Directory Calendar News Careers Alumni Giving

Biostatistics Seminars

Department of Biostatistics Seminar Series


The Department of Biostatistics presents a regular speaker series each semester, generally on Thursday afternoon each week. Diverse experts lecture on their work in biostatistics.

Upcoming Biostats Seminars

Thu 3/1/2018 3:30PM - 4:30PM
Inference and Variable Selection with Random Forests - Lucas Mentch
Public Health Auditorium (G23)

Biostatistics guest speaker, Lucas Mentch, University of Pittsburgh, Statistics, will present, "Inference and Variable Selection with Random Forests."
Thu 4/5/2018 3:30PM - 4:30PM
Seonjoo Lee, Columbia University
Public Health Auditorium (G23)

Biostatistics guest speaker, Seonjoo Lee, Columbia University, will present.
Thu 4/12/2018 3:30PM - 4:30PM
Ciprian Crainiceanu, Johns Hopkins University
Public Health Auditorium (G23)

Biostatistics guest speaker, Ciprian Crainiceanu, Johns Hopkins University, will present.
Thu 4/19/2018 3:30PM - 4:30PM
Wensheng Guo, University of Pennsylvania
Public Health Auditorium (G23)

Biostatistics guest speaker, Wensheng Guo, University of Pennsylvania, will present.

Previous Biostats Seminars

Biostatistics Seminar Series

Zhixiang Lin, Stanford University

Tuesday 1/23 3:00PM - 4:00PM
Public Health A522

Biostatistics guest speaker, Zhixiang Lin, Stanford University, will present, "Statistical methods for high-throughput genomic data." 

In the first part of the talk, a dimension reduction method will be introduced where we extend Principal Component Analysis to propose AC-PCA for simultaneous dimension reduction and Adjustment for Confounding variation. We show that AC-PCA can adjust for variations across individual donors present in a human brain dataset. For gene selection purposes, we extend AC-PCA with sparsity constraints, and propose and implement an efficient algorithm. The second part of the talk will be focused on clustering methods in single cell genomics. In single cell genomics, it is technically challenging to obtain chromatin accessibility and gene expression data for the same cell. We have developed a computational approach to this problem, where a model-based clustering method is proposed to match cell sub-populations in these two data types. We also demonstrate that using one data type can guide clustering of the other data type. Our proposed Bayesian model accounts for the stochasticity due to biological and technical effects. Last, methodologies motivated by spatial temporal modeling of gene expression dynamics during human brain development will be briefly discussed.

 

Last Updated On Friday, January 12, 2018 by Kapko, Bernadette E
Created On Friday, January 12, 2018

Contact

For information on seminars and events in the department, contact:

Bernadette Kapko
bkapko@pitt.edu
412-624-3022

© 2018 by University of Pittsburgh Graduate School of Public Health

Login  |  Sitemap