Unless otherwise noted, seminars are held in PGH 646A (Philip Guthrie Hoffman Hall).
Time: TBA
Hengrui Luo
Assistant Professor, Department of Statistics, Rice University
Title: Modern Statistical Methods for Tensors: Compression, Communication, and Completion
Abstract:
The talk will develop a common statistical perspective on several recent works on tensor-structured problems, with an emphasis on how preserving multiway structure leads to phenomena and methods that are fundamentally different from treating the data as a vector. Modern applications, including compression and distributed learning for large language model weight tensors, will also be discussed.
Time: 4-5 PM
Noel Cressie
Distinguished Professor of Statistics, School of Mathematics and Applied Statistics, University of Wollongong, Australia
Title: TBA
Abstract: TBA
Time: TBA
Debangan Dey
Assistant Professor, Department of Statistics, Texas A&M University
Title: TBA
Abstract: TBA
Time: TBA
Cindy Zhang
Assistant Professor, Department of Statistics, Rice University
Title: TBA
Abstract: TBA
Time: TBA
Gao Wang
Assistant Professor of Neurological Sciences and Biostatistics, Columbia University Vagelos College of Physicians and Surgeons
Title: Extending SuSiE for Fine-Mapping Molecular QTLs at Scale with Applications in Alzheimer's Disease Genetics
Abstract:
SuSiE (Sum of Single Effects) is a Bayesian fine-mapping framework that has been broadly adopted and extended by many research groups for polygenic prediction, cross-ancestry GWAS and eQTL analysis, causal gene identification in transcriptome-wide association, and other related applications. In this talk, I present the core ideas of a series of methodological extensions motivated by our own analysis of multi-omic data in aging brains through the FunGen-xQTL project within the Alzheimer's Disease Sequencing Project Functional Genomics Consortium. These include fine-mapping of epigenomic QTL via function-on-scalar regression (fSuSiE), joint modeling of chromatin accessibility and RNA programs (mfSuSiE), multi-gene eQTL fine-mapping (an mvSuSiE extension), and a multi-trait colocalization method inspired by SuSiE (ColocBoost). We also address practical challenges including fine-mapping under highly polygenic architectures (SuSiE-ASH) and diagnosing LD and z-score mismatch in GWAS summary statistics (SuSiE-RSS-QC). Applying these methods to integrate chromatin, gene expression, splicing, protein abundance, and metabolomic data across aging cohorts, we characterize regulatory cascades at AD-associated loci with cell-type resolution and identify mechanisms including oligodendrocyte excitotoxicity, primary cilia dysfunction, and mitophagy suppression. Finally, we discuss intrinsic limitations of fine-mapping that persist beyond statistical methodology and their implications for study design and functional follow-up.