Jeffrey A. Bilmes

Jeffrey A. Bilmes is a Professor in the Department of Electrical and Computer Engineering at the University of Washington, Seattle, with adjunct roles in Computer Science & Engineering and Linguistics. He founded the MELODI Lab, focusing on machine learning, optimization, and data interpretation. Bilmes holds a Ph.D. from UC Berkeley and a Master's from MIT. His research spans graphical models, speech recognition, bioinformatics, and submodular optimization, with notable contributions like the GMTK toolkit and pioneering work in submodularity. He has received prestigious awards including the NSF Career Award (2001), NAE Gilbreth Lectureship (2008), and best paper awards at ICML/NIPS (2013). Bilmes has held leadership roles in UAI and NeurIPS conferences, and his work bridges theoretical foundations with practical applications in computational systems and human-computer interaction.

Education: Ph.D., Computer Science, UC Berkeley; M.S., MIT. Research Interests: Machine learning, temporal graphical models, submodularity, speech interfaces, and algorithmic optimization. His work on submodular functions has been recognized with multiple awards, including the 25-year ICS award for his 1997 matrix optimization research. He actively contributes to academic service through conference organization and editorial roles at JMLR. The MELODI Lab develops cutting-edge tools like GMTK, PhiPAC, and Vocal Joystick for real-world applications.

Recent Activities: Invited lectures at Yale (2016), Harvard (2015), and IIT Bombay. Co-organized NIPS workshops on discrete optimization (2013–2016). Authored influential papers on submodular optimization, semi-supervised learning, and parallel computing. Current research emphasizes submodular applications in large-scale data management and distributed systems.