Research

At the Security and Artificial Intelligence Lab, our dedication lies in advancing the fields of Federated Learning and NLP Robustness.

In the field of Federated Learning, we investigate various aspects such as privacy concerns, novel algorithms, fairness considerations, and more. Our goal is to address the challenges associated with federated learning, including backdoor attacks, federated unlearning, personalized federated learning, and fairness. By conducting research in these areas, we strive to advance the state-of-the-art and promote the adoption of secure and privacy-preserving machine learning techniques. Join our research lab and be part of our vibrant community focused on cutting-edge research in Machine Learning, Federated Learning, Information Security, and Trustworthy AI.

In NLP Robustness, we focus on ensuring the Robustness of Natural Language Processing (NLP) models. Our research group explores topics such as adversarial attacks, defense mechanisms, model interpretability, and more. Through in-depth discussions and research, we aim to deepen our understanding of NLP robustness and contribute to the development of robust NLP systems.

We developed a web-based platform (shinyapps) to track and filter research papers on Federated Learning, Trustworthy AI, Large Language Models, and Multimodal Machine Learning, sourced from IEEE Xplore, ACM, ScienceDirect, Springer, OpenReview, arXiv, DBLP, and Google Scholar.

A web-based platform to track and filter research papers (https://mtuann.shinyapps.io/research-papers/).

Projects

  1. Project Title: “Improving NLP Applications in Low-resource Languages: One Country and One Use Case At A Time,” A Cross-College Project at VinUniversity, Hanoi, Vietnam. (Principal Investigator: Prof. Khoa D Doan, Co-PI: Dr. Dat Q Nguyen, Prof. Nidal Kamel, Prof. Kyunghwa Chung, Prof. Pranee Liamputtong, Prof. David Harrison, Prof. Dinh Nguyen, Prof. Kok-seng Wong, Doctor Paul O’Halloran)
  2. Project Title: “Green Serverless Computing for Resource-Efficient AI Training,” Center for Environment Intelligence, VinUniversity, Hanoi, Vietnam. (Principal Investigator: Prof. Kok-Seng Wong, Co-PI: Prof. Nguyen Van Dinh, Prof. Andrew Le Duy Dung)
  3. Project Title: “Smart Indoor Air Quality Control System for Healthier and Greener Buildings,” Vinuni-Illinois Smart Health Center Project. (Principal Investigator: Prof. Andrew Le Duy Dung & Prof. Vishal Verma, Co-PI: Prof. Kok-Seng Wong)
  4. Project Title: “Development of Brain-Computer Interface SW/HW Solutions,” Oct 2022- Oct 2024, Commercialization Project, Nazarbayev University, Kazakhstan. (Principal Investigator: Prof. Minho Lee, Co-PI: Prof. Kok-Seng Wong)
  5. Project Title: “Applying Machine Learning to Mitigate RF Impairments in Wireless Communications Systems,” Oct 2022- Dec 2023, Seed Grant, VinUniversity, Hanoi, Vietnam. (Principal Investigator: Prof. Kok Seng Wong)
  6. Project Title: “Privacy-Preserving, Robust, and Explainable Federated Learning Framework for Healthcare System,” Vinuni-Illinois Smart Health Center Project. (Principal Investigator: Prof. Kok Seng Wong, Co-PI: Prof. Khoa Doan)
  7. Project Title: “Privacy-Preserving Data Publishing for Autonomous Vehicles,” Jan 2021- Dec 2023, Seed Grant, VinUniversity, Vietnam. (Principal Investigator: Prof. Kok Seng Wong)

Research focus

  • Backdoor Attacks in Machine Learning/ Federated Learning
  • Depoloyment of Federated Learning on Edge Devices
  • Machine/ Federated Unlearning, Personalized Federated Learning, and Fairness
  • NLP Robustness

List of AI/Security Conference Deadlines

Seminar Series on Federated Learning and NLP Robustness

  • Weekly Seminar Group on Federated Learning: Every Wednesday, 10:30 AM - 11:30 AM Vietnam GMT+7
  • Weekly Reading Group on NLP Robustness: Every Wednesday, 9:30 AM - 10:30 AM Vietnam GMT+7

… and more.