Our research aims to develop Machine Learning Algorithms that Make Sense in constrained and large-scale settings with applications in Advertising, Healthcare, Sustainability (Climate, Computing, Agricultural), Social Goods
[more about our research]


I'm open to research/industry collaborations in ML/CV/NLP:
  • Intersted in Ph.D. position? Please apply through the CS department and include my name as a potential advisor in your application.
  • Interested in ML research with me (Research Assistant/Intern)? Please fill in the form here (w. your CV, transcript, level of commitment, & description of what types of projects you want to work on). Only shortlisted candidates will be contacted!
  • Other collaboration? Please reach out via email.

news

[02/2024] Heng Ji & I gave talks at HCMUT/HCMUS (Ho Chi Minh, Jan 31st) and HUST/VNU/VinAI (Hanoi, Feb 01 and 02). We have immediate PhD (at VinUni or UIUC)/Research Assistants (at VinUni) positions to work on LLM Truthfulness and NLP for Molecular Discovery. I’m also recruiting students to work on Counterfactual Infererence/Explanation.
[01/2024] :fire: Our “AI for Environmental Intelligence: The Past, The Present, and The Future” Workshop proposal (w. Helen Nguyen, Nitesh Chawla, Alexandre d’Aspremont, Karina Ginn) accepted at CAI 2024 (June 25-27, 2024). More information here!
[01/2024] :fire: We’re granted 2 proposals by Vinuni-UIUC Smarthealth Center on Causal Inference in Healthcare and Evaluating Truthfulness/Misinformation of NLP/LLM (more details; we’re recruiting PhD Students/RAs)
[01/2024] One paper accepted to ICLR 2024 (congrats Hung-Quang Nguyen, w. Tung Pham/VinAI), on same-inference-time Adversarial Defense.
[09/2023] Three papers accepted to NeurIPS 2023 (w. Anh Tuan Tran/VinAI), EAAI Journal, ACML 2023 (congrats Minh-Tuan Nguyen) on Backdoor, Federated Learning, and Unleaning.
[08/2023] :fire: We (w. Helen Meng & Viet-Anh Nguyen, CUHK) are granted $150k by the Gates Foundation/GC to use Generative AI/LLMs for Inclusive AI and Equitable Access in Healthcare (more details)
[07/2023] :fire: Our BUGS workshop (w. Aniruddha Saha, Anh Tuan Tran, Yingjie Lao, Kok-seng Wong, Ang Li, Haripriya Harikumar, Eugene Bagdasaryan, Micah Goldblum, & Tom Goldstein) on Backdoor/Watermarking/Social Goods accepted to NeurIPS’2023.
[05/2023] Will serve as Invited PC for NeurIPS/WACV 2023 and AAAI/ICLR 2024.
[04/2023] :fire: 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] :fire: Will serve on the Editorial Board of Springer’s Discover Data.
[12/2022] :fire: 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.

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

Open Office Hour

I will ocassionally be holding group open office hours (fully ONLINE) for *anyone*. Feel free to sign up to connect, chat, or ask any questions.

When I was a student, I was clueless sometimes (if not most of the time) and I had no idea how to get help. I hope that, via this modest effort, I can share some experience with you, as well as address some questions you may have, using my experience working in both industry and academia and applied and research projects, as well as experience in studying abroad in the US. I encourage to converse in English.

This effort is inspired by ML Collective

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