Bilmes is a professor in the Department of Electrical Engineering and an adjunct professor in Computer Science & Engineering and the Department of Linguistics. He is the founder of the MELODI (MachinE Learning for Optimization and Data Interpretation) lab. His primary interests lie in statistical modeling, particularly graphical model approaches, and signal processing for pattern classification, speech recognition, language processing, bioinformatics, machine learning, submodularity in combinatorial optimization and machine learning, active and semi-supervised learning, and audio/music processing.
Bilmes expects to pursue activities that relate to Themes 3 (Services) and 4 (Tools), specifically developing methods for submodular summarization of static TerraSwarm data and new ways to summarize streaming data using submodular functions in a way that has bounded memory resources. Additionally, his research will address a fundamental question about TerraSwarm data-- that is, can statistical predictions, using modern machine learning methods, be made more cost effective using the “right” small data subset?
Emily B. Fox is the Amazon Assistant Professor of Machine Learning in the Department of Statistics. Her interests focus on Bayesc Bayesian approaches to time-series and longitudinal data analysis, with an emphasis on extensions to high-dimensional data. She plans to augment the capabilities of the TerraSwarm project by developing an open source software that people can use to plug in swarms of sensors of various types.
According to Fox: "From a specified swarm of data streams, the system will parse the heterogenous recordings into event states as well as maintain compact feature-descriptors for each sensor in the swarm. To aid in interpretability, the output of the system will provide alerts when new event states are detected."
Jeff Bilmes: http://melodi.ee.washington.edu/~bilmes
Emily Fox: https://www.stat.washington.edu/~ebfox