Nhu-Y Tran-Van

Highly accomplished AI researcher focused on advanced solutions for edge AI and IoT. Possesses expertise in developing and optimizing deep learning models for resource-constrained environments, with successful applications across diverse domains. Brings valuable experience in academic instruction and student mentorship, driven by a passion for leveraging advanced technology to solve complex, real-world problems.

• 2022 | Certificate of merit for excellent graduates

Awarded by: University of Information Technology - Vietnam National University, Ho Chi Minh City (UIT-VNU-HCM).

• 2017-2021 | UIT scholarship for excellent academic results in eight semesters in a row

Awarded by: University of Information Technology - Vietnam National University, Ho Chi Minh City (UIT-VNU-HCM).

• 2021 | Certificate of Merit for students with good achievements in Scientific Research

University of Information Technology - Vietnam National University, Ho Chi Minh City (UIT-VNU-HCM).

[1] Nhu-Y Tran-Van and Kim-Hung Le. "A Multimodal Skin Lesion Classification through Cross-Attention Fusion and Collaborative Edge Computing." Journal of Computerized Medical Imaging and Graphics (2025). ISI-Q1, Impact Factor 5.4. Accepted and planned for publication in June 2025.

[2] Ngoc-Truong Nguyen, Nhu-Y Tran-Van, Cao-Thi Nguyen, and Khanh-Hoi Le-Minh. "WleAtNet: A Lightweight Deep Learning Model and Data Framework for Wireless Link Estimation in Resource-Constrained Internet of Things," in 2025 International Conference on Computational Collective Intelligence (ICCC). Rank B CORE2021. Accepted and planned for presentation in November 2025. 

[3] Nhu-Y Tran-Van, Hoang-Trung Le Pham, Huy-Tan Thai, and Kim-Hung Le. "Investigating the Vulnerability of Deep Neural Network to Bit-Flip Attacks in Collaborative Inference Systems," in the 2025 Conference on Information Technology and its Applications (CITA). Accepted and planned for presentation in July 2025. 

[4] Minh-Hao Ho, Nhu-Y Tran-Van, and Kim-Hung Le, “A multi-input bi-LSTM autoencoder model with wavelet transform for air quality prediction,” in 2024 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), IEEE, 2024, pp. 1–6. DOI: 10.1109/MAPR63514.2024.10660818. 

[5] Nhu-Y Tran-Van, Huy-Tan Thai, Khanh-Hoi Le-Minh, and Kim-Hung Le, “Towards real-time outdoor air quality prediction using a hybrid model based on internet of things devices,” in 2023 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), IEEE, 2023, pp. 683–688. DOI: 10.1109/COMNETSAT59769.2023.10420823. 

[6] Huy-Tan Thai, Nhu-Y Tran-Van, Khanh-Hoi Le-Minh, and Kim-Hung Le, “An edge-based fire detection system for real-time IoT applications,” in 2023 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT), IEEE, 2023, pp. 646–651. DOI: 10.1109/COMNETSAT59769.2023.10420588. 

[7] Nhu-Y Tran-Van, Nhat-Tuan Pham, and Kim-Hung Le, “Ls-tfp: A LSTM-based traffic flow prediction method in intelligent internet of things,” in Proceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021, Volume 1, Springer, 2022, pp. 23–33. DOI: https://doi.org/10.1007/978-981-19-2350-0_3. 

[8] Nhat-Tuan Pham, Nhu-Y Tran-Van, and Kim-Hung Le, “Ls-spp: A LSTM-based solar power prediction method from weather forecast information,” in 2021 8th NAFOSTED Conference on Information and Computer Science (NICS), IEEE, 2021, pp. 144–148. î DOI: 10.1109/NICS54270.2021.9701529. 

[9] Huy-Tan Thai, Nhu-Y Tran-Van, and Kim-Hung Le, “Artificial cognition for early leaf disease detection using vision transformers,” in 2021 International Conference on Advanced Technologies for Communications (ATC), 2021, pp. 33–38. DOI: https://doi.org/10.1109/ATC52653.2021.9598303.