جست‌وجوی استاد و پژوهشگر

Claudia Solís-Lemusمشاهده پروفایل

استادیار

• Statistical Modeling
• Phylogenetics
• Microbiome Analysis
• +۳ مورد دیگر

Claudia Solís-Lemus is an Assistant Professor in the Department of Plant Pathology at the University of Wisconsin-Madison, where she develops statistical and machine learning methods to solve complex biological problems. Her work bridges computational statistics with plant pathology and evolutionary biology, focusing on network-based approaches to genomic and microbiome data. Educational background: PhD in Statistics, University of Wisconsin–Madison Her research centers on phylogenetic network inference , microbiome analysis , and high-dimensional statistical modeling . She creates open-source tools like CMiNet and MiNAA to empower biologists with robust network analysis capabilities. Her lab tackles challenges in biodiversity research, agricultural disease prediction, and microbial ecology through innovative computational frameworks that handle massive biological datasets. Analysis of her 2024-2025 publications reveals a cohesive focus on scalable network inference methods across phylogenetics and microbiome studies. She integrates Bayesian statistics, regularization techniques (e.g., spike-and-slab LASSO), and high-performance computing to address data complexity. Her work consistently emphasizes practical software implementation (R packages, Shiny apps, Julia tools) for real-world biological applications including potato disease prediction, hornwort evolution, and freshwater ecosystem dynamics. Scientific recognition: NSF CAREER Award (2022) for "Towards Scalable and Robust Inference of Phylogenetic Networks" Dr. Solís-Lemus leads an interdisciplinary research group at the Wisconsin Institute for Discovery, securing competitive grants to advance phylogenetic network methodology. Her CAREER project combines algorithmic innovation with educational outreach to train next-generation computational biologists. Current efforts focus on improving network inference for polyploid genomes and developing consensus methods for microbiome data integration across diverse environmental conditions. The Solís-Lemus Lab operates within UW-Madison's Wisconsin Institute for Discovery ecosystem, fostering collaborations between statisticians, computer scientists, and biologists. Her team actively develops user-friendly software to lower computational barriers for life scientists studying evolutionary processes and microbial communities.

• Algorithms
• Regression Analysis
• Machine Learning
• +۳ مورد دیگر

Slav Angelov is a Senior Lecturer in the Department of Informatics at New Bulgarian University (NBU), serving since September 2017. He teaches core courses including Applied Mathematics for Economists, Quantitative Methods in Management, Probability and Statistics, and Knowledge Extraction from Data, while contributing to NBU's joint program with the University of Sheffield. His academic credentials include: Master's degree with honors in Optimization from Sofia University "St. Kliment Ohridski" (2014) PhD in Computer Science and Informatics from NBU (2019), dissertation: "Computer Simulation and Assessment of Corporate Risks" Dr. Angelov's research focuses on statistical modeling and algorithmic development, with expertise in regression analysis (particularly multicollinearity solutions and cross-validation techniques), machine learning applications, and knowledge extraction systems. His work bridges theoretical statistics with practical implementations in energy forecasting and bioinformatics. His publication record reveals a consistent research trajectory centered on advancing regression methodologies across diverse domains. Key contributions include modified regression techniques for risk assessment and educational frameworks for data mining pedagogy, demonstrating dual expertise in methodological innovation and curriculum development. He has been an integral member of NBU's Center for Risk Management, leading analytical work on critical national infrastructure projects including the Belene Nuclear Power Plant, Balkan gas hub development, and long-term electricity consumption forecasting for Bulgaria through 2030.

• Statistical methods for high-dimensional data
• Cancer genomics
• Biomarker identification
• +۹ مورد دیگر

Dr. Yuan Huang is an Assistant Professor in the Department of Biostatistics at Yale School of Public Health. She holds multiple affiliations across Yale University, including the Yale Cancer Center, Yale Center for Analytical Sciences (YCAS), Cancer Prevention and Control program, and the Center for Brain & Mind Health. Her research focuses on developing statistical methods for high-dimensional data, addressing challenges in cancer genomics such as low reproducibility, nonlinearity, and heterogeneity. Dr. Huang specializes in biomarker identification, large-scale network structure estimation, and gene-environment analysis, with recent expansion into neurodegenerative diseases including Alzheimer's, Huntington's, and Parkinson's diseases. Dr. Huang's publication record demonstrates expertise in advanced statistical methodologies including Bayesian mixture models, precision matrix estimation, functional data analysis, and stagewise algorithms for nonconvex optimization. Her work consistently bridges theoretical statistical development with practical biomedical applications. As an active collaborator, Dr. Huang works on clinical trials, genetics, epidemiology, and other biomedical research projects, applying her methodological expertise to solve complex data analysis challenges in these fields.

• 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.

• Mathematical modeling
• Linear programming
• Non-linear programming
• +۶ مورد دیگر

Dr. Przemysław Kowalik is an Assistant Professor at the Department of Quantitative Methods in Management within the Faculty of Management at Lublin University of Technology. He has been employed at the university since 1993, initially working in the Department of Operational Research (1993-2001), then in the Department of Enterprise Organization (2002-2003), before moving to his current department in 2003. Dr. Kowalik's research focuses on mathematical modeling of decision-making situations through linear and non-linear programming, implementation of optimization and econometric models in spreadsheets, mathematical aspects of consumer rights infringement (particularly regarding railway fares and financial products), and investment portfolio theory. His work often bridges theoretical mathematical concepts with practical regulatory and business applications. His recent publications (2011-2018) demonstrate a consistent focus on optimization problems, railway tariff analysis, and econometric modeling. The research shows a progression from theoretical mathematical methods toward increasingly applied work in transportation economics and consumer protection, particularly examining how mathematical models can identify regulatory non-compliance in railway pricing systems. Dr. Kowalik teaches Operational Research and Mathematics courses, including for Erasmus+ international students in English. His educational background includes mathematics studies at Maria Curie-Skłodowska University and a PhD in Management Sciences from AGH University of Science and Technology in Krakow. He maintains regular consultation hours for students every Wednesday (10:00-12:00) and provides additional learning materials through his dedicated website, emphasizing practical applications of mathematical methods in business contexts with spreadsheet implementations that make complex techniques accessible to students.