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] [more about SAIL Research]


I'm open to research/industry collaborations in ML/CV/NLP:
  • Interested in ML research as Research Assistant, Intern, PhD, Postdoc? 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!
  • :fire:Immediate Positions:fire:: Research Assistant (2-3), UIUC or VinUni PhD/Postdoc (co-advised with Heng Ji, Minh Do, Bo Li, etc...), please also fill in the form here.
  • Other collaboration? Please reach out via email.

news

[04/2024]
Congrats Nghia Dai Nguyen for his acceptance to UIUC PhD Program (co-advised)
[03/2024] Will serve as Invited Area Chair for NeurIPS 2024.
[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.
[12/2023]
Congrats Hoang M Nguyen for his new journey as PhD Student at Saarland University
[12/2023] Will serve as Invited PC for ECCV and ICML 2024.
[10/2023] Will serve as Invited PC for CVPR 2024.
[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.

selected publications [full list]

  1. PREPRINT Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models
    Sze Jue Yang, Chinh D La, Quang H Nguyen, Eugene Bagdasaryan, Kok-Seng Wong, Anh Tuan Tran, Chee Seng Chan, and Khoa D Doan
    2024
  2. PREPRINT Forget-Me-Not: Making Backdoor Hard to be Forgotten in Fine-tuning
    Tran Ngoc Huynh, Anh Tran, Khoa D Doan, and Tung Pham
    2024
  3. PREPRINT Fooling the Textual Fooler via Randomizing Latent Representations
    Cao-Duy Hoang, Quang H Nguyen, Saurav Manchanda, Minlong Peng, Kok-Seng Wong, and Khoa D Doan
    2024
  4. ICLR Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks
    Quang H Nguyen, Yingjie Lao, Tung Pham, Kok-Seng Wong, and Khoa D Doan
    In The Twelfth International Conference on Learning Representations 2024
  5. NeurIPS Iba: Towards irreversible backdoor attacks in federated learning
    Thuy Dung Nguyen, Tuan A Nguyen, Anh Tran, Khoa D Doan, and Kok-Seng Wong
    Advances in Neural Information Processing Systems 2024
  6. EAAI Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions
    Thuy Dung Nguyen, Tuan Nguyen, Phi Le Nguyen, Hieu H Pham, Khoa D Doan, and Kok-Seng Wong
    Engineering Applications of Artificial Intelligence 2024
  7. SIGIR Asymmetric Hashing for Fast Ranking via Neural Network Measures
    Khoa D Doan, Shulong Tan, Weijie Zhao, and Ping Li
    In 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023
  8. AAAI Defending backdoor attacks on vision transformer via patch processing
    Khoa D Doan, Yingjie Lao, and Ping Li
    In AAAI Conference on Artificial Intelligence 2023
  9. NeurIPS Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class
    Khoa D Doan, Yingjie Lao, and Ping Li
    In Thirty-Sixth Conference on Neural Information Processing Systems 2022
  10. CVPR One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching
    Khoa D Doan, Peng Yang, and Ping Li
    In Conference on Computer Vision and Pattern Recognition 2022
  11. NeurIPS Backdoor Attack with Imperceptible Input and Latent Modification
    Khoa D Doan, Yingjie Lao, and Ping Li
    In Thirty-Fifth Conference on Neural Information Processing Systems 2021
  12. ICCV LIRA: Learnable, Imperceptible and Robust Backdoor Attacks
    Khoa D Doan, Yingjie Lao, Weijie Zhao, and Ping Li
    In International Conference on Computer Vision 2021
  13. SIGIR Interpretable Graph Similarity Computation via Differentiable Optimal Alignment of Node Embeddings
    Khoa D Doan, Saurav Manchanda, Suchismit Mahapatra, and Chandan K Reddy
    In 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021
  14. WWW Efficient Implicit Unsupervised Text Hashing Using Adversarial Autoencoder
    Khoa D Doan, and Chandan K Reddy
    In Proceedings of The Web Conference 2020
  15. arXiv Gradient boosting neural networks: Grownet
    arXiv preprint arXiv:2002.07971 2020
  16. CIKM Adversarial Factorization Autoencoder for Look-Alike Modeling
    Khoa D Doan, Pranjul Yadav, and Chandan K Reddy
    In Proceedings of the 28th ACM International Conference on Information and Knowledge Management 2019
Majority of work done by MAIL/SAIL members!
Submission History shows the venues where the work has been submitted (🙃 including rejections 🙃). I hope some of my poor rejection/failure histories (record now is 10 rejections 😅) give you some encouragement to try again when things don't work out (don't give up -- good work doesn't need to be rushed)!

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