Animashree Anandkumar

Animashree Anandkumar

Bren Professor
Microsoft and Sloan Fellow
Computing + Mathematical Sciences
California Institute of Technology


Email: anima *
Admin: Pamela S. Albertson
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Computing + Mathematical Sciences
California Institute of Technology
316 Annenberg Hall
1200 E California Blvd. MC 305-16, Pasadena, CA 91125


Animashree (Anima) Anandkumar is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. Her research spans both theoretical and practical aspects of machine learning. In particular, she has spearheaded research in tensor-algebraic methods, large-scale learning, deep learning, probabilistic models, and non-convex optimization.
Anima is the recipient of several awards such as the Alfred. P. Sloan Fellowship, NSF Career Award, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. She is the youngest named professor at Caltech, the highest honor bestowed to an individual faculty. She is part of the World Economic Forum's Expert Network consisting of leading experts from academia, business, government, and the media. 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, visiting researcher at Microsoft Research New England in 2012 and 2014, assistant professor at U.C. Irvine between 2010 and 2016, associate professor at U.C. Irvine between 2016 and 2017, and principal scientist at Amazon Web Services between 2016 and 2018.

Full CV

For Prospective Students and Postdocs

We are looking for highly motivated and self-driven PhD students and postdoctoral candidates with strong foundation in machine learning, statistics, and algorithms. Both theoretical and empirical research is carried out in the group and students who can build bridges between the two, and also between different disciplines will be good fit here. There are also a small number of positions for undergraduate research.

Selected Publications

  • Tensor Decompositions for Learning Latent Variable Models, JMLR, 2014. pdf
  • signSGD: compressed optimisation for non-convex problems, ICML, 2018. pdf
  • Combining Symbolic Expressions and Black-Box Function Evaluations In Neural Programs, ICLR 2018. pdf
  • Learning From Noisy Singly-labeled Data, ICLR 2018. pdf

Complete list of publications

Media Features

  • Tensor Operations for Machine Learning with Anima Anandkumar, TWiML&AI Podcast, 2018. url
  • Story of Anima Anandkumar, the machine learning guru powering Amazon AI, YourStory, 2017 url
  • Teaching Machines How to Learn: An Interview with Animashree Anandkumar, Caltech, 2017 url

All media stories and interviews/panels


Winter 2019 Foundations of Machine Learning

All teaching courses


Technical Talks

Complete list of talks