Luba Gloukhova leads and executes advanced machine learning projects for high tech firms and major research universities in Silicon Valley. She also preaches what she practices, serving as the founding chair of Deep Learning World – the premier conference covering the commercial deployment of deep learning – and delivering highly-rated talks at many other events as well. Luba previously supported Stanford faculty as an internal consultant at the unversity's Graduate School of Business, conceiving and generating innovative solutions to accelerate research.
Before that, Luba gained industry experience in high frequency trading analysis, catastrophe risk modeling, and marketing analytics. She received her master’s in analytics from the University of San Francisco and two bachelors degrees from Berkeley: applied mathematics and economics. Luba also teaches yoga and enjoys an active lifestyle.
Groundbreaking theory, big data, and compute power — with this trifecta, the extraordinary advent of deep learning seems almost inevitable. It propels machine learning to new heights across many industries. As we ride this wave of progress, still in acceleration, we come to a new class of challenges akin to those of traditional machine learning — but now the stakes are higher. In this presentation Luba Gloukhova will cover great challenges deep learning has already overcome and those that still remain.