Anima Anandkumar
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.
Publications
- Lale, Sahin;Renn, Peter I. et al. (2024) FALCON: Fourier Adaptive Learning and Control for Disturbance Rejection Under Extreme Turbulencenpj Robotics
- McClain Gomez, Abigail;Patti, Taylor L. et al. (2024) Near-term distributed quantum computation using mean-field corrections and auxiliary qubitsQuantum Science and Technology
- Gopakumar, Vignesh;Pamela, Stanislas et al. (2024) Plasma surrogate modelling using Fourier neural operatorsNuclear Fusion
- Azizzadenesheli, Kamyar;Kovachki, Nikola et al. (2024) Neural operators for accelerating scientific simulations and designNature Reviews Physics
- Li, Zongyi;Zheng, Hongkai et al. (2024) Physics-Informed Neural Operator for Learning Partial Differential EquationsACM / IMS Journal of Data Science
- Qiao, Zhuoran;Nie, Weili et al. (2024) State-specific protein–ligand complex structure prediction with a multiscale deep generative modelNature Machine Intelligence
- Zhou, Tingtao;Wan, Xuan et al. (2024) AI-aided geometric design of anti-infection cathetersScience Advances
- Luo, Zelun;Zou, Yuliang et al. (2024) Differentially Private Video Activity Recognition
- Zheng, Zhiling;Alawadhi, Ali H. et al. (2023) Shaping the Water-Harvesting Behavior of Metal–Organic Frameworks Aided by Fine-Tuned GPT ModelsJournal of the American Chemical Society
- Feng, Jie;Shi, Yuanyuan et al. (2023) Stability Constrained Reinforcement Learning for Decentralized Real-Time Voltage ControlIEEE Transactions on Control of Network Systems