Short Bio
Yao-Yuan is currently working in DeepMind. In 2022, he received his Ph.D. from the Computer Science and Engineering department of UCSD, advised by Professor Kamalika Chaudhuri. His thesis was 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 my CV,research statement, and three representative papers (paper1, paper2, paper3).
Here are some problems he previously worked on:
- Cost-sensitive multi-label classification
- Active learning
- Brain decoding with electroencephalogram (EEG)
News
- Our work "Person-identifying brainprints are stably embedded in EEG mindprints" get accepted to Scientific Reports
- Our work "What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning" get accepted to NeurIPS 2022
- Our work "Connecting Interpretability and Robustness in Decision Trees through Separation" get accepted to ICML 2021
- Our work "A Closer Look at Accuracy vs. Robustness" get accepted to NeurIPS 2020
- Our work "Robustness for Non-Parametric Methods: A Generic Attack and Defense" get accepted to AISTATS 2020
- I am presenting my work at the Machine Learning with Guarantees workshop at NeurIPS 2019
- I am presenting my work at ACML 2019
Experiences
- Research Engineer, DeepMind, August 2022 - Current
- Ph.D., Computer Science and Engineering, University of California, San Diego, September 2017 - June 2022
- Alternative service of education, Minister of Education, Aug 2016 - June 2017
- B.S., Department of Computer Science and Information Engineering, National Taiwan University, September 2012 - June 2016
- Lab member, Modeling & Informatics Lab, 2014 - 2016
- Research Assistant, Computational Learning Lab, 2013 - 2016
- Lab member,Fast Crypto Lab, 2012 - 2014