For more information on the Global WiDS Conference visit www.widsconference.org

For Questions /  Contact us at catalina@exteractions.com

© 2019 by Exteractions  

Anima Anandkumar

Director of Research in Machine Learning
NVIDIA

Anima Anandkumar is a Bren Professor of Computing and Mathematical Sciences at the California Institute of Technology and Director of Research in Machine Learning at NVIDIA. Her research is in the areas of large-scale machine learning and high-dimensional statistics, and in particular, development of tensor methods that scale up machine learning to higher dimensions. She is also the recipient of the Alfred Sloan Fellowship, Microsoft Faculty Fellowship, ARO and AFOSR Young Investigator Awards, NSF Career Award and several paper awards. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a Postdoctoral Researcher in the Stochastic Systems Group at MIT from 2009-2010, an Assistant Professor at UC Irvine from 2010-2016, and Principal Scientist at Amazon Web Services from 2016-2018.

Infusing Structure into Machine Learning

Standard deep-learning algorithms are based on a function-fitting approach that do not exploit any domain knowledge or constraints. This makes them unsuitable in applications that have limited data or require safety or stability guarantees, such as robotics. By infusing structure and physics into deep-learning algorithms, we can overcome these limitations. There are several ways to do this. For instance, we use tensorized neural networks to encode multidimensional data and higher-order correlations. We infuse symbolic expressions into deep learning to obtain strong generalization. We utilize spectral normalization of neural networks to guarantee stability and apply it to stable landing of quadrotor drones. These instances demonstrate that building structure into ML algorithms can lead to significant gains.