Ciprian Crainiceanu is a professor in the Department of Biostatistics at Johns Hopkins University. A Cornell Alumni (MS '02, PhD '03), Crainiceanu's research is centered around Statistical methods for new technologies used in public health and medical studies. These technologies provide new types of data that are increasing both in size and complexity. He is interested in developing analytic tools that are tailored to specific applications, address the particular subtleties of the problem, and then find the common thread that eventually becomes Statistical methodology. His current scientific research interest centers around sleep research (EEG, polysomnograms), wearable computing (accelerometers, heart monitors), and multimodality brain imaging (SPECT, MRI, CT) with applications to Alzheimer, Multiple Sclerosis, traumatic brain injury, and cancer. His statistical expertise centers around inferential methods for ultra high dimensional data, mixed effects modeling, Bayesian inference, and smoothing.
Title: Biostatistical Methods for Wearable and Implantable Technology
Abstract: 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 ecosystem 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.