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]
Selected Press Coverage: khoahocphattrien , Thanh Nien, VnExpress, BaoDauTu, DanTri, Vietnam.vn, Vietnam.vn, Yahoo Finance, Benzinga, Macau Business, Taiwan News, TNGlobal, VinGroup…
VinUni-Illinois Smart Health Center – VISHC: VISHC is open to collaborate with all researchers and research/industry institutions in Vietnam and around the world. VISHC aims to solve various healthcare related challenges with translational and innovative research. Led by Prof. Minh Do and Prof. Helen Nguyen at UIUC, and Prof. Khoa D Doan at VinUni, the VISHC’s team comprises of world-renowned researchers and talented PhD/Master Students, Research Assistants and Postdocs. Please reach out via email for any collaboration opportunities.
Center for Environmental Intelligence – CEI: CEI represents a pioneering initiative at the intersection of advanced technology, environmental science, and interdisciplinary research and is open for collaboration. Led by Prof. Laurent El Ghaoui, CEI aims to address critical global sustainability challenges with innovative approaches based on AI. Please reach out via email for any collaboration opportunities, especially those related to environmental monitoring.
- 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!
- Several Positions (PhD, Master, Research Assistants, Software Developers) are SOON posted (by Mid November, 2024). Please check back in a few weeks for potential collaborations.
- Other collaboration? Please reach out via email.
news
[10/2024] | We received ~$340K USD AWS funding from AWS Health Equity Initiative (HEI) Program. |
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[09/2024] | Will serve as Invited Area Chair for AISTATS 2025. |
[08/2024] | Accepted papers - [ICPR’24] on learning novel concepts with Backdoors and [CODS-COMAD’24] on imbalanced text classification with contrastive learning. |
[07/2024] | Accepted papers - [ECCV’24-a] (Oral) on resilient backdoor attack and [ECCV’24-b] on backdoor attack after quantization (congrats Hoang V Pham and Tran Huynh/VinAI). |
[06/2024] |
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[06/2024] | I (w. co-leads Helen Nguyen/UIUC and Giang Phi/VinUni, and other collaborators) received the grand-prize award (£1M) at Trinity Challenge (in partnership with MIT Solve). Our work, using Generative AI to help farmers make better decisions and community managers have better antibiotics cluster surveilance to reduce antimicrobial resistance, is featured at several news outlets: Office Annoucement, Thanh Nien, VnExpress, BaoDauTu, DanTri, Vietnam.vn, Yahoo Finance, etc… Click to read more about our Farm2Vet Solution. |
[05/2024] | Accepted papers - [UAI’24] on lightweight, pluggable textual adversarial defense, and [ACL’24] on cold-start recommendation with personalized rating elicitation (hat off to Viet-Anh Nguyen/CUHK, Hieu Nguyen/VinAI, Duy C Hoang and Quang H Nguyen |
[05/2024] |
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[04/2024] |
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[03/2024] | Will serve as Invited Area Chair for NeurIPS and ACML 2024. |
[03/2024] | Will serve as Workshop Chair and Area Chair for ACML 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] | 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] | 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] | Accepted paper - [ICLR’24] on same-inference-time Adversarial Defense (congrats Quang H Nguyen and Tung Pham/VinAI). |
selected publications [full list]
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ECCV Flatness-aware Sequential Learning Generates Resilient BackdoorsIn European Conference on Computer Vision 2024
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ECCV Data Poisoning Quantization Backdoor AttackIn European Conference on Computer Vision 2024
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ICPR Composite Concept Extraction through BackdooringIn 27th International Conference on Pattern Recognition 2024
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UAI Cold-start Recommendation by Personalized Embedding Region ElicitationIn The Conference on Uncertainty in Artificial Intelligence 2023
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ACL-Findings Fooling the Textual Fooler via Randomizing Latent RepresentationsIn Findings of the Association for Computational Linguistics 2024
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ICLR Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial AttacksIn The Twelfth International Conference on Learning Representations 2024
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NeurIPS Iba: Towards irreversible backdoor attacks in federated learningAdvances in Neural Information Processing Systems 2024
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EAAI Backdoor attacks and defenses in federated learning: Survey, challenges and future research directionsEngineering Applications of Artificial Intelligence 2024
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SIGIR Asymmetric Hashing for Fast Ranking via Neural Network MeasuresIn 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023
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AAAI Defending backdoor attacks on vision transformer via patch processingIn AAAI Conference on Artificial Intelligence 2023
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NeurIPS Marksman Backdoor: Backdoor Attacks with Arbitrary Target ClassIn Thirty-Sixth Conference on Neural Information Processing Systems 2022
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CVPR One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional MatchingIn Conference on Computer Vision and Pattern Recognition 2022
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