Modern deep learning has relied on large labeled datasets for training. However, such datasets are not easily available in all domains, and are expensive/difficult to collect. We integrate data collection and aggregation with model training through active learning, partial feedback and crowdsourcing methods. We also develop sample efficient training algorithms through the use of synthetic data, generative models and semi-supervised learning. We develop tensor algebraic algorithms that efficiently encode multiple modalities and higher order dependencies. These techniques can drastically reduce data requirements in a variety of domains.
Anima Anandkumar is a Bren professor at Caltech CMS department. Her research spans both theoretical and practical aspects of large-scale machine learning. In particular, she has spearheaded research in tensor-algebraic methods, non-convex optimization and probabilistic models.
Anima is the recipient of several awards and honors such as the Bren endowed chair professorship at Caltech, Alfred. P. Sloan Fellowship, Microsoft Faculty Fellowship, Google faculty award, ARO and AFOSR Young Investigator Awards, NSF Career Award, Best Thesis Award from the ACM Sigmetrics society, IBM Fran Allen PhD fellowship, and several best paper awards. She was recently nominated to join the World Economic Forum's Expert Network. She has been featured in documentaries by PBS, KPCC, wired magazine, and in articles by MIT Technology review, Forbes, Yourstory, O’Reilly media, and so on. Anima 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 at MIT from 2009 to 2010, a visiting researcher at Microsoft Research New England in 2012 and 2014, an assistant professor at U.C. Irvine between 2010 and 2016, an associate professor at U.C. Irvine between 2016 and 2017, and a principal scientist at Amazon Web Services between 2016 and 2018.