The Future of Machine Intelligence

Chapter 1. Anima Anandkumar: Learning in Higher Dimensions

Anima Anandkumar is on the faculty of the EECS Department at the University of California Irvine. Her research focuses on high-dimensional learning of probabilistic latent variable models and the design and analysis of tensor algorithms.

Key Takeaways

• Modern machine learning involves large amounts of data and a large number of variables, which makes it a high dimensional problem.

• Tensor methods are effective at learning such complex high dimensional problems, and have been applied in numerous domains, from social network analysis, document categorization, genomics, and towards understanding the neuronal behavior in the brain.

• As researchers continue to grapple with complex, highly-dimensional problems, they will need to rely on novel techniques in non-convex optimization, in the many cases where convex techniques fall short.

Let’s start with your background.

I have been fascinated with mathematics since my childhood—its uncanny ability to explain the complex world we live in. During my college days, I realized the power of algorithmic thinking in computer science and engineering. Combining these, I went on to complete a Ph.D. at Cornell University, then a short postdoc at MIT before moving to the faculty at UC Irvine, where I’ve spent the past six years.

During my Ph.D., I worked on the problem of designing efficient algorithms for distributed learning. More specifically, when multiple devices or sensors are ...