Hello! My name is Chawin Sitawarin. I am a PhD student in Computer Science at UC Berkeley, and I am a part of the security group, Berkeley Artificial Intelligence Research (BAIR) and Berkeley DeepDrive (BDD). My advisor is Prof. David Wagner.
I am broadly interested in the intersection between machine learning and computer security. Most of my current and previous works are in the domain of adversarial machine learning, particularly adversarial examples and robustness of machine learning algorithms. If you are wondering why I appear as a panda, give this paper a read.
I used to keep track of papers on adversarial examples, but I stopped after the number of papers has become overwhelming. You can still find the list here (last update: Sep 2019).
|Aug 30, 2021||Our project on large-scale adversarial patch benchmark is funded by Microsoft-BAIR Commons.|
|Jul 23, 2021||Our paper, Improving the Accuracy-Robustness Trade-Off for Dual-Domain Adversarial Training, is accepted to ICML 2021 Workshop on Uncertainty & Robustness in Deep Learning. [paper]|
|Jun 8, 2021||I interned at Nokia Bell Labs (remote) and was very fortunate to be mentored by Anwar Walid.|
|May 7, 2021||Our paper, Mitigating Adversarial Training Instability with Batch Normalization, is accepted to ICLR 2021 Workshop on Security and Safety in Machine Learning Systems. This work is led by Arvind Sridhar, an undergraduate student I mentor at UC Berkeley. [paper]|
|Dec 10, 2020||Our project was awarded a grant from Center for Long-Term Cybersecurity (CLTC) for 2021.|
|May 28, 2019||I was fortunate to intern at IBM Research (Yorktown Heights, NY) over the summer of 2019 and to be mentored by Supriyo Chakraborty.|