جست‌وجوی استاد و پژوهشگر

Annette Kopp-Schneiderمشاهده پروفایل

استاد

• Medical Biometry
• Biostatistics
• Clinical Trials
• +۷ مورد دیگر

Prof. Dr. Annette Kopp-Schneider is Head of Department at the German Cancer Research Center (DKFZ) and affiliated with the Faculty of Medicine at the University of Heidelberg. She holds a professorial rank and leads research in medical biometry with a focus on clinical trial methodology in precision oncology. Diploma in Mathematics, RWTH Aachen, 1984 Dr. rer. nat. in Mathematics, RWTH Aachen, 1987 Habilitation in Medical Biometry, University of Heidelberg, 1997 Her research interests include clinical trials for precision oncology , dose-response modeling , and biologically based mechanistic modeling of cell systems . She develops advanced statistical methods for trial design, particularly in pediatric oncology and settings with small sample sizes. Her work emphasizes rigorous control of type I error when borrowing historical data and optimal experimental designs for drug combinations. The recent publications reflect a strong trend in methodological biostatistics , particularly in Bayesian and frequentist approaches to clinical trial design , external data incorporation , and transparent evaluation in biomedical image analysis . Her contributions span oncology, toxicology, and translational medicine, with a consistent focus on statistical rigor and reproducibility. She has contributed to major initiatives such as the Pediatric Precision Oncology INFORM Registry, advancing personalized treatment for children with cancer. Her methodological work supports fair comparisons in hybrid and single-arm trials and improves the reliability of challenge-based research in medical imaging. Prof. Kopp-Schneider is actively engaged in grant-funded research and mentoring within her department. She leads a team focused on biostatistical innovation in clinical research, particularly in oncology settings where data are sparse and traditional designs face limitations. She is involved in collaborative teams working on biomedical image analysis challenges and pediatric cancer registries. Her lab contributes to open science through toolkits for challenge analysis and transparent reporting guidelines like BIAS (Transparent Reporting of Biomedical Image Analysis Challenges).

Silvia Calderazzoمشاهده پروفایل

پژوهشگر

• Bayesian Statistics
• Clinical Trial Design
• Markov Models
• +۵ مورد دیگر

Dr. Silvia Calderazzo is a researcher specializing in Bayesian statistics and its applications in clinical and biomedical research. Her work centers on statistical methodology for clinical trial design, dose-response modeling, and inference in complex biological systems. Research Interests: Her primary areas include Bayesian clinical trial design, Markov models, and statistical modeling of dose-response relationships. She applies advanced statistical techniques to problems in toxicology, oncology, and public health, with a strong methodological focus. Publication Trends: Her recent publications (2018–2019) show a consistent emphasis on Bayesian methods, particularly in quantifying prior information, incorporating external data in trials, and modeling biological and toxicological responses. These works appear in top journals such as JAMA , Biometrics , and Archives of Toxicology , reflecting both methodological rigor and clinical relevance. Education: PhD in Statistics, University of Warwick, UK (2017) Master degree in Statistics, University of Padua, Italy (2012) Bachelor degree in Statistics, population and society, University of Padua, Italy (2010) Scientific Awards: Advising and Grants: No information is available regarding students advised or research grants received. Labs and Teams: While specific lab affiliations are not mentioned, her collaborative publications suggest involvement in interdisciplinary research teams focused on biostatistics and translational medicine.

Alexander Aguilarمشاهده پروفایل

استاد مدعو

• Industrial-Organizational Psychology
• Business Psychology
• Organizational Leadership
• +۶ مورد دیگر

Dr. Alexander Aguilar serves as Affiliate Faculty at The Chicago School of Professional Psychology, teaching in the Department of IO/Business Psychology, Organization Leadership, and Behavioral Economics across Online campuses. With ten years of institutional involvement since completing his Psy.D. in 2013, he concurrently leads People Analytics product development at Perceptyx for global enterprises including 30 Fortune 100 companies. His educational credentials include: B.A. in Psychology with Economics minor from Ohio Wesleyan University (2008) M.A. from The Chicago School of Professional Psychology (2010) Psy.D. from The Chicago School of Professional Psychology (2013) Dr. Aguilar specializes in merging Industrial-Organizational Psychology with advanced data science, focusing on innovative survey methodologies, research design, and statistical analysis (Frequentist/Bayesian frameworks). His work challenges traditional data practices to maximize organizational impact through efficient analytics implementation, particularly in behavioral economics and leadership contexts. He prioritizes translating theoretical concepts into actionable business solutions. As an educator, he emphasizes student accountability—requiring thorough preparation, active application of feedback, and proactive question-asking to foster professional growth. His industry experience at Perceptyx directly informs curriculum development, ensuring students gain practical skills in people analytics for real-world HR and organizational challenges.

Alexander Lincoln Readمشاهده پروفایل

استاد

• Precision Higgs boson measurements
• Dark Matter detection
• Statistical analysis in particle physics
• +۳ مورد دیگر

Alexander Lincoln Read is a Professor at the University of Oslo's High Energy Physics department, specializing in precision measurements of the Higgs boson through the ATLAS experiment at CERN. His research bridges particle physics and advanced statistical/data analysis methods. Education: BS (1981, University of Illinois) | PhD (1986, University of Colorado) Employment: NAVF Scientific Assistant (1986-89) | CERN Scientific Associate (1989-91) | Professor (1993-present) Research Focus: Higgs boson properties, Dark Matter connections, CLs statistical technique, Compressed Sensing, Gaussian Processes, and Machine Learning applications in particle physics. Scientific Contributions: Key role in Higgs boson discovery (2012), development of the CLs method, and detector calibration innovations. Scientific awards: CLs technique creator | Higgs discovery contributor Projects: ATLAS experiment, NorLHC (extreme collision rates), Insights ITN (statistics network), Strategic Dark Matter Initiative.

• Structural Equation Modeling
• Bayesian Statistics
• Adolescent Mental Health
• +۳ مورد دیگر

Dr. G Smid is a Researcher in the Department of Psychology within the Faculty of Social and Behavioural Sciences at Utrecht University, specializing in Clinical Psychology. His expertise centers on advanced statistical methodologies applied to psychological research, with particular emphasis on structural equation modeling under constrained data conditions. His primary research domains include Structural Equation Modeling, Bayesian Statistics, Adolescent Mental Health, Clinical Psychology, Data Synthesis, and Systematic Reviews. He has pioneered critical methodological frameworks for Bayesian SEM applications with small samples, developed interpretable synthetic data techniques, and investigated adolescent mental health determinants including harmful sexual behavior and immigration-related impacts. His work bridges complex statistical theory with clinical psychology applications. Analysis of Dr. Smid's 13 publications (2014-2023) reveals a dominant methodological trajectory focused on overcoming small-sample limitations in psychological research. His most influential contributions address Bayesian prior specification in SEM, measurement equivalence validation, and adolescent mental health risk factors. The publications demonstrate consistent innovation in statistical methodology while maintaining strong clinical relevance, particularly in adolescent psychology and mental health assessment.

• Applied Data Science
• Network Analysis
• Longitudinal Data Analysis
• +۶ مورد دیگر

Dr. Mahdi Shafiee Kamalabad is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. His research focuses on developing advanced statistical and machine learning methods for complex data analysis, particularly in social and behavioral sciences, life sciences, and bioinformatics. Applied Data Science Network Analysis Bayesian Statistics Longitudinal Data Analysis He specializes in Dynamic Bayesian Network Models, Relational Event Models, and Change Point Detection algorithms. His work spans interdisciplinary collaborations, combining educational psychology, applied linguistics, and data science to improve understanding of multilingual classroom interactions and epidemic prediction models. He has contributed to R software packages like remify, remstats, and remstimate for relational event history data analysis. Notable projects include "Better Together: A Social Network Analysis of Multilingual Interactions in the Classroom" (2022) and methodological developments for malaria dynamics analysis in Cameroon. His teaching includes Data Wrangling and Data Analysis courses. Funding sources include Utrecht University's Faculty of Social and Behavioural Sciences.

Roderick Joseph Littleمشاهده پروفایل

استاد

• Biostatistics
• Missing Data Analysis
• Survey Sampling
• +۳ مورد دیگر

Roderick Joseph Little is the Richard D. Remington Distinguished University Professor of Biostatistics at the University of Michigan School of Public Health. He is also a Professor in the Department of Statistics and a Research Professor at the Institute for Social Research. Dr. Little previously chaired the Biostatistics Department from January 2007 to December 2009 and from 1993 to 2001. Dr. Little earned his educational credentials from prestigious institutions: PhD in Statistics, London University, 1974 MSc in Statistics and Operational Research, London University, 1972 BA in Mathematics, Cambridge University, 1971 Dr. Little's primary research interests focus on the analysis of data with missing values and complex survey designs. His work has revolutionized methods for handling missing data, moving from ad-hoc approaches like discarding incomplete cases to sophisticated model-based methods using likelihood-based inferential techniques. He is particularly known for his work on pattern-mixture models and penalized spline of propensity prediction methods. His research also extends to model-based approaches for survey analysis that are robust to misspecification. Dr. Little's inferential philosophy is model-based and Bayesian, though he carefully considers the effects of model misspecification. His applied interests span mental health, demography, environmental statistics, biology, economics, and social sciences. Dr. Little has received numerous prestigious awards and recognitions including the Wilks' Memorial Award from the American Statistical Association, being named an ISI highly cited researcher, and election to the National Academy of Medicine. He is also a Fellow of the American Statistical Association, the American Academy of Arts and Sciences, and the Royal Statistical Society. Throughout his career, Dr. Little has chaired or co-chaired 30 doctoral committees, mentoring the next generation of statisticians. He has served in significant editorial and leadership roles, including as Coordinating and Applications Editor of the Journal of the American Statistical Association (1992-1994), co-editor of the Journal of Survey Statistics and Methodology (2016-2018), and Vice President of the American Statistical Association (2010-2012). From September 2010-January 2013, he served as the inaugural Associate Director for Research and Methodology and Chief Scientist at the U.S. Census Bureau. Dr. Little is best known for his seminal book "Statistical Analysis with Missing Data" (co-authored with Donald Rubin), now in its third edition (2019). His work has had profound impacts on both theoretical statistics and practical applications across numerous scientific disciplines.

• Statistics
• Probability
• Machine Learning
• +۶ مورد دیگر

Feng Yu is a Senior Lecturer in Statistics at the School of Mathematics, University of Bristol, focusing on probability and statistical methods. Research interests include Bayesian inference, statistical hypothesis testing, machine learning applications in medical research, and interdisciplinary work with biomedical engineering. Key contributions involve developing methods for multiple comparison procedures, dose-finding trials, and 3D printing parameter optimization using AI. Recent publications highlight trends in integrating statistical rigor with biomedical innovation, spanning clinical trials, dose-finding studies, and additive manufacturing for bone scaffolds and implants. Collaborations include work on hybrid systems for tissue regeneration. Projects include "Girsanov Transformation and the Rate of Adaptation" (2011-2013), exploring probabilistic models in evolutionary biology. Affiliations are centered on the University of Bristol, with no scientific awards mentioned.

Conor Mayo-Wilsonمشاهده پروفایل

دانشیار

• Formal Epistemology
• Logic
• Philosophy of Mathematics
• +۵ مورد دیگر

Conor Mayo-Wilson is an Associate Professor in the Department of Philosophy at the University of Washington , specializing in formal epistemology, logic, and philosophy of science. His research spans interdisciplinary methodologies bridging philosophy with statistics, game theory, and causal inference. Education: Ph.D., Philosophy, Carnegie Mellon University (2012) M.S., Logic and Computation, Carnegie Mellon University (2009) M.S., Mathematics, Carnegie Mellon University (2009) B.S., Mathematics, Stanford University (2006) B.A., Philosophy, Stanford University (2006) His research focuses on the intersection of epistemology , statistics , and causal inference , with notable contributions to robust Bayesianism, causal discovery algorithms, and the philosophical foundations of statistical methods. Recent articles address computational philosophy, measurement theory, and severe testing frameworks. Mayo-Wilson has taught courses including Introduction to Logic, Seminar in Epistemology, and Statistics and Philosophy of Voting. He is affiliated with the Center for Statistics in the Social Sciences at the University of Washington. Scientific Awards: Best Paper Award, 2014

• Macroeconomics
• Monetary Economics
• International Economics
• +۳ مورد دیگر

Matthias Meier is a Professor of Economics at the University of Mannheim, holding a Heisenberg Professorship funded by the German Research Foundation (DFG). He is a project leader (C05) in the Collaborative Research Center (CRC) TR 224 at Universität Bonn, focusing on quantitative macroeconomic analysis. Research Interests : Macroeconomics, Monetary Economics, International Economics, and Econometrics. His work bridges theoretical models with empirical evidence, particularly in monetary policy transmission, firm heterogeneity, supply chain disruptions, and labor market dynamics. Recent Research Trends : His articles explore the interplay between monetary policy and aggregate productivity, the labor market implications of ECB policy, and the economic impact of global supply chain shocks during the pandemic. Methodological contributions include novel approaches to set-identified structural vector autoregressions (SVARs). Lamfalussy Fellowship, European Central Bank Best Paper Award in Applied Economics, Econometric Society European Meeting Best Student Paper Award, Real Options Group Conference Best Paper Award (runner-up), Spring Meeting of Young Economists Best Paper Award (runner-up), European Workshop on Efficiency and Productivity Analysis Workshop Leadership : Organizer of annual workshops on firm heterogeneity and macroeconomics since 2018, including events in Bonn and Mannheim. These workshops focus on intersections between firm-level dynamics and macroeconomic outcomes.

David Bickelمشاهده پروفایل

استاد

• Statistics
• Bioinformatics
• Genomics
• +۷ مورد دیگر

David Bickel is a Professor in the School of Information, Library, and Research Sciences at the University of North Carolina at Greensboro. With a PhD from the University of North Texas and undergraduate studies at Baylor University, he has established himself as a leading researcher in statistical methodology for genomic and biological data analysis. Dr. Bickel's research focuses on empirical Bayes methods, false discovery rates, uncertainty quantification, and information theory. His work bridges theoretical statistics with practical applications in genomics, molecular evolution, metabolomics, and proteomics. He has developed several influential software packages including LFDRenrich, LFDRhat, and LFDREmpiricalBayes that implement his methodological contributions. His publications reveal a consistent focus on improving statistical inference in high-dimensional biological data, with particular attention to proper calibration of statistical evidence and uncertainty quantification. His work helps researchers navigate the challenges of multiple testing that are ubiquitous in modern genomics. Dr. Bickel has authored three major books: Phylogenetic Trees and Molecular Evolution: A Hands-on Introduction with Uncertainty Quantification , Genomics Data Analysis: False Discovery Rates and Empirical Bayes Methods , and a book on Bayes-frequentist statistics that explores integrating these complementary approaches. His research has practical impact across biological sciences, providing methodological tools that help scientists extract reliable insights from complex data while properly accounting for statistical uncertainty. His software implementations make advanced statistical methods accessible to domain scientists who may not have deep statistical expertise. Dr. Bickel maintains an active research program as evidenced by recent publications and software updates, continuing to contribute to the evolving field of statistical genomics and bioinformatics.

Robert Suchtingمشاهده پروفایل

استادیار

• Biostatistics
• Data Science
• Machine Learning
• +۳ مورد دیگر

Robert Suchting, PhD, is an Assistant Professor in the Department of Psychiatry and Behavioral Sciences at the University of Texas Health Science Center at Houston (UTHealth Houston). He is affiliated with the McGovern Medical School and conducts research at the Center for Neurobehavioral Research on Addiction (CNRA). His expertise lies in applying quantitative methods to psychiatric research, including machine learning, biostatistics, and data science. Dr. Suchting's research interests span: Substance use disorders Mood disorders (e.g., depression) Predictive modeling using clinical datasets Advanced statistical approaches (Bayesian/Frequentist) His recent publications (2021–2025) focus on therapeutic targets for COVID-19 lung injury, smoking cessation pharmacotherapy, emotion regulation neurophysiology, and addiction treatment efficacy. Trends indicate strong emphasis on translational clinical research with machine learning applications.

• Nonparametric Statistics
• Bootstrap Methods
• Smoothing Methods
• +۵ مورد دیگر

Alan M. Polansky is an Associate Professor in the Department of Statistics and Actuarial Science at Northern Illinois University (NIU), where he has maintained a continuous faculty appointment since 1995. His research program centers on advancing nonparametric statistical methodologies with significant contributions to bootstrap theory, smoothing techniques, and observed confidence levels. His academic credentials include a Ph.D. from Southern Methodist University and M.S./B.S. degrees from the University of Texas at San Antonio. As an Honors Faculty Fellow (2022-2023), he developed and taught an innovative seminar on Data and Social Justice for the University Honors Program. Polansky's research spans several interconnected domains: Foundational work on bootstrap methodology and confidence interval construction Development of observed confidence levels as an alternative to multiple comparison techniques Asymptotic theory for statistical limit theorems Emerging research on network data analysis and Bayesian inference for stochastic processes His 25+ year publication record shows consistent evolution from core nonparametric methods toward contemporary applications, with recent work focusing on network statistics and Bayesian approaches while maintaining theoretical rigor. Publications appear in premier journals including the Journal of the Royal Statistical Society, Technometrics, and Computational Statistics and Data Analysis. Key recognitions include: Honors Faculty Fellowship (2022-2023) Authorship of two influential monographs: 'Observed Confidence Levels' (2007) and 'Introduction to Statistical Limit Theory' (2011) As an educator, Professor Polansky maintains regular office hours in DuSable Hall and has advised students across statistics and actuarial science programs. His research demonstrates sustained methodological innovation with practical applications in insurance, manufacturing, and emerging network-based domains, reflecting both deep theoretical expertise and commitment to real-world problem solving.