Animashree Anandkumar

Animashree Anandkumar

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

Contact

Admin: Alma R. Rangel-Fuentes (arangelf@caltech.edu)
Please email Alma if you want to contact me.

Computing + Mathematical Sciences
California Institute of Technology
316 Annenberg Hall
1200 E California Blvd. MC 305-16, Pasadena, CA 91125

Bio

Anima Anandkumar holds dual positions in academia and industry. She is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. At NVIDIA, she is leading the research group that develops next-generation AI algorithms. At Caltech, she is the co-director of Dolcit and co-leads the AI4science initiative, along with Yisong Yue.
She has spearheaded the development of tensor algorithms, first proposed in her seminal paper. They are central to effectively processing multidimensional and multimodal data, and for achieving massive parallelism in large-scale AI applications.
Prof. Anandkumar is the youngest named chair professor at Caltech, the highest honor the university bestows on individual faculty. She is recipient of several awards such as the Alfred. P. Sloan Fellowship, NSF Career Award, Faculty fellowships from Microsoft, Google and Adobe, and Young Investigator Awards from the Army research office and Air Force office of sponsored research. She has been featured in documentaries and articles by PBS, wired magazine, MIT Technology review, yourstory, and Forbes.
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.

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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.