جست‌وجوی استاد و پژوهشگر

• Structural Dynamics
• Vibration Control
• System Identification
• +۲ مورد دیگر

Antonina Pirrotta is Full Professor at University of Palermo, serving as Director of the PhD School (since 2023) and CIDiS Research Center (since 2021). Her research in structural dynamics includes vibration control systems and probabilistic methods. Honors include the EASD Senior Research Prize (2020), EMI Fellowship (2020), and International Excellence Fellowship at KIT (2021). Innovations include optimized dampers for seismic and wind load mitigation. Research integrates analytical stochastic methods with experimental dynamics. Recent publications advance fractional calculus applications in random vibration theory and control device optimization. Editorial roles include Associate Editor for Meccanica and Journal of Engineering Mechanics .

Zheyang Wuمشاهده پروفایل

استاد

• Biostatistics
• Bioinformatics
• Statistical Genetics
• +۷ مورد دیگر

Zheyang Wu is a Professor of Mathematical Sciences (Biostatistics) at Worcester Polytechnic Institute (WPI), affiliated with the Bioinformatics and Computational Biology and Data Science programs. He holds a PhD and MPH from Yale University. His research focuses on bridging statistics with data-driven biomedical sciences, including signal detection in genetic studies, computational statistics, and predictive modeling for cardiovascular and neurodegenerative diseases. He has secured grants totaling over $3M from NSF and NIH, including DMS-2113570 for p-value combination methods and DMS-1812082 for ALS exome sequencing analysis. Wu's lab emphasizes interdisciplinary teamwork, with current and alumni students working on topics like p-value combination algorithms (e.g., TFisher, GFisher), rare variant analysis, and biomechanical modeling of plaque progression. He teaches courses in applied statistics, biostatistics, probability, and genetics. His work has led to software packages like SetTest, Qapprox, and GFisher, widely used in genomic studies. Collaborations include projects with Prof. Dalin Tang on cardiovascular modeling and Prof. Songbai Ji on concussion biomechanics. Wu advises over 20 graduate students and has published extensively in top journals like Annals of Applied Statistics , Biometrics , and IEEE Transactions on Signal Processing . His teaching mission emphasizes deep understanding and practical application, with MQP projects addressing real-world statistical challenges in healthcare and engineering.

Amin Coja-Oghlanمشاهده پروفایل

استاد

• Probabilistic Combinatorics
• Mathematical Physics
• Disordered Systems
• +۲ مورد دیگر

Amin Coja-Oghlan is Professor of Efficient Algorithms and Complexity Theory at TU Dortmund University's Department of Computer Science. His research integrates probabilistic combinatorics, information theory, and statistical physics to solve fundamental problems in theoretical computer science. Education includes a doctorate in Mathematics (University of Hamburg, 2002) and habilitation in Computer Science (Humboldt University Berlin, 2005). Research advances understanding of phase transitions in constraint satisfaction problems, optimization landscapes, and random structures. Recent publications analyze SAT thresholds, group testing, and sparse matrix properties. Academic appointments include professorships at Goethe University Frankfurt and lectureships at Edinburgh and Warwick. Research contributions bridge discrete mathematics with computational complexity.

• Machine Learning
• High-Dimensional Statistics
• Network Models
• +۳ مورد دیگر

Yang Feng is a Professor and Ph.D. Program Director in the Department of Biostatistics at New York University's School of Global Public Health. He holds affiliate faculty positions at the Center for Data Science and PRIISM. His research focuses on Machine Learning, High-Dimensional Statistics, Network Models, and Applications in Biostatistics and Public Health. Key contributions include advancements in Neyman-Pearson classification, federated learning, transfer learning, and high-dimensional statistical methods. He has authored influential papers in journals such as the Annals of Statistics, JASA, and Journal of Machine Learning Research. Recipient of prestigious honors: ASA Fellow, IMS Fellow, and ISI Elected Member. Leading an NSF-funded project on high-dimensional multi-task learning inference (Grant DMS-2324489). Editorial roles at top journals including JASA and AOAS. Research emphasizes theoretical foundations and practical applications, spanning statistical methodology, algorithm development, and interdisciplinary collaborations in health and data science.

Loriano Manciniمشاهده پروفایل

استاد

• Financial Econometrics
• Corporate Finance
• Volatility and Liquidity Risks
• +۳ مورد دیگر

Loriano Mancini is a Full Professor of Finance at the Institute of Finance, Università della Svizzera italiana (USI Lugano), where he has been faculty since 2017. He obtained his PhD in Economics from USI in 2004, followed by research positions at Princeton University and professorships at the University of Zurich and Swiss Finance Institute at EPFL. His research focuses on financial econometrics, specializing in volatility and liquidity risks, interbank markets, corporate finance, and statistical analysis of financial data. Recent work examines risk premia modeling, corporate cash flow dynamics, and market microstructure. Publications show sustained focus on quantitative finance methods, with emerging themes in derivatives pricing and systemic risk. His articles consistently appear in top-tier journals including Journal of Finance and Review of Financial Studies . Awards include: Outstanding Paper in International Finance (Eastern Finance Association 2010) Best Paper in Quantitative Finance (Quantitative Methods in Finance Conference 2005) He has advised multiple PhD students at USI, EPFL and University of Zurich. Major grants include Swiss National Science Foundation funding for projects on climate risk (CHF 800k) and corporate short-termism (CHF 590k). He serves as Associate Editor for Journal of Financial Econometrics and directs the Master in Finance program at USI.

Sergios Agapiouمشاهده پروفایل

دانشیار

• Nonparametric Bayesian Statistics
• Asymptotic Statistics
• Inverse Problems
• +۵ مورد دیگر

Sergios Agapiou serves as Associate Professor in the Department of Mathematics and Statistics at the University of Cyprus, School of Natural and Applied Sciences since 2024, following prior roles as Lecturer (2015-2019) and Assistant Professor (2019-2024). His academic credentials include: Bachelor of Applied Mathematics and Physical Sciences, National Technical University of Athens (2009) M.Sc. in Mathematics, University of Warwick (2010) Ph.D. in Mathematics, University of Warwick (2013) His research explores theoretical foundations of Bayesian inference with emphasis on asymptotic behavior in nonparametric settings and computational methods for infinite-dimensional problems . Key focus areas include statistical inverse problems, Monte Carlo techniques in function spaces, and intersections between differential equations and high-dimensional statistics, addressing fundamental challenges in modern statistical theory. No scientific awards were documented in the source material. Professional trajectory shows continuous academic employment since postdoctoral positions at the University of Warwick (2013-2015), though specific grant funding or student supervision details remain unreported. No laboratory or research team affiliations are specified in the available text.

Ilias Chronopoulosمشاهده پروفایل

مدرس

• Financial Econometrics
• High-Dimensional Statistics
• Time Series Analysis
• +۱ مورد دیگر

Ilias Chronopoulos is a Lecturer in Finance at the Essex Business School , University of Essex, specializing in financial econometrics and machine learning. He joined the university in April 2022 and holds a PhD in Econometrics from King's College London (2022), an MRes in Economics (2017), and an MSc in Economics (2016) from Queen Mary University of London. His research focuses on: High-dimensional statistics for financial data Machine learning applications in econometric modeling Time series analysis and volatility forecasting Deep neural networks for risk management Key publications include work in the Annals of Statistics and Journal of Financial Econometrics , with recent studies on deep learning for Value-at-Risk forecasting and penalized estimation in high-dimensional settings. His teaching includes courses in Financial Modelling, Financial Time Series, and Research Methods in Financial Econometrics.

Axel Bücherمشاهده پروفایل

استاد

• Extreme value statistics
• Nonparametric statistics
• Copulas
• +۴ مورد دیگر

Prof. Axel Bücher holds the Chair of Mathematical Statistics at Ruhr-Universität Bochum, part of the Faculty of Mathematics. He specializes in extreme value statistics, copula models, and time series analysis. His research bridges theoretical advancements with practical applications in insurance risk, climate science, and financial modeling. Bücher has been recognized with the GAUSS Prize (2024) for his work on claim reserving models with Alexander Rosenstock. Education: Studied mathematics at Ruhr-Universität Bochum (2003–2008), earned his PhD in statistics there in 2011 under Prof. Holger Dette. Postdoctoral work at Université catholique de Louvain (Belgium) followed, before interim professorships at Heidelberg and Dortmund universities. Served as full professor at Heinrich-Heine-Universität Düsseldorf (2018–2023) before returning to Bochum in 2023. Research focuses on extreme value theory, nonparametric methods, and statistical applications in actuarial science. His recent work includes developing novel copula-based models and neural network approaches for predicting insurance claims. Current projects include ClimXtreme, analyzing extreme weather events' statistical patterns under climate change. Publications highlight contributions to extreme value copulas, tail dependence estimation, and block maxima methods. Over 70 peer-reviewed papers appear in top journals like Annals of Statistics and Bernoulli. Serves as associate editor for Journal of Statistical Planning and Inference, Bernoulli Journal, and Statistical Inference for Stochastic Processes. Advising: Supervised doctoral student Alexander Rosenstock (GAUSS Prize co-recipient). Engaged in promoting early-career researchers through collaborative projects and interdisciplinary initiatives.

Robin Pemantleمشاهده پروفایل

استاد

• Probability Theory
• Analytic Combinatorics
• Stochastic Processes
• +۴ مورد دیگر

Robin Pemantle is a Professor of Mathematics and affiliated with the Computer and Information Science department at the University of Pennsylvania's School of Engineering and Applied Science. He holds primary appointment in the Department of Mathematics. His research spans probability theory, analytic combinatorics, stochastic processes, and mathematics education. He co-authored influential works such as There is No One Way to Teach Math (2024) and Analytic Combinatorics in Several Variables (2024), emphasizing active learning strategies and combinatorial methodologies. Research interests include asymptotic analysis of generating functions, percolation theory, and applications of probability to biology and networks. He has advised over 20 PhD and Master's students, focusing on topics like random walks, combinatorial models, and education technology. Pemantle leads initiatives like the Penn Calculus Project, redesigning calculus curricula with active learning principles. His work bridges pure and applied mathematics, with contributions to statistical physics (e.g., Ising models) and theoretical computer science. Notable recent studies address invasion percolation on trees, trace reconstruction algorithms, and aggregation methods for probabilistic forecasts.

Weijie Suمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence
• Differential Privacy
• +۳ مورد دیگر

Weijie Su is an Associate Professor in the Department of Computer and Information Science at the University of Pennsylvania's School of Engineering and Applied Science. His research focuses on machine learning, artificial intelligence, differential privacy, statistical theory, optimization algorithms, and data science. He explores foundational questions in large language models (LLMs), alignment mechanisms, privacy-preserving techniques, and algorithmic optimization. His work bridges theoretical insights with practical applications in AI ethics, peer review systems, and decentralized learning. Notable research themes include analyzing LLM statistical foundations, developing matrix-gradient optimizers (e.g., PolarGrad), and addressing challenges in copyright and privacy for generative AI. He has contributed to frameworks for calibration-aware fine-tuning, magnetic preference optimization, and watermarking strategies. His studies also extend to game-theoretic alignment limits, communication-efficient optimization over manifolds, and bias mitigation in peer review. His publications span top conferences and journals, emphasizing interdisciplinary approaches to AI safety, differential privacy, and algorithmic robustness. He actively explores theoretical underpinnings of deep learning dynamics, including neural collapse under privacy constraints and the interplay between optimization and stability.

Woldegebriel Assefa Woldegerimaمشاهده پروفایل

استادیار

• Biomathematics
• Differential Equations
• Infectious Disease Modeling
• +۲ مورد دیگر

Dr. Woldegebriel Assefa Woldegerima is an Assistant Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. He previously held a Postdoctoral Research Fellowship at the University of Pretoria, South Africa. His academic credentials include a Ph.D. in Applied Mathematics from the University of Buea (co-supervised with Lehigh University), and Master's degrees from the African Institute for Mathematical Sciences and Addis Ababa University. His research program focuses on mathematical modeling of biological systems, with particular emphasis on infectious disease dynamics, in-host pathogen interactions, and the impact of climate change on disease transmission. Specific research areas include biomathematics, differential equations, artificial intelligence applications in epidemiology, and computational analysis of disease dynamics using Python-based frameworks. Dr. Woldegerima's publications demonstrate a consistent focus on developing novel mathematical frameworks for emerging infectious diseases. His recent work employs machine learning techniques to model transmission dynamics of pathogens including MPXV, Zika virus, and avian influenza. A significant portion of his research utilizes geospatial analysis and Bayesian hierarchical modeling to investigate socio-environmental determinants of disease spread across diverse global contexts.

Song Caiمشاهده پروفایل

دانشیار

• Empirical Likelihood
• Asymptotic Theory
• Density Ratio Model
• +۳ مورد دیگر

Song Cai is an Associate Professor at the School of Mathematics and Statistics, Carleton University. His research focuses on advanced statistical methodologies, including Empirical Likelihood, Asymptotic Theory, and Spatio-temporal Modeling. He is affiliated with the Ottawa-Carleton Institute for Mathematics and Statistics (OCIMS). Education details are not explicitly provided, but his professional role suggests advanced training in statistics or related fields. His research interests emphasize non-parametric and semi-parametric inference, alongside computational statistics. No specific awards or grants are mentioned in the text. He does not have listed advisees or students. His office is located at 5215 HP, and he can be reached via email: scai@math.carleton.ca .

Andreas Hagemannمشاهده پروفایل

دانشیار

• Econometrics
• Quantitative Methods
• Cluster-Robust Inference
• +۳ مورد دیگر

Andreas Hagemann is an Associate Professor of Business Economics and Public Policy at the Stephen M. Ross School of Business, University of Michigan. As of 2024/25, he is on sabbatical leave. His work focuses on econometric methods, causal inference, and quantitative strategies in business and policy contexts. Research interests include cluster-robust statistical techniques, permutation inference, collider bias in management studies, and quantile regression applications. His contributions span methodological advancements and applied analyses in areas like treatment effect evaluation, policy impact assessment, and behavioral economics. Key publications address challenges in finite cluster inference, moral hazard in airline frequent flyer programs, and the causal effects of college education on mortality. His methodological work has led to software implementations in R, such as the 'quantreg' package's cluster option. He collaborates with scholars across disciplines, including marketing science (e.g., Y. Orhun and T. Guo) and health economics (e.g., K. Buckles). His research emphasizes rigorous statistical frameworks for addressing real-world economic questions and policy design.

Andrew Carterمشاهده پروفایل

دانشیار

• Asymptotic Statistical Inference
• Nonparametric Regression
• Mathematical Statistics
• +۶ مورد دیگر

Andrew V. Carter is an Associate Professor and Graduate Vice Chair in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. His research focuses on asymptotic statistical inference, nonparametric regression, and equivalence theory for statistical experiments. Email: carter@pstat.ucsb.edu Office: South Hall 5507 Research Interests include nonparametric function estimation, wavelet algorithms, regime-switching models, cluster-robust variance estimation, and Gaussian process approximations. He has worked on extending asymptotic equivalence theory to handle unknown variances, correlated noise, and multidimensional regression problems. Publications analyze topics such as regime-switching detection in econometric models, deficiency distances between experiments, and applications of wavelet decompositions. His work bridges theoretical statistics with practical challenges in financial, biological, and survey data analysis.

Rao Jammalamadakaمشاهده پروفایل

استاد

• Mathematical Statistics
• Probability
• Bayesian Analysis
• +۴ مورد دیگر

Rao Jammalamadaka is a Distinguished Professor in the Department of Statistics and Applied Probability at the University of California, Santa Barbara. His research focuses on mathematical statistics, probability, and their applications to circular data analysis, Bayesian methods, and survival analysis. He holds a PhD from the Indian Statistical Institute. Education : PhD in Statistics, Indian Statistical Institute. Research Interests : Dr. Jammalamadaka’s work spans foundational and applied statistical theory. Key areas include circular statistics, Bayesian model selection, hypothesis testing for directional data, multivariate analysis, and applications in biostatistics and actuarial science. He has pioneered methods for analyzing circular data using von Mises and wrapped stable distributions, with applications to wind direction modeling, medical imaging (e.g., OCT data), and survival analysis in pandemic studies. Advising & Grants : While specific advising details are not listed, his extensive publications suggest mentorship in statistical methodology and interdisciplinary research. Grants and funding information are not explicitly mentioned in the provided text. Labs/Teams : No specific laboratories or research groups are detailed here, though his work implies collaboration with interdisciplinary teams in biostatistics and data science.