Yao Qin

Assistant Professor @ UC Santa Barbara

Co-Director @ REAL AI initiative

Senior Research Scientist @ Google

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 Research. 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:

  • Robustness in multi-modality models
  • Fairness in generative modeling
  • AI for healthcare, particularly for diabetes
  • 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!


    Lab Members

    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 Fairness in Generative Models


    Towards Robust Prompts on Vision-Language Models
    Jindong Gu, Ahmad Beirami, Xuezhi Wang, Alex Beutel, Philip Torr and Yao Qin

    Deflecting Adversarial Attacks
    Yao Qin, Nicholas Frosst, Colin Raffel, Garrison Cottrell and Geoffrey Hinton

    Evaluation Methodology for Attacks Against Confidence Thresholding Models
    Ian Goodfellow, Yao Qin, David Berthelot

    Selected Publications

    (* indicates equal contributions.)

    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.

    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

    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

    Are Vision Transformers Robust to Patch Perturbations?
    Jindong Gu, Volker Tresp, Yao Qin
    European Conference on Computer Vision (ECCV), 2022

    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

    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

    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)

    Hierarchical Cellular Automata for Visual Saliency
    Yao Qin*, Mengyang Feng*, Huchuan Lu, Garrison Cottrell
    International Journal of Computer Vision (IJCV), 2017.

    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.

    Saliency Detection via Cellular Automata
    Yao Qin, Huchuan Lu , Yiqun Xu, He Wang
    IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.


    Trip Journal Series: Turkey - Paris, 2024
    [English Version][中文版]

    Trip Journal Series: Vienna, 2023
    [English Version][中文版]