Research Interest

I am an AI researcher with 8+ years of experience in Deep Learning, Machine Learning, Computer Vision, and NLP. My research, presented at leading AI conferences such as ICML, ICCV, ICLR, UAI, and IJCAI, covers a broad range of topics, including deepfake detection, multi-modal learning, generative AI, LLM safety, domain adaptation, and transfer learning. I have 3+ years of experience mentoring 200+ students at Monash University and organizing Kaggle competitions to foster student engagement.

"It Always Seems Impossible Until It Is Done." - Nelson Mandela

Hobbies

I am passionate about sports, particularly badminton, soccer, and table tennis. I also enjoy playing the guitar and continually expanding my knowledge through new courses.

Contact

I look forward to engaging in stimulating discussions and potential collaborations with you. Please feel free to contact me through:

Selected Publications

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Coming soon Cycle Class Consistency with Distributional Optimal Transport and Knowledge Distillation for Unsupervised Domain Adaptation

Tuan Nguyen, Van Nguyen, Trung Le, He Zhao, Quan Hung Tran, and Dinh Phung.

UAI, 2022.

[Paper]

Coming soon TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport

Tuan Nguyen, Trung Le, Nhan Dam, Quan Hung Tran, Truyen Nguyen, and Dinh Phung.

IJCAI, 2021.

[Paper]

Coming soon MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning

Tuan Nguyen, Trung Le, He Zhao, Quan Hung Tran, Truyen Nguyen, and Dinh Phung.

UAI, 2021.

[Paper][Code][Slides]

Coming soon LAMDA: Label Matching Deep Domain Adaptation

Trung Le, Tuan Nguyen, Nhat Ho, Hung Bui, and Dinh Phung.

ICML, 2021.

[Paper][Code]

Coming soon STEM: An approach to Multi-source Domain Adaptation with Guarantees

Van-Anh Nguyen, Tuan Nguyen, Trung Le, Quan Hung Tran, and Dinh Phung.

ICCV, 2021.

[Paper][Code]

Coming soon Maximal divergence sequential autoencoder for binary software vulnerability detection

Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague, Olivier De Vel and Lizhen Qu.

ICLR, 2018.

[Paper][Code]

Coming soon Deep cost-sensitive kernel machine for binary software vulnerability detection

Tuan Nguyen, Trung Le, Khanh Nguyen, Olivier de Vel, Paul Montague, John Grundy, and Dinh Phung.

PAKDD, 2020.

[Paper][Code][Slides]

Coming soon Fuzzy Kernel Stochastic Gradient Descent Machines

Tuan Nguyen, Phuong Duong, Trung Le, Anh Le, Viet Ngo, Dat Tran and Wanli Ma.

IJCNN, 2016.

[Paper]

News

September 2024: I have started a new postdoctoral position at the Qatar Computing Research Institute in Doha, Qatar.

January 2024: I have received my conferral letter and completed my PhD program.

August 2023: I am thrilled to have received a Research Assistant position at Monash University.

May 2022: Our paper “Cycle Class Consistency with Distributional Optimal Transport and Knowledge Distillation for Unsupervised Domain Adaptation” has been accepted to UAI 2022.

July 2021: Our paper “STEM: An approach to Multi-source Domain Adaptation with Guarantees” has been accepted to ICCV 2021.

May 2021: Our paper “LAMDA: Label Matching Deep Domain Adaptation” has been accepted to ICML 2021.

May 2021: Our paper “MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning” has been accepted to UAI 2021.

April 2021: Our paper “TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport” has been accepted to IJCAI 2021.

March 2021: I have advanced to a Ph.D. degree due to my notable research achievements.

March 2020: I commenced my Master’s degree at Monash University, Australia, under the supervision of Prof. Dinh Phung, Dr. Trung Le, and Dr. He Zhao.

Professional Services

I am honored to serve as a reviewer for prestigious conferences such as AAAI, AISTATS, ICCV, CVPR, ICML, and NeurIPS.

Teaching

2023: Teaching Associate - Head Tutor, FIT5215 Deep learning, Semester 2, Monash University.

2022: Teaching Associate - Head Tutor, FIT5215 Deep learning, Semester 2, Monash University.

2022: Teaching Associate, FIT3181 Deep learning, Semester 2, Monash University.

2021: Teaching Associate - Head Tutor, FIT5215 Deep learning, Summer Semester A, Monash University.

2021: Teaching Associate, FIT5215 Deep learning, Semester 2, Monash University.


Based on the code of Ankit Sultana