The Knowledge and Information Discovery (KID) lab focuses on problems at the intersection of machine learning and signal processing. Graduate and undergraduate students work on problems ranging from speech recognition to electricity demand forecasting. We study machine learning techniques, including deep neural networks, decision trees and Bayesian estimation, and signal processing approaches such as phase space reconstruction. Current projects include forecasting natural gas pipeline alarms and diagnosing vestibular disorders.
Dizziness and imbalance are among the most common conditions for which patients seek medical evaluation, adults are disabled, and the elderly are injured. Unfortunately, misdiagnosis of vestibular disorders is common; some studies showing misdiagnosis rates of 74-81% in emergency departments and 85% in primary care offices. Such misdiagnosis is costly, inefficient and causes patient harm and dissatisfaction. At root of this misdiagnosis is a profound knowledge gap among non-vestibular specialty providers as to what patient information is relevant, critical, and diagnostic. Complete histories from vestibular patients can predict specific clinical conditions, risk of falls and injury, appropriate diagnostic tests, and cost-effective interventions and treatment. The ultimate goal of our work is to improve the accuracy and efficiency of diagnosing vestibular conditions by acquiring complete and accurate histories directly from the patient.
Natural gas pipeline operators transport natural gas from the well head to downstream customers. The downstream customers require that gas quality metrics for energy content, CO2, H2O, and H2S are met. If these metrics are not met, the pipeline operator is shut-in and must flare off the poor-quality gas. To avoid these gas quality issues, we predict when gas quality will go out of specification.
Dr. Richard Povinelli
Associate Professor of Electrical and Computer Engineering