Mr. David Ledbetter has an extensive and deep understanding of decision theory. He has experience implementing various decision engines, including convolutional neural networks, random forests, extra trees, and linear discrimination analysis. His particular area of focus is in performance estimation, where he has been able to accurately predict performance on new data in nonstationary, real-world scenarios, including detecting circulating tumor cells in blood, automatic target recognition utilizing CNNs from satellite imagery, make/model car classification for the Los Angeles Police Department, and acoustic whale call detection from underwater sonobuoys. Recently, he has been developing a Recurrent Neural Network to generate acuity scores and personalized treatment recommendations to optimize patient outcomes using electronic medical records from 15 years of data collected from the Children’s Hospital Los Angeles Pediatric Intensive Care Unit.

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