Dan Burke

Dan Burke began their work experience in 2004 as a Co-op Student at Ross Video, where they added new features and fixed bugs on digital video switchers. Dan then worked as a Research Assistant at CEA in 2005, conducting experiments, analyzing data, and preparing papers for publishing. From 2007 to 2013, they worked as a TA / RA at Carleton University, focusing on writing, researching, and implementing meshless solvers for partial differential equations. Since 2012, they have been working at Lumentum as an R&D Senior Principal Engineer, where they have been involved in various tasks such as algorithms development, simulations, data analysis, and deep CNN training. Their contributions include developing a calibration method that reduced manufacturing calibration time and implementing a deep CNN for use on the manufacturing line.

Dan Burke's education history includes a Ph.D. in Electrical and Computer Engineering from Carleton University, obtained from 2007 to 2013. Prior to that, they completed a Bachelor of Engineering in Engineering Physics from Carleton University, from 2002 to 2007. Dan has also pursued various nanodegree programs from Udacity, including Artificial Intelligence, Deep Learning Foundations, and Machine Learning, all completed in different years. Additionally, they hold a certification in "Prepare To Teach" from Carleton University, although the specific obtained month and year are not provided.