جست‌وجوی استاد و پژوهشگر

Zoltán Szabóمشاهده پروفایل

استاد

• Statistical Machine Learning
• Kernel Methods
• Information Theoretical Estimators
• +۳ مورد دیگر

Zoltán Szabó is a Professor of Data Science at the Department of Statistics, London School of Economics (LSE). His research focuses on statistical machine learning, particularly kernel methods, information theoretical estimators, and scalable computation. He has held roles such as Programme Director of the MSc Data Science program and is actively involved in academic service, including Area Chair positions at top conferences like NeurIPS and ICML. His work bridges theory and applications, with contributions to fields like safety-critical learning, economics, climate data analysis, and natural language processing. Education and Affiliations: While specific educational details are not explicitly listed, his career trajectory indicates advanced training in statistics and machine learning. He is affiliated with the LSE's Department of Statistics and the Turing Institute, contributing to academic leadership and interdisciplinary projects. Research Interests: His work emphasizes kernel-based methods, including kernel Stein discrepancies, Hilbert-Schmidt independence criteria, and shape-constrained prediction. Applications span finance, economics, robotics, and environmental data analysis. Recent projects include developing outlier-robust estimators and scalable algorithms for high-dimensional data. Publications: His recent work includes advancements in kernel methods for dependency testing, shape-constrained regression, and robust estimation. Key themes include improving computational efficiency, theoretical guarantees for kernel approximations, and practical applications in interdisciplinary domains. Awards and Grants: While no explicit awards are listed, his contributions to NeurIPS (e.g., a Best Paper Award in 2017) and his role in securing grants (e.g., Europlace Institute of Finance) highlight his impactful research. He also serves on editorial boards, including JMLR and ACM Transactions on Probabilistic Machine Learning. Students and Labs: Supervises PhD students in areas like functional data analysis and scalable computation. Collaborates with researchers on projects such as distribution regression and safety-critical learning, contributing to both theoretical and applied outcomes.

Ran Daiمشاهده پروفایل

استادیار

• High-Dimensional Statistics
• Nonparametric Statistics
• Machine Learning
• +۳ مورد دیگر

Ran Dai, PhD, MS, is an Assistant Professor in the Department of Biostatistics at the University of Nebraska Medical Center (UNMC) College of Public Health. She joined UNMC in 2020. Her research focuses on high-dimensional statistical inference, nonparametric methods, shape-constrained regression, and machine learning applications in clinical trials, drug development, and public health. She has expertise in false discovery rate (FDR) control and collaborates on projects ranging from biostatistics to geology. Education: PhD in Statistics, University of Chicago (2020) MS in Statistics, University of Chicago (2016) PhD in Medicinal Chemistry, University of Minnesota (2015) BS in Pharmaceutical Sciences, Peking University (2009) Research Interests: Dr. Dai’s work emphasizes statistical methodologies for high-dimensional data analysis, particularly in healthcare and environmental contexts. She applies machine learning to understand complex biological systems and public health trends. Recent projects include modeling post-transplant diabetes risks, analyzing electronic health records disparities, and exploring bioturbation in geological data. Articles Trends: Her recent publications highlight advancements in statistical methods for causal inference, mediator selection, and FDR control. Notably, she has investigated the impact of vaccinations and breakthrough infections in solid organ transplant recipients during the pandemic. Her work bridges theoretical statistics and applied problems in medicine and environmental science. Advising & Grants: While no advisees are listed, Dr. Dai collaborates with multidisciplinary teams on NIH-funded projects and contributes to initiatives like the National COVID Cohort Collaborative (N3C). Her expertise in statistical design supports translational research and policy decisions. Labs & Teams: She is affiliated with the UNMC Department of Biostatistics, contributing to collaborative efforts in precision medicine and public health data science.

• Symbolic Regression
• Constraint Evaluation
• Physical Internet
• +۴ مورد دیگر

Christian Alexander Haider is a researcher at University of Applied Sciences Hagenberg, affiliated with the Research Center Hagenberg and multiple Centers of Excellence including Logistics, Smart Production, Digital Transformation, and ICT. His work focuses on developing advanced computational methods for practical applications in logistics and production systems. Haider's research interests center around symbolic regression techniques with particular emphasis on shape-constrained models that incorporate domain knowledge. His work addresses critical challenges in constraint evaluation, simulation optimization, and the Physical Internet concept. He develops methods that ensure regression models respect physical constraints while maintaining accuracy, which is crucial for real-world engineering applications where model violations could have serious consequences. Analysis of his publication trends from 2022-2025 reveals a consistent focus on improving symbolic regression methodologies. His research has evolved from basic constraint evaluation techniques to sophisticated multi-objective approaches that balance multiple competing requirements in model building. The applications span diverse fields including magnetization processes, extrusion manufacturing, and data validation systems, demonstrating the versatility of his methodological contributions. Haider has been actively involved in multiple research projects including: FinCoM - Financial Condition Monitoring (2022-2023) Josef Ressel Zentrum für Symbolische Regression (2018-2022) DigiVent - Predictive Maintenance für Industrie-Radialventilatoren (2017-2020) K.I. für E-Mails (2017-2019) ATROPINE - Fast Track to the Physical Internet (2016-2018) His work is conducted within the Center of Excellence Logistics and Center of Excellence for Smart Production, where he collaborates with researchers across disciplines to develop solutions for complex logistical challenges using advanced computational techniques.

• Nonparametric statistics
• Causal inference
• Survival analysis
• +۳ مورد دیگر

Ted Westling is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on nonparametric statistics, causal inference, survival analysis, and shape-constrained inference. He holds a PhD in Statistics from the University of Washington and a BS in Mathematics from Stanford University. His work emphasizes methodological advancements in causal inference, particularly in observational studies and survival analysis. Notable contributions include developments in nonparametric causal effect estimation, machine learning integration for treatment-specific survival curves, and isotonic regression techniques for monotone functions. Westling's recent publications explore topics such as debiased covariate-adjusted regression, clustered observational studies, and statistical robustness in causal hypothesis testing. His research bridges theoretical statistics with applied problems in healthcare, epidemiology, and social network analysis. He is actively involved in interdisciplinary collaborations, addressing challenges in global health, vaccine efficacy evaluation, and emergency medical services demand forecasting. His methodological innovations aim to enhance the reliability and applicability of statistical methods in real-world settings.

Chuan-Fa Tangمشاهده پروفایل

استادیار

• Statistics
• Survival Analysis
• Statistical Machine Learning
• +۹ مورد دیگر

Chuan-Fa Tang is an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas, affiliated with the School of Natural Sciences and Mathematics. He joined UTD in 2019 and teaches graduate-level courses including Applied Linear Models, Linear Statistical Models, and Nonparametric Methods. Ph.D. in Statistics from University of South Carolina (2017) M.S. and B.S. in Mathematics from National Taiwan University (2010, 2007) His research focuses on order-restricted inference , shape-constrained inference , survival analysis , and statistical machine learning , with particular interest in: Nonparametric and semiparametric models Empirical processes and likelihood methods Post-selection inference Stochastic ordering and heavy-tailed distributions His work includes developing goodness-of-fit tests for Cox models and stochastic ordering frameworks, with software implementations in R . Key publications examine uniform stochastic ordering comparisons, positive quadrant dependence testing, and Taylor's laws for heavy-tailed data. Scientific awards include: NSF DMS 2311292 grant (2023-2026) for monotonic hazard trend research He has contributed computational tools like TestUSO and LLGOF_UniCoxPH packages for statistical analysis.

• Econometrics
• Industrial Organization
• Antitrust
• +۴ مورد دیگر

Dong Li is a Professor of Economics at the University of Texas at Dallas (UT Dallas), affiliated with the School of Economic, Political and Policy Sciences. His research focuses on econometrics, industrial organization (particularly antitrust issues), financial economics, and the Chinese economy. He holds a Ph.D. in Economics from Texas A&M University (2000), an M.A. in Quantitative Economics from Huazhong University of Science & Technology (1994), and a B.A. in Quantitative Economics from the same institution (1991). Li's work emphasizes methodological contributions to panel data models, spatial econometrics, and semiparametric estimation techniques. His recent studies include analyses of cartel behavior in agricultural markets, military aid's impact on terrorism, and the implications of securities transaction taxes in emerging markets. His research bridges theoretical econometrics with applied policy questions, particularly in antitrust and financial regulation contexts. His articles span topics ranging from Bayesian auction analysis to China's economic policies, showcasing interdisciplinary rigor. While no specific awards are noted, his extensive publication record reflects sustained academic influence. His research often addresses practical economic challenges, such as optimizing college admissions systems and evaluating currency valuation impacts on macroeconomic variables like inflation and output growth.

• Medical Natural Language Processing
• Clinical Text Analysis
• Radiology Informatics
• +۵ مورد دیگر

Chaitanya Shivade is a prominent researcher specializing in medical natural language processing with significant contributions to clinical text analysis, radiology informatics, and behavioral health documentation. His work bridges computational linguistics and healthcare applications, focusing on practical solutions for clinical documentation challenges. Shivade's research spans multiple critical areas: developing evaluation frameworks for behavioral therapy notes (TN-Eval), creating shared tasks for medical summarization (MEDIQA), advancing visual dialog systems for radiology, and pioneering synthetic clinical note generation. He has made substantial contributions to textual inference in clinical domains through the MedNLI dataset and has explored fundamental linguistic challenges like negation detection and gradable term analysis in medical text. As a workshop organizer for the NLP for Medical Conversations series, he has helped shape community standards and foster collaboration. His publication record demonstrates consistent leadership in applying NLP to real-world healthcare problems, with particular emphasis on evaluation methodologies, dataset creation, and practical clinical applications. Shivade has collaborated extensively with medical professionals and researchers across institutions to ensure clinical relevance of his technical work. Organized MEDIQA shared tasks (2019, 2021) Co-organized NLP for Medical Conversations workshops (2019, 2020) Developed TN-Eval framework for therapy note quality assessment Created MedNLI dataset for clinical textual inference Pioneered synthetic clinical note generation approaches His work consistently addresses the tension between clinical utility and technical innovation, with growing emphasis on evaluating LLM performance in healthcare contexts. The progression from foundational clinical NLP techniques to complex evaluation frameworks demonstrates his evolving research trajectory toward ensuring reliable AI deployment in medical settings.

• 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.

• Machine Learning
• Recommender Systems
• Data Privacy
• +۷ مورد دیگر

Mihaela Curmei is a researcher active in machine learning, recommender systems, and algorithmic fairness, with significant contributions to privacy-preserving methods, behavioral modeling, and optimization. Her collaborative work spans institutions and co-authors like Benjamin Recht, Georgina Hall, and Sarah Dean. Research Themes: Shape-constrained regression using polynomial optimization Privacy in matrix factorization with public features Dynamic preference modeling grounded in psychology Multi-learner systems under participation dynamics Temporal impacts of recommendation algorithms Technical Domains: Sum-of-squares programming Federated and distributed learning Stochastic reachability analysis Gradient flow-based dataset modeling Key Venues: Published in Operations Research , NeurIPS, ICML, RecSys, FAccT, and preprint archives. Her recent work (2023-2025) focuses on temporal effects in recommendations, private computation methods, and equilibrium analysis in multi-agent games. Earlier projects (2017-2021) include search engine indexing techniques (BitFunnel) and foundational studies on offline/online metric correlations in recommendation systems.

Miguel Arenas Bustoمشاهده پروفایل

استاد

• Population Genetics
• Molecular Evolution
• Molecular Adaptation
• +۶ مورد دیگر

Miguel Arenas Busto is a Professor at the University of Vigo, Spain, affiliated with the Faculty of Biology and the Department of Biochemistry, Genetics and Immunology. He leads the Computational Molecular Evolution (CME) Team within the Biomedical Research Center at the Vigo campus. His career includes positions as Principal Investigator under the Ramón y Cajal program (2017-2022), postdoctoral research at prestigious institutions including the University of Bern and the Centre for Molecular Biology 'Severo Ochoa', and a PhD in Genetics from the University of Vigo completed cum laude in 2010. PhD in Genetics, University of Vigo (2010), Cum Laude PostDoc, University of Bern (2010-2012) PostDoc, Centre for Molecular Biology 'Severo Ochoa' CSIC (2012-2015) Principal Investigator, IPATIMUP/i3S, University of Porto (2015-2017) Principal Investigator (Ramón y Cajal), University of Vigo (2017-2022) Professor PCD, University of Vigo (2022-2023) Professor TU, University of Vigo (2023-Present) Arenas specializes in the evolutionary analysis of genetic data at molecular and population levels. His research encompasses Population Genetics, Molecular Evolution, Molecular Adaptation, Genetic Diversity, Phylogenetics, Phylogenomics, and the evolution of structure and function of biological macromolecules. He has developed computational methods and software tools for molecular evolution analysis, including Recodon, NetRecodon, ProteinEvolver, and ProtASR. His recent publications reveal a strong focus on protein evolution with structural constraints, substitution model development, ancestral sequence reconstruction, and viral evolution (particularly HIV-1 and SARS-CoV-2). A significant trend across his work is the integration of protein folding stability into evolutionary models, demonstrating how structural constraints shape molecular evolution. His research bridges computational biology with experimental validation, with applications spanning infectious disease dynamics, molecular adaptation, and phylogenetic inference. Thesis Award (best Thesis of University of Vigo, 2010) Group Leader Junior of Computational Molecular Evolution Group (CINBIO, 2021) Group Leader of Computational Genetics and Genomics Group (IPATIMUP/I3S) EMBO Fellowship (2014) Ramón y Cajal Grant from Spanish Government (2017-present) Arenas has supervised numerous undergraduate, graduate, and PhD students, with a focus on computational approaches to evolutionary questions. His research has been funded by multiple agencies including the Spanish Ministry of Economy and Competitiveness, Xunta de Galicia, Fundación BBVA, The Foundation for Science and Technology, European Molecular Biology Organization, and EU Framework Programs. He serves as Associate Editor for several journals including Genes and Frontiers in Ecology and Evolution, and as a reviewer for numerous high-impact journals and research agencies across Europe. He directs the Computational Molecular Evolution (CME) Team at the University of Vigo, which focuses on developing computational methods for evolutionary analysis, with particular emphasis on protein evolution constrained by structural stability. The lab maintains active collaborations with research groups across Europe and the United States, participating in international networks including the Society for Molecular Biology and Evolution and the Spanish Society of Bioinformatics and Computational Biology.

Georgios Iliopoulosمشاهده پروفایل

استاد

• Markov chain Monte Carlo methods
• Statistical Decision Theory
• Scale parameter estimation
• +۲ مورد دیگر

Professor Georgios Iliopoulos is a distinguished academic serving as Professor and Department Chair of the Department of Statistics and Actuarial Science at the University of Piraeus. With an extensive academic career spanning over two decades, Professor Iliopoulos has established himself as a leading expert in statistical theory and methodology. His leadership as Department Chair demonstrates his significant contribution to the academic community and his commitment to advancing statistical education and research. Professor Iliopoulos completed his educational journey at the University of Patras, earning his B.A. in Mathematics in 1993 followed by a PhD in Statistics in 1999. His academic career progressed through various institutions before he settled at the University of Piraeus, where he has held positions from Assistant Professor (2003-2010) to Associate Professor (2010-2015) and ultimately to Professor (2015-present). Professor Iliopoulos's research focuses on several key areas of statistical theory, with particular emphasis on Markov chain Monte Carlo methods, Statistical Decision Theory, Scale parameter estimation, Accurate inference under censorship, and Constrained inference arrangement. His work bridges theoretical statistics with practical applications, demonstrating how sophisticated statistical methods can solve real-world problems across various domains. His research has consistently addressed challenging theoretical questions while maintaining relevance to practical statistical challenges faced by researchers and practitioners. Analysis of Professor Iliopoulos's publication record reveals a strong focus on theoretical statistics with applications in reliability analysis, Bayesian inference, and censoring methodologies. His work demonstrates a consistent pattern of advancing statistical theory while maintaining practical relevance, particularly in the areas of parameter estimation, confidence interval construction, and inference with censored data. The interdisciplinary nature of his research is evident in publications spanning journals in statistics, biostatistics, and computational statistics. Professor Iliopoulos is actively involved in teaching both undergraduate and postgraduate courses. At the undergraduate level, he teaches Linear Algebra, Statistics II: Hypothesis Testing, and Special Topics in Statistics (Bayesian Statistics). For postgraduate students, he offers Computational Statistical Techniques as part of the Master of Science in Applied Statistics program. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications of advanced statistical methods.

• Statistics
• Nonparametric Statistics
• Semiparametric Statistics
• +۳ مورد دیگر

Professor Natalie Neumeyer is a faculty member in the Department of Mathematics at the University of Hamburg, specifically within the Division of Mathematical Statistics and Stochastic Processes. Her office is located at Bundesstraße 55, 20146 Hamburg. Her research focuses on advanced statistical methodologies with emphasis on: Nonparametric and semiparametric techniques under structural constraints Monotonicity and concavity assumptions in regression and density estimation Adaptive inference procedures with theoretical efficiency guarantees She actively leads multiple German Research Foundation (DFG) projects including the Research Unit 'Structural Inference in Statistics: Adaptation and Efficiency', demonstrating significant research leadership. Her current projects investigate shape-constrained estimation methods and semiparametric structural analysis, contributing to foundational advances in statistical theory. Professor Neumeyer's work shows consistent funding support through DFG grants, indicating strong peer recognition of her methodological contributions to statistical science.