Anima Anandkumar facts for kids

Animashree (Anima) Anandkumar is the Bren Professor of Computing at California Institute of Technology. Previously, she was a senior director of Machine Learning research at NVIDIA and a principal scientist at Amazon Web Services. Her research considers tensor-algebraic methods, deep learning and non-convex problems.

Education and early career

Anandkumar was born in Mysore. Her parents are both engineers, and her grandfather was a mathematician. Her great-great-grandfather was the Sanskrit scholar R. Shamasastry. She began to study Bharatanatyam and she learnt this style of dancing for many years. She studied electrical engineering at the Indian Institute of Technology Madras and graduated in 2004. She joined Cornell University for her graduate studies, earning a PhD under the supervision of Lang Tong in 2009. Her first project looked at distributed statistical estimation. She was an IBM Fellow at Cornell University between 2008 and 2009. Her thesis considered Scalable Algorithms for Distributed Statistical Inference. During her PhD she worked in the networking group at IBM on end-to-end service-level transactions. She was a postdoctoral scholar at Massachusetts Institute of Technology until 2010, where she worked in the Stochastic Systems Group with Alan Willsky.

Research

In 2010, Anandkumar joined University of California, Irvine, as an assistant professor. At the time, the technology industry was at the beginning of the big data revolution. Here she started working on tensor decompositions of latent variable models. She joined Microsoft Research in New England as a visiting scientist in 2012. In 2013 she was awarded a National Science Foundation CAREER Award to investigate big data and social networks. She was made an assistant professor with tenure at UC Irvine in 2016. She specialised in large-scale machine learning and high-dimensional statistics. Anandkumar was a Principal Scientist at Amazon Web Services from 2016 to 2018. She worked with the Apache MXNet tool, introducing new functionality and developing multi-modal processing algorithms. She represented Amazon Web Services at the Anita Borg Institute in 2017, the Mulan forum for Chinese women entrepreneurs and Shaastra in 2018, discussing Deep Learning. She also worked on Amazon Rekognition, Amazon Lex and Amazon Polly. She was involved with the launch of Amazon SageMaker, an opportunity for developers to use machine learning models. Anandkumar joined the Machine Learning Conference Board of Advisors in 2018. In 2018, Anandkumar joined NVIDIA as Director of Machine Learning Research, and Caltech as the Bren Professor of Computing and Mathematical Sciences. At NVIDIA she opened a new core laboratories in artificial intelligence and machine learning in Santa Clara. She has pushed for governments to invest in robotics and artificial intelligence. She spoke at the 2018 TEDxIndiana University about the algorithms she has developed to process big data.

Anima Anandkumar has also developed AI algorithms that with applications in various scientific domains including weather forecasting, drug discovery, scientific simulations and engineering design. She invented Neural Operators that extend deep learning to modeling multi-scale processes in these scientific domains and learn in function spaces and are orders of magnitude faster than traditional simulations. She has developed AI-based high-resolution weather models, an AI-aided method for designing anti-infection medical catheters. Neural operators were featured as a highlight for 2021 in Math and Computer Science by the Quanta Magazine, and genome-scale foundation models with emergent behavior in predicting evolutionary dynamics and protein function in several diverse tasks and scenarios, which won the Association for Computing Machinery (ACM) Gordon Bell Special Prize for High Performance Computing-Based COVID-19 Research in 2022.

Anandkumar has also done some of the early work on generalist AI agents using language models, which are capable of life-long learning using foundation models in an interactive manner. In particular, her work has shown how interactive in-context learning in language models can be used to construct actions in form of program code to solve complex open-ended tasks in environments such as Minecraft and robotic reinforcement learning.

While at Caltech, Anandkumar co-founded the AI for Science initiative in 2018. In 2023, she was invited by the Presidential Council of Advisors on Science and Technology (PCAST) on AI+Science. In addition, she has given keynotes at the Annual Meeting of the US National Committee for Theoretical and Applied Mechanics, the UCLA distinguished seminar, the SIAM annual meeting, and the Nature Reviews Physics, hosted by the Alan Turing Institute.

Diversity in technology

Anandkumar is committed to improving diversity in the technology sector. She launched a petition to Timothy A. Gonsalves to try and convince him at the Ministry of Human Resource Development to end gender segregation in the admissions process at the Indian Institute of Technology Madras. ..... She was one of several campaigners to rename the Conference on Neural Information Processing Systems 'NIPS' as NeurIPS. In 2018, she was awarded a New York Times Good Tech Award.

Awards and honors

Anandkumar has won several awards and honours, including:

• 2024 Distinguished Alumnus Award by IIT Madras
• 2023 Guggenheim Fellow
• 2023 Schmidt Sciences AI 2050 Senior Fellow
• 2023 AAAI Fellow
• 2022 Outstanding Paper at Neural Information Processing
• 2022 ACM Gordon-Bell Special Prize for HPC for COVID-19 Research
• 2022 ACM Fellow
• 2020 IEEE Fellow
• 2020 Women in AI research award by Venturebeat
• 2017 Caltech Bren Chair
• 2015 Google Research Award
• 2015 Air Force Office of Scientific Research Young Investigator Award
• 2015 University of California, Irvine Early Career Research Award
• 2014 Sloan Research Fellowship
• 2013 Microsoft Faculty Fellowship
• 2013 Army Research Office Young Investigator Award
• 2013 National Science Foundation CAREER Award
• 2011 ACM SIGMETRICS Best Paper Award
• 2009 ACM SIGMETRICS Best Thesis Award
• 2008 IEEE Signal Processing Society Best Paper Award