David Ledbetter has an extensive and deep understanding of decision theory. He has experience implementing various decision engines, including convolutional neural networks, recurrent neural networks, random forests, extra trees, and linear discrimination analysis. His particular area of focus is in performance estimation, where he has demonstrated a tremendous ability to accurately predict performance on new data in nonstationary, real-world scenarios. David has worked on a number of real-world detection projects, 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 using CNNs, and acoustic right whale call detection from underwater sonobuoys. Recently, David has been developing RNNs to generate personalized treatment recommendations to optimize patient outcomes using unstructured electronic medical records from 10 years of data.