied Statistics.

• Computational Finance
• International Investments
• Option Pricing
• +۶ مورد دیگر

Nikolas Topaloglou is a Professor of Finance at IPAG Business School since 2014 and holds a visiting professorship at Hellenic Open University since 2009. He previously served as Associate Professor (2010-2014), Assistant Professor (2010-2014), and Lecturer (2006-2010) at Athens University of Economics and Business, and as a Lecturer at HEC Geneva in 2004-2005. Ph.D. in Finance, University of Cyprus (2004) MSc in Decision Sciences (Honors), Athens University of Economics and Business (2000) MSc in Operations Research, University Paris IX Dauphine (1998) Bachelor in Electronics and Computer Engineering, Technical University of Crete (1997) His research spans computational finance, portfolio theory, stochastic programming, and applications of uncertainty models in finance. Key areas include stochastic dominance efficiency testing, prospect theory, international investments, option pricing, and bank liquidity risk. He has published extensively in journals like European Journal of Operational Research and Journal of Econometrics . Recent publications focus on international portfolio risk hedging, stochastic dominance frameworks, commodity diversification, and multidimensional poverty modeling. His methodological approach integrates stochastic programming and econometric testing.

Chu-An Liuمشاهده پروفایل

پژوهشگر ارشد

• Econometrics
• Model Averaging
• Forecast Combination
• +۳ مورد دیگر

Chu-An Liu is a Research Fellow at the Institute of Economics, Academia Sinica (Taipei, Taiwan). He received a PhD in Economics from the University of Wisconsin-Madison in 2012. His teaching experience includes graduate-level and PhD econometrics courses at National Chengchi University (Spring 2022-present) and National University of Singapore (2013-2015). Research Interests: His work focuses on econometrics , model averaging , and forecast combination , with significant contributions to nonparametric methods, causal inference, and statistical modeling for high-dimensional economic data. Key areas include bounds estimation for continuous treatments, spectral analysis in time series, and model uncertainty frameworks. Publications: He has published extensively in top journals like Journal of Econometrics , Econometric Theory , and Journal of Business & Economic Statistics . Recent trends emphasize kernel regressions, autoregressive models, and integration of machine learning with traditional econometric techniques. Academic Collaborations: He has collaborated with scholars such as Xinyu Zhang, Ying-Ying Lee, and Biing-Shen Kuo on topics spanning model selection, causal inference, and nonstationary data analysis.

• Stochastic Geometry
• Spatial Statistics
• Image Analysis
• +۳ مورد دیگر

Claudia Redenbach is a Professor and Dean of Mathematics at RPTU Kaiserslautern, Germany. She is affiliated with the Department of Mathematics and leads the Statistics Working Group (AG Statistik), with additional connections to the Department of Statistics and the Graduate School 'Mathematics as a Key Technology.' Position: Dean of Mathematics Institution: RPTU Kaiserslautern Location: Building 48, Room 534, Gottlieb-Daimler-Straße, 67663 Kaiserslautern Contact: claudia.redenbach@rptu.de | +49 (0)631 205 3620 Professor Redenbach's research focuses on the intersection of stochastic geometry, spatial statistics, and image analysis with applications in materials science. Her work bridges theoretical mathematics with practical engineering applications, particularly in analyzing microstructures of concrete, foams, composites, and other materials. She has developed innovative methods for analyzing spatial point patterns, directional data, and 3D image data from various microscopy techniques including CT, FIB-SEM, and light-sheet microscopy. Her recent publications (2024-2025) reveal a strong emphasis on computational methods for materials analysis, including crack detection in concrete, fiber orientation analysis, artifact removal in imaging, and the development of mathematical morphology techniques for directional data. She frequently collaborates with materials scientists and engineers, with K. Schladitz appearing as a consistent co-author across numerous publications. Her work demonstrates a consistent focus on developing mathematical tools that solve practical problems in materials characterization and analysis. Professor Redenbach has made significant contributions to spatial statistics, particularly in anisotropy analysis of point patterns, and to the application of stochastic geometry models for material microstructure analysis. Her research spans both theoretical developments in statistical methods and their practical implementation for real-world materials science problems.

• Theory of Probability
• Theory of Statistics
• Stochastic Processes
• +۶ مورد دیگر

İsmihan Bayramoğlu is Professor of Mathematics and Statistics and former Dean of the Faculty of Arts and Sciences at Izmir University of Economics, Turkey, serving in the deanship from 2001 to 2022. His academic career spans Azerbaijan State University, the Azerbaijan Academy of Sciences, and Ankara University, where he became a Turkish citizen in 1999. His educational background includes: Applied Mathematics at Baku State University (1976-1981) PhD in Computational Mathematics, Theory of Probability and Mathematical Statistics at National Taras Shevchenko University of Kiev (1984-1988) Bayramoğlu's research centers on order statistics , copulas , and reliability analysis , with significant contributions to distribution characterization and statistical inference. His work bridges theoretical probability with practical applications in engineering and data science, emphasizing bivariate and multivariate models. Recent publications (2020-2023) reveal a focus on bivariate distributions, exceedance statistics, and reliability applications, demonstrating consistent innovation in statistical methodology. His articles frequently address coherent systems, random threshold models, and distributional properties. His scientific recognition includes: Ankara University Science Award (2001) Bayramoğlu serves as Editor-in-Chief of the Journal of Turkish Statistical Association and has organized major international conferences including the International Conference on Advances in Statistics (ICAS), which he chairs. His editorial work spans multiple journals as associate and guest editor. He leads the ICAS symposium series, fostering global collaboration through events in Helsinki, Zagreb, Athens, and St. Petersburg, and has delivered keynote addresses at conferences across 10+ countries.

Emmanuel Caronمشاهده پروفایل

استادیار

• Nonparametric regression
• Model selection
• Slope heuristic
• +۹ مورد دیگر

Emmanuel Caron is an Assistant Professor at Avignon University , affiliated with the Laboratoire de Mathématiques d'Avignon . His research focuses on statistical methodologies and machine learning phenomena. Key research areas include nonparametric regression under dependent observations and statistical learning , particularly the double descent phenomenon. Specializes in model selection , slope heuristic , stationary processes , time series analysis , and hypothesis testing . Contact: emmanuel.caron-parte@univ-avignon.fr

Sylvain Sardyمشاهده پروفایل

دانشیار

• statistical machine learning
• sparsity
• optimization
• +۶ مورد دیگر

Sylvain Sardy is an Associate Professor in the Department of Mathematics at the University of Geneva, where he conducts research at the intersection of statistics, optimization, and machine learning. He is affiliated with the Analysis, Mathematical Physics and Probability research group and has held significant editorial positions including Associate Editor for Computational Statistics and Data Analysis since 2020. Professor Sardy's research focuses on statistical machine learning, sparsity, and optimization with applications spanning astronomy, chemometrics, finance, and tomography. His work develops innovative methods for high-dimensional data analysis, particularly using wavelet-based approaches and LASSO regularization techniques for feature selection, denoising, and model selection. His publications reveal a consistent focus on finding sparse signals in complex datasets across diverse scientific domains. Professor Sardy has mentored numerous graduate students, currently supervising PhD candidate Maxime van Cutsem and having previously guided Dr. Xiaoyu Ma, Dr. Pascaline Descloux, Prof. Jairo Diaz Rodriguez, and Dr. Caroline Giacobino. His Master's students include Jairo Diaz (now Professor at Universidad del Norte, Colombia), Jean-Luc Baeriswyl, and others who have pursued careers in academia, industry, and education. His academic service includes leadership roles as Swiss representative at the European Regional Committee of the Bernoulli Society (2014-2018), President of the Doctoral School of Applied Statistics and Probability (2010-2013), and Student Advisor for the Mathematics Section (2008-2015). His teaching portfolio includes Optimization with Applications I, Statistical Machine Learning, and Pharmaceutical Statistics and Methodology, reflecting his expertise in statistical methodology and its practical implementation.

Chinthaka Kuruwitaمشاهده پروفایل

دانشیار

• Statistics
• Regression Analysis
• Nonparametric Methods
• +۳ مورد دیگر

Chinthaka Kuruwita is an Associate Professor of Statistics at Hamilton College, affiliated with the Department of Mathematics and Statistics. His research focuses on developing advanced regression models and statistical methodologies for diverse applications including public health and network security. Ph.D. in Mathematical Sciences, Clemson University M.S. in Mathematical Sciences, Clemson University B.Sc. in Statistics, University of Colombo Kuruwita's work spans nonparametric and semiparametric statistical techniques, with notable contributions to quantile regression, censored data analysis, and anomaly detection. His research has been applied to adolescent suicide risk modeling and speaker recognition systems. His publications demonstrate expertise in asymmetric kernel density estimation, varying coefficient models, and robust statistical approaches for complex datasets. Key journals include Biometrika, Journal of Statistical Planning and Inference, and IEEE Symposium on Circuits & Systems. Scientific Awards Clayton V. Auconi Outstanding Master's Student Award (2005-06) Sri Lanka Association for the Advancement of Science Best Undergraduate Research (2004) University of Colombo Statistics Gold Medal (2003) V.W. Samaranayake Memorial Gold Medal (2003) Kuruwita teaches courses in mathematical statistics and statistics seminar topics, contributing to data science education at Hamilton College.

• Statistics in Financial Econometrics
• Quantile Regression
• Variable Selection
• +۴ مورد دیگر

Jiang Xuejun is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech). He has been with SUSTech since 2013, initially as a Tenure-Track Assistant Professor and promoted to Associate Professor in 2019. Prior to joining SUSTech, he served at Zhongnan University of Economics and Law. His educational background includes: Ph.D. in Statistics from The Chinese University of Hong Kong (2009) M.Sc. from Yunnan University B.Sc. from National University of Defense Technology Jiang Xuejun's research focuses on advanced statistical methodologies with applications in various domains. His work spans statistical theory development and practical applications in finance, economics, and risk assessment. He has made significant contributions to quantile regression, variable selection, survival analysis, and nonparametric regression methods. His research publications demonstrate a strong trend toward developing innovative statistical methods for high-dimensional data analysis, Bayesian modeling approaches, and applications in financial econometrics and disaster risk assessment. Many of his recent papers focus on quantile regression techniques, dimension reduction methods, and robust statistical testing procedures. Jiang Xuejun has received several prestigious awards: Shenzhen Outstanding Teacher (2018) Southern University of Science and Technology "Outstanding Teaching Award" (2018) "Excellent Mentor Award" from Southern University of Science and Technology (2016) Selected for Shenzhen's "Peacock Plan" for overseas high-level talents He has successfully secured multiple research grants as Principal Investigator, including projects funded by the National Natural Science Foundation of China (both General and Youth programs), Guangdong Provincial Natural Science Foundation, and Shenzhen Science and Technology Innovation Commission. His research portfolio includes work on likelihood inference for high-dimensional models, statistical methods for epidemic disease control, and quantitative trading systems using machine learning. Jiang maintains an active research group focusing on statistical methodology development and applications, with particular emphasis on financial statistics and econometrics. His team collaborates with researchers across multiple disciplines to address complex data analysis challenges in economics, finance, and public health.