This project develops robust, accurate, and efficient next-generation deep learning algorithms with data privacy and theoretical guarantees for solving challenging artificial intelligence (AI) problems. The methods will have robustness to adversarial attacks with theoretical guarantees. The project will push artificial intelligence gains in performance and privacy to mobile devices. A broad range of applications includes autonomous driving, drug and material discovery, medical treatment planning, national defense, privacy-preserving machine learning at the edge, federated learning, and also blockchain. Moreover, the developed tools will significantly scale the existing scientific simulations to ultra-large scale and high-dimensional scenarios. This project will partially support one graduate student per year at each campus.
Our approach toward trustworthy deep learning is theoretically principled by modern partial differential equations and optimization algorithms and theories. The project involves new algorithmic and theoretical techniques to tackle graph representation in high-dimensional non-convex, non-smooth AI settings. In particular, the project will study (1) developing adversarial robust deep learning algorithms and their theoretical foundations; (2) improving the accuracy of deep learning leveraging new stochastic optimization and principled neural network unit design assisted neural architecture search; (3) advancing deep neural networks compression with algorithms and hardware co-design; (4) designing new data privacy mechanisms to optimally tradeoff between utility and privacy; (5) inventing new quantitative analysis tools to decipher the mysteries of deep learning theoretical challenges; (6) quantifying uncertainties of sophisticated deep learning algorithms. The project trains a diverse body of graduate and undergraduate students at UC Irvine, UCLA, and University of Utah through collaborative education and research activities in applied mathematics, computer science, data science, and general biological, physical, and sociological disciplines.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
