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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 9/20/2018 3:30PM - 4:30PM
Biostatistics at the FDA - Jessica (Jeongsook) Kim, FDA
Public Health Auditorium (G23)

Jessica Kim, Lead Mathematical Statistician, Food and Drug Administration, will present, “Biostatistics at the FDA”.
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

Lu Tang, University of Michigan on "Fusion Learning in Integrative Data Analysis"

Tuesday 1/16 2:30PM - 3:30PM

Biostatistics guest speaker, Lu Tang, University of Michigan, will present, "Fusion Learning in Integrative Data Analysis."

Pooling data sets from multiple studies is often undertaken in practice to achieve larger sample sizes and greater statistical power. A major analytic challenge arising from data integration pertains to data heterogeneity in terms of underlying study population, study design, data collection or data availability. Ignoring such heterogeneity in integrative data analysis may result in biased estimation and misleading inference. In this talk, I will present new machine learning methodologies to address the challenge. 1) The first part of the talk is motivated from the ELEMENT multiple cohorts of Mexican adolescents to study the association of metabolomics outcomes with in utero environmental exposure to toxic chemicals (e.g. PBA and phthalates). I will introduce new data integration analytics based on the fused LASSO that allow learning similarities and differences of covariate effects across cohorts in the setting of generalized linear models. 2) The second part of my talk is motivated by a prospective longitudinal cohort study examining risk predictors of suicidal ideation in US medical interns. To improve statistical power in the utility of pattern mixture models for non-ignorable missing data, I will introduce a fusion learning method to identify and merge similar missing data patterns under the framework of generalized estimating equations (GEE) in population-average models for longitudinal data. I will also discuss ongoing and future projects, including scalability issues in the wake of recent movements toward distributed computing.

Last Updated On Friday, January 12, 2018 by Borkowski, Matthew Gerard
Created On Friday, January 05, 2018


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

Bernadette Kapko

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