Assistant Professor @ UC Santa Barbara
Co-Director @ REAL AI initiative
Senior Research Scientist @ Google DeepMind
I am an Assistant Professor at the Department of Electrical and Computer Engineering, affiliated with the Department of Computer Science at UC Santa Barbara, where I am also co-leading the REAL AI initiative. Meanwhile, I am a senior Research Scientist at Google DeepMind, working on Gemini Safety. I obtained my PhD degree at UC San Diego in Computer Science, advised by Prof. Garrison W. Cottrell. During my PhD, I was very fortunate to intern under the supervision of Geoffrey Hinton, Ian Goodfellow and many others.
My research interests primarily focus on robustness in machine learning, such as adversarial robustness, out-of-distribution generalization, and fairness. In addition, I am highly passionate about developing reliable AI-driven models tailored for healthcare, with a particular focus on diabetes management. In my lab, we explore various research themes for designing robust & fair machine learning models. Specifically, our research themes include:
Services: Invited to serve as an Area Chair for ICML-24, ICLR-24/23, ICCV-23, Co-Local Arrangement Chair for KDD-2023.
Hiring! I am actively seeking motivated postdoctoral researchers and students who are enthusiastic about robustness and diabetes care to join my research lab. In addition, we also have open positions for visiting students interested in engaging in collaborative research. You are welcome to contact me at yaoqin@ucsb.edu with your resume if you have an interest to join us. If you are a current student at UCSB, please email me with [UCSB Student] in the title!
Recommendations! If you are an undergraduate/master student at UCSB and wants to work with me, the best way is to reach out to my amazing PhD students first to seek potential collaborations!
Mehak Dhaliwal (PhD)
Working on Large Language Model & AI for Diabetes
Andong Hua (PhD)
Working on Robustness in Multimodal & Large Language Model
Kenan Tang (PhD)
Working on AI for Diabetes
Youngseok Yoon (PhD, co-advised with Prof. Haewon Jeong)
Working on Generative Models
Detecting Out-of-Distribution through the Lens of Neural Collapse
Litian Liu and Yao Qin
[Paper]
Towards Robust Prompts on Vision-Language Models
Jindong Gu, Ahmad Beirami, Xuezhi Wang, Alex Beutel, Philip Torr and Yao Qin
[Paper]
Deflecting Adversarial Attacks
Yao Qin, Nicholas Frosst, Colin Raffel, Garrison Cottrell and Geoffrey Hinton
[Paper]
Evaluation Methodology for Attacks Against Confidence Thresholding Models
Ian Goodfellow, Yao Qin, David Berthelot
[Paper]
Fast Decision Boundary based Out-of-Distribution Detector
Litian Liu and Yao Qin
International Conference on Machine Learning (ICML), 2024.
[Paper][Code]
Initialization Matters for Adversarial Transfer Learning
Andong Hua, Jindong Gu, Zhiyu Xue, Nicholas Carlini, Eric Wong and Yao Qin
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
[Paper][Code]
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel and Yao Qin
Advances in Neural Information Processing Systems (NeurIPS), 2023
[Paper]
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel and Xuezhi Wang
Advances in Neural Information Processing Systems (NeurIPS), 2022
[Paper]
Are Vision Transformers Robust to Patch Perturbations?
Jindong Gu, Volker Tresp, Yao Qin
European Conference on Computer Vision (ECCV), 2022
[Paper][Code]
Improving Calibration through the Relationship with Adversarial Robustness
Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi
Advances in Neural Information Processing Systems (NeurIPS), 2021
[Paper]
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
Yao Qin*, Nicholas Frosst*, Sara Sabour, Colin Raffel, Garrison Cottrell and Geoffrey Hinton
International Conference on Learning Representations (ICLR), 2020
[Paper]
Imperceptible, Robust and Targeted Adversarial Examples for Automatic Speech Recognition
Yao Qin, Nicholas Carlini, Ian Goodfellow, Garrison Cottrell, Colin Raffel
International Conference on Machine Learning (ICML), 2019.
[Paper][Project Page][Code]
Autofocus Layer for Semantic Segmentation
Yao Qin, Konstantinos Kamnitsas, Siddharth Ancha, Jay Nanavati, Garrison Cottrell, Antonio Criminisi, Aditya Nori
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018.
Oral presentation (4% acceptance rate)
[Paper][Code]
Hierarchical Cellular Automata for Visual Saliency
Yao Qin*, Mengyang Feng*, Huchuan Lu, Garrison Cottrell
International Journal of Computer Vision (IJCV), 2017.
[Paper][Code]
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, Garrison Cottrell
International Joint Conference on Artificial Intelligence (IJCAI), 2017.
[Paper][Code][Data]
Saliency Detection via Cellular Automata
Yao Qin, Huchuan Lu , Yiqun Xu, He Wang
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
[Paper][Code][中文版]
Other than research, I am also a big fan of old movies. Unfortunately, all my reviews are only in Chinese, but you can use LLM to translate them if you want :)