Lu Mao, PhD, Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison.
Chris McKennan, PhD, Department of Statistics, University of Pittsburgh.
Caroline P Groth, PhD, Department of Biostatistics, School of Public Health, West Virginia University.
There will be a Q&A session with students from the Biostatistics and Statistics departments who have completed internships.
Meetings of the Eastern North American Region of the International Biometric Society (a.k.a. "ENAR meetings") are held in late March or early April each year and reflect the broad interests of the Society, including both quantitative techniques and application areas. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations.
The Joint Statistical Meetings, known simply as "JSM", is the largest gathering of statisticians held annually in North American. Faculty and student presenters from the Department of Biostatistics regularly participate giving invited talks, contributed talks, and poster presentations. Our students often receive top awards and participate in the affiliated career marketplace at the event.
Biostatistics guest speaker, Ciprian Crainiceanu, Johns Hopkins University, will present, " Biostatistical Methods for Wearable and Implantable Technology."
Wearable and Implantable Technology (WIT) is rapidly changing the Biostatistics data analytic landscape due to their reduced bias and measurement error as well as to the sheer size and complexity of the signals. In this talk I will review some of the most used and useful sensors in Health Sciences and the ever expanding WIT analytic environment. I will describe the use of WIT sensors including accelerometers, heart monitors, glucose monitors and their combination with ecological momentary assessment (EMA). This rapidly expanding data eco-system is characterized by multivariate densely sampled time series with complex and highly non-stationary structures. I will introduce an array of scientific problems that can be answered using WIT and I will describe methods designed to analyze the WIT data from the micro- (sub-second-level) to the macro-scale (minute-, hour- or day-level) data.
Last Updated On Wednesday, March 28, 2018 by Temp, GSPH Marketing & Development
Created On Friday, January 05, 2018