
Award Number and Duration 
NSF DMS 2134223 
PI and Point of Contact 
Yi Zhou (PI)
Bei Wang (coPI)
Jie Ding (PI) 
Overview 
The past decade has witnessed the great success of deep learning in broad societal and commercial applications. However, conventional deep learning relies on fitting data with neural networks, which is known to produce models that lack resilience. For instance, models used in autonomous driving are vulnerable to malicious attacks, e.g., putting an art sticker on a stop sign can cause the model to classify it as a speed limit sign; models used in facial recognition are known to be biased toward people of a certain race or gender; models in healthcare can be hacked to reconstruct the identities of patients that are used in training those models. The nextgeneration deep learning paradigm needs to deliver resilient models that promote robustness to malicious attacks, fairness among users, and privacy preservation. This project aims to develop a comprehensive learning theory to enhance the model resilience of deep learning. The project will produce fast algorithms and new diagnostic tools for training, enhancing, visualizing, and interpreting model resilience, all of which can have broad research and societal significance. The research activities will also generate positive educational impacts on undergraduate and graduate students. The materials developed by this project will be integrated into courses on machine learning, statistics, and data visualization and will benefit interdisciplinary students majoring in electrical and computer engineering, statistics, mathematics, and computer science. The project will actively involve underrepresented students and integrate research with education for undergraduate and graduate students in STEM. It will also produce introductory materials for K12 students to be used in engineering summer camps. 
Honors and Awards 
Highlights 
Publications 
Papers marked with * use alphabetic ordering of authors. Students are underlined. 
Software Downloads 
Presentations, Educational Development and Broader Impacts 
Students and Postdocs 
Collaborators 

Acknowledgement 
This material is based upon work supported or partially supported by the National Science Foundation under Grant No. 2134223 and No. 2134148. 