Yao-Yuan is a Ph.D. candidate in the Computer Science and Engineering department of UCSD. He is currently working with Professor Kamalika Chaudhuri on trustworthy machine learning, which includes topics such as adversarial examples, interpretability, and spurious correlations. Prior to joining UCSD, he received his B.S. in Computer Science from National Taiwan University in 2016.
Here are some problems he previously worked on:
Yao-Yuan Yang, Angel Hsing-Chi Hwang, Chien-Te Wu, and Tsung-Ren Huang, in submission, 2021, [bib]
Yao-Yuan Yang*, Moto Hira*, Zhaoheng Ni*, Anjali Chourdia, Artyom Astafurov, Caroline Chen, Ching-Feng Yeh, Christian Puhrsch, David Pollack, Dmitriy Genzel, Donny Greenberg, Edward Z. Yang, Jason Lian, Jay Mahadeokar, Jeff Hwang, Ji Chen, Peter Goldsborough, Prabhat Roy, Sean Narenthiran, Shinji Watanabe, Soumith Chintala, Vincent Quenneville-Bélair, and Yangyang Shi, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, [bib] [arxiv] [code]
Angel Hsing-Chi Hwang, Cheng Yao Wang, Yao-Yuan Yang, and Andrea Stevenson Won, in Proceedings of the 2021 ACM Conference on Computer Supported Cooperative Work (CSCW), 2021, [bib] [pdf] [video] [url]
Benjamin Cosman, Madeline Endres, Georgios Sakkas, Leon Medvinsky, Yao-Yuan Yang, Ranjit Jhala, Kamalika Chaudhuri, and Westley Weimer, in Proceedings of the Special Interest Group on Computer Science Education (SIGCSE), 2020, [bib] [pdf]