Abstract - LED lighting is rapidly replacing the incandescent and fluorescent lighting. As LEDs become more efficient and more affordable, they will accelerate the demise of all other forms of lighting. Some interesting questions one can ask are: How much more efficient are they? And, why is that so? Why do they cost so much, relative to an incandescent light? How does an LED work? What kind of power do they require? What is the simplest way to make an LED light? We look at three common applications in automotive, home, and commercial and how practical LED fixtures are designed and what we can expect in the future for LED lighting.

Mr. Benny Smith grew up in Texas and graduated from Texas A&M University with a BSEE degree in 1966. He spent two years in the U.S. Army as a Second Lieutenant (Armor). Benny attained a Teaching Certificate in elementary education in 1971 and taught junior-high math and science for one year. He then pursue graduate work and received his MSEE from Arizona State University in 1976. He worked as a design engineer at Motorola Semiconductor in Phoenix and moved to Sonoma County in 1976 to work at HP’s Santa Rosa Division. Benny retired from Agilent in 2004 and built his first commercial LED light fixture for Visionsmith in 2012.

Abstract - Learning models from data has a significant impact on many disciplines, including computer vision, medical imaging, social networks and signal processing. In the network inference problem, one may model the relationships between the network components through an underlying inverse covariance matrix. The sparse inverse covariance estimation problem is commonly solved using an ℓ1-regularized Gaussian maximum likelihood estimator, known as “graphical lasso”. Despite the popularity of graphical lasso, its computational cost becomes prohibitive for large data sets. In this talk, we will develop new notions of sign-consistent matrices and inverse-consistent matrices to obtain key properties of graphical lasso and prove that although the complexity of solving graphical lasso is high, the sparsity pattern of its solution has a simple formula if a sparse graphical model is sought. We will prove — under mild assumptions— that the graphical lasso estimator can be retrieved by soft-thresholding the sample covariance matrix and solving a maximum determinant matrix completion (MDMC) problem, and describe a Newton-CG algorithm to efficiently solve the MDMC problem. We will illustrate our results in different case studies.

Dr. Somayeh Sojoudi is an Assistant Professor in the Departments of Electrical Engineering & Computer Sciences and Mechanical Engineering at UC Berkeley. She is an Associate Editor of the journals of IEEE Transactions on Smart Grid, IEEE Access, and Systems & Control Letters. She is a member of the conference editorial board of the IEEE Control Systems Society. She is a recipient of the 2015 INFORMS Optimization Society Prize for Young Researchers and a recipient of the 2016 INFORMS ENRE Energy Best Publication Award. She was a finalist (as advisor) for the Best Student Paper Award at the 2018 American Control Conference and a finalist (as a co-author) for the best student paper award at the 53rd IEEE Conference on Decision and Control 2014.