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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 9/27/2018 3:30PM - 4:30PM
Hyun Jung (HJ) Park, University of Pittsburgh
Public Health Auditorium (G23)

Hyun Jung (HJ) Park, Department of Human Genetics, University of Pittsburgh, will present, “Insights into RNA Biology from Statistical Modeling for CancerTherapeutics”.
Thu 10/4/2018 3:30PM - 4:30PM
Peter X.K. Song, University of Michigan
Public Health Auditorium (G23)

Peter X.K. Song, Department of Biostatistics, University of Michigan, will present, “HASS: Hybrid Algorithm for Subgroup Search via ADMM and EM Algorithms”.
Thu 10/25/2018 3:30PM - 4:30PM
Dana L. Tudorascu, University of Pittsburgh
Public Health Auditorium (G23)

Thu 11/1/2018 3:30PM - 4:30PM
Jingyi (Jessica) Li, University of California, Los Angeles
Public Health Auditorium (G23)

Thu 11/8/2018 3:30PM - 4:30PM
Hongzhe Li (Lee), University of Pennsylvania
Public Health Auditorium (G23)

Thu 11/29/2018 3:30PM - 4:30PM
Dulal K. Bhaumik, University of Illinois at Chicago
Public Health Auditorium (G23)

Previous Biostats Seminars

Biostatistics Seminar Series

Biostatistics Seminar: Shuangge Steven Ma, PhD, Yale School of Public Health

Thursday 9/14 3:30PM - 5:00PM
Public Health Auditorium (G23)

Biostatistics Seminar guest speaker, Shuangge Steven Ma, PhD, Yale School of Public Health, will present, "Integrating multidimensional omics data for cancer prognosis."

Prognosis is of essential interest in cancer research. Multiple types of omics measurements – including mRNA gene expression, methylation, copy number variation, SNP, and others – have been implicated in cancer prognosis. The analysis of multidimensional omics data is challenging because of the high data dimensionality and, more importantly, because of the interconnections between different units of the same type of measurement and between different types of omics measurements. In our study, we have developed novel regularization based methods, effectively integrated multidimensional data, and constructed prognosis models. It is shown that integrating multidimensional data can lead to biological discoveries missed by the analysis of one dimensional data and superior prognosis models.

Last Updated On Tuesday, August 29, 2017 by Kapko, Bernadette E
Created On Wednesday, August 09, 2017


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

Bernadette Kapko

© 2018 by University of Pittsburgh Graduate School of Public Health

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