Hongyang Zhang is a Postdoc fellow at Toyota Technological Institute at Chicago, hosted by Avrim Blum and Greg Shakhnarovich. He obtained his Ph.D. from CMU Machine Learning Department in 2019, advised by Maria-Florina Balcan and David P. Woodruff. His research interests lie in the intersection between theory and practice of machine learning, robustness and AI security. His methods won the championship or ranked top in various competitions such as the NeurIPS’18 Adversarial Vision Challenge (all three tracks), the Unrestricted Adversarial Examples Challenge hosted by Google, and the NeurIPS’20 Challenge on Predicting Generalization of Deep Learning. He also authored a book in 2017.

SWS   Info

AG Audience

English

60 Minutes

Saarbrücken

In this talk, I will describe a distributionally robust learning framework that offers accurate uncertainty quantification and rigorous guarantees under data distribution shift. This framework yields appropriately conservative yet still accurate predictions to guide real-world decision-making and is easily integrated with modern deep learning. I will showcase the practicality of this framework in applications on agile robotic control and computer vision. I will also introduce a survey of other real-world applications that would benefit from this framework for future work.

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Danielle Dalton

+49 681 9303 9106

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