coe-staff: Feedback Request for Katerina Marcoulides, Clinical Trials Methodology Candidate

[Jennifer A McGovney]

The Clinical Trials Methodology search committee would like to thank everyone who took time out of their busy schedules to meet with candidate Katerina Marcoulides, and/or attend her colloquium.


If you were unable to attend her colloquium, you can view the recording here: https://youtu.be/cqslddXTil8

Please provide your feedback on the candidate by completing our survey here: https://oregon.qualtrics.com/SE/?SID=SV_9BsJUA0R7lMOKnH

The survey will stay open until January 25. Katerina's CV is attached to this email. Thank you again for your participation in this important process.


From: coe-staff-bounces at lists.uoregon.edu [mailto:coe-staff-bounces at lists.uoregon.edu] On Behalf Of Denise E McKenney
Sent: Wednesday, January 11, 2017 10:02 AM
To: coe-staff at lists.uoregon.edu
Subject: coe-staff: Colloquium - Katerina Marcoulides, Clinical Trials Methodology Tenure Track Faculty Candidate

Please join us for a colloquium presented by one of the candidates for the College of Education Clinical Trials Methodology tenure-track faculty position.

Katerina Marcoulides
A Bayesian Synthesis Approach to Data Fusion using Data Dependent Priors

Colloquium and Q&A
Tuesday, January 17, 2017 - 11:00 AM-12:30 PM
Lokey ED 115
Lunch provided at 10:45 AM

The process of combining data is one in which information from disjoint datasets sharing at least a number of common variables is merged. This process is commonly referred to as data fusion, with the main objective of creating a new dataset permitting more flexible analyses than the separate analysis of each individual dataset. Many data fusion methods have been proposed in the literature, although most utilize the frequentist framework. This study investigates an approach called Bayesian Synthesis in which information obtained from one dataset acts as priors for the next analysis. The process continues sequentially until a single fused posterior distribution is created using all available data. These informative data dependent priors provide an extra source of information that may aid in the accuracy of estimation. To examine its performance, Bayesian Synthesis results of data simulated under several conditions with known population values are evaluated. In summary, the results illustrate that Bayesian Synthesis with data driven priors is a highly effective approach, provided that the sample sizes for the fused data are large enough to supply unbiased estimates. To date, data integration methods have been shown to be very valuable in helping researchers address important questions with more power and accuracy than any single study. Bayesian Synthesis is another beneficial approach that can effectively be used to conduct data fusion activities and enhance the validity of conclusions obtained from the merging of data from different studies.

Katerina Marcoulides is currently completing her PhD in Quantitative Psychology at Arizona State University, with an expected graduation date of May 2017.  She received her Master's degree in Quantitative Psychology from the University of California, Davis and her Bachelor's degree in Psychology with a minor in Education from the University of California, Santa Barbara. She has previously interned as a research associate at Yale University, VU University in Amsterdam, and Houghton Mifflin Harcourt. Her research interests focus on Bayesian methodology, data mining, data fusion, longitudinal data analysis, item response theory, and structural equation modeling, particularly as they relate to the study of developmental processes in educational and psychological research. To date, her research contributions have received travel and best paper awards from the Psychometric Society, the Society of Multivariate and Experimental Psychology (SMEP), and the National Communication Association.  She is currently serving as a research associate on a federally funded research grant.

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