Khoa D. Doan

AI ResearcherCognitive Computing LabBaidu Research | Vietnamese

I am a Researcher in the Cognitive Computing Lab at Baidu Research working with Dr. Ping Li on generative modeling and its practical applications (e.g., Information Retrieval and AI Security). Previously, I was at Virginia Tech/Sanghani Center for Artificial Intelligence & Data Analytics (Prof. Chandan K. Reddy) ⟵ Criteo AI Lab (Dr. Sathiya Keerthi Selvaraj and Dr. Fengjiao Wang) ⟵ UMCP/NASA Goddard Space Flight Center. More about me HERE and my professional/academic services HERE.

I’m looking for motivated research interns/assistants in Vietnam (and potentially other Asian countries), working on Robust ML (e.g., adversarial robustness, backdoor/poisoning attacks, & OOD/generalization robustness), Generative Models (e.g., energy-based generative models and diffusion models), and Low-resource NLP (e.g., translation & summarization). Please fill out this form if you are interested!

Research Interests

My research seeks answers to the following question: How to make ML models simpler & reliable to use in constrained settings? I am interested in developing computational frameworks that enable existing complex/deep models to be more suitable for practical uses. I focus on improving the following aspects of existing models: (i) training/inference, (ii) realistic assumptions, (iii) algorithmic robustness, and (iv) efficiency in constrained settings. Most of my ML/AI solutions center around generative-based approaches that have low computational complexity and require less human effort. More about my research interests HERE.


[04/2023] One paper accepted to SIGIR’2023 on real-time ranking with non-metric/non-linear ranking functions (yes, Neural Networks!)
[02/2023] Gave a talk at Lucy Family Institute for Data and Society, University of Notre Dame on Toward Practical Machine Learning Applications in Constrained Settings.
[02/2023] Will serve as Invited PC for FAccT, ICML, and ICCV 2023.
[01/2023] Will serve on the Editorial Board of Springer’s Discover Data.
[12/2022] Recognized as Top Reviewer at NeurIPS 2022.
[11/2022] One paper accepted to AAAI’2023 on a novel/ONLINE defense against Backdoor Attacks on ViTs.
[11/2022] Will serve as Invited PC for CVPR 2023.
[09/2022] One paper accepted to NeurIPS’2022 on a new threat of Backdoor Attacks.
[09/2022] One paper accepted to ACCV’2022 on EBM/Retrieval.
[07/2022] Will serve as Invited PC for AAAI and ICLR 2023.
[04/2022] Gave a talk on Deep Retrieval Model at VinAI Research, Vietnam
[03/2022] Will serve as Invited PC for NeurIPS 2022.
[03/2022] One paper accepted to CVPR’2022 on Generative Model/Image Hashing.
[03/2022] Will serve as Invited PC for ECCV 2022.
[12/2021] Will serve as Invited PC for ICML 2022.

selected publications [full list]

  1. AAAI Defending backdoor attacks on vision transformer via patch processing
    Doan, Khoa D, Lao, Yingjie, and Li, Ping
    In AAAI Conference on Artificial Intelligence 2023
  2. NeurIPS Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class
    Doan, Khoa D, Lao, Yingjie, and Li, Ping
    In Thirty-Sixth Conference on Neural Information Processing Systems 2022
  3. CVPR One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching
    Doan, Khoa D, Yang, Peng, and Li, Ping
    In Conference on Computer Vision and Pattern Recognition 2022
  4. NeurIPS Backdoor Attack with Imperceptible Input and Latent Modification
    Doan, Khoa D, Lao, Yingjie, and Li, Ping
    In Thirty-Fifth Conference on Neural Information Processing Systems 2021
  5. ICCV LIRA: Learnable, Imperceptible and Robust Backdoor Attacks
    Doan, Khoa D, Lao, Yingjie, Zhao, Weijie, and Li, Ping
    In International Conference on Computer Vision 2021
  6. SIGIR Interpretable Graph Similarity Computation via Differentiable Optimal Alignment of Node Embeddings
    Doan, Khoa D, Manchanda, Saurav, Mahapatra, Suchismit, and Reddy, Chandan K
    In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021
  7. WWW Efficient Implicit Unsupervised Text Hashing Using Adversarial Autoencoder
    Doan, Khoa D, and Reddy, Chandan K
    In Proceedings of The Web Conference 2020
  8. arXiv Gradient boosting neural networks: Grownet
    arXiv preprint arXiv:2002.07971 2020
  9. CIKM Adversarial Factorization Autoencoder for Look-Alike Modeling
    Doan, Khoa D, Yadav, Pranjul, and Reddy, Chandan K
    In Proceedings of the 28th ACM International Conference on Information and Knowledge Management 2019
The brick walls are there for a reason. The brick walls are not there to keep us out. The brick walls are there to give us a chance to show how badly we want something -- Randy Pausch