Jeffrey A. Bilmes is a professor in the Department of Electrical Engineering at the University of Washington, Seattle, where he is also 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) at UW. He received his PhD from the Computer Science Division of the department of Electrical Engineering and Computer Science at UC Berkeley. Bilmes is a 2001 NSF Career award winner, a 2002 CRA Digital Government Fellow, a 2008 NAE Gilbreth Lectureship award recipient, and a 2012/2013 ISCA Distinguished Lecturer. Bilmes was a UAI (Conference on Uncertainty in Artificial Intelligence) program chair (2009) and general chair (2010), a NIPS (Neural Information Processing Systems) workshop chair (2011) and tutorials chair (2014), and is currently an action editor for JMLR (Journal of Machine Learning Research). He received a best paper award at ICML in 2013, a best paper award at NIPS in 2013, and a most influential paper in 25 years award from the International Conference on Supercomputing in 2014. Bilmes's primary interests lie in machine learning and data science, and he specializes in submodular and discrete optimization for machine learning, and in graphical models (primarily over time series). His other interests are in signal processing, information theory, speech recognition, language processing, bioinformatics, active and semi-supervised learning, audio/music processing, speech-based human-computer interfaces, and high-performance computing. Bilmes has authored the graphical models toolkit (GMTK), a dynamic graphical-model based software system widely used in speech, language, bioinformatics, and human-activity recognition.