جست‌وجوی استاد و پژوهشگر

Veera Baladandayuthapaniمشاهده پروفایل

استاد

• High-dimensional data modeling
• Bayesian inference
• Functional data analysis
• +۱۱ مورد دیگر

Dr. Veera Baladandayuthapani is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He serves as the Director of the Cancer Data Science Shared Resource and previously held positions as Professor and Institute Faculty Scholar at the University of Texas MD Anderson Cancer Center. His research focuses on high-dimensional data modeling and Bayesian inference, with applications in genomics, epigenomics, transcriptomics, proteomics, and cancer imaging. PhD, Statistics, Texas AandM University, 2005 MA, Statistics, University of Rochester, 2000 BS, Mathematics, Indian Institute of Technology (IIT) Kharagpur, 1999 His research develops Bayesian probabilistic models and machine learning methods for biomedical big data, emphasizing integrative models combining multi-omics and imaging data for biomarker discovery and clinical prediction. Current projects include Bayesian hierarchical models, graphical models, and scalable software tools for precision medicine. Recent publications span Bayesian graphical regression, proteogenomics, and spatial cellular interaction modeling. He holds multiple NIH and NSF grants for advancing biostatistical methods in oncology. Fellow of the American Statistical Association Elected member of the International Statistical Institute He has advised numerous PhD students and postdoctoral researchers, with alumni positions at institutions like Emory University, University of Warwick, and Pfizer. His BayesRx research group actively recruits post-doctoral fellows.

Mingyao Liمشاهده پروفایل

دانشیار

• Bioinformatics
• Biostatistics
• Genetics
• +۲ مورد دیگر

Mingyao Li, PhD, is an Associate Professor of Biostatistics whose scholarship bridges high-dimensional statistics with modern genomics and spatial biology. Her work is recognised for advancing computational methods that dissect complex tissue architecture and disease heterogeneity at single-cell resolution. Research Interests Bioinformatics and computational biology Statistical methods for high-dimensional genomic data Single-cell and spatial multi-omics integration Genetics and genomics of cardiovascular and cancer systems Systems biology approaches to disease mechanisms Across more than one hundred peer-reviewed publications, Li’s research exhibits a clear trajectory from developing rigorous statistical frameworks—such as hypothesis testing in stochastic block models—to large-scale applications in human disease. A dominant theme is the fusion of spatial transcriptomics, single-cell multi-omics and AI-driven image analysis to uncover cell-type heterogeneity, lineage relationships and microenvironmental crosstalk in atherosclerosis, lung adenocarcinoma, pancreatic cancer and neurodegenerative disorders. Scientific Awards & Recognition While specific awards are not listed in the provided text, the consistent appearance of Dr Li as senior or corresponding author on high-impact papers in Nature family journals and the development of widely-used computational tools (e.g., MISO, iSCALE) attest to significant scholarly recognition. Research Support & Teams Granting agencies and collaborative networks are not explicitly detailed, but the scale and scope of the projects—from whole-organ spatial atlases to primate genome-editing studies—imply substantial multi-institutional funding and interdisciplinary teams integrating biostatisticians, wet-lab scientists and clinician-scientists.

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.