At the forefront of innovation, our team (Data Science Lab at Phenikaa University) is dedicated to transforming data into actionable insights through cutting-edge research and development. Our multidisciplinary team explores the intersection of deep learning, data mining, and AI applications to solve real-world problems. Our research spans diverse domains:
Developing intelligent systems for materials discovery and informatics.
Building advanced platforms for big-data analytics and storage. Innovating in smart vision systems and speech recognition technologies.
Advancing large vision-language models for multimodal data analysis
Van-Quyen Nguyen, Phuoc-Anh Le, Phi Long Nguyen, Tien-Lam Pham, Thi Viet Bac Phung, Kostya S Novoselov, Laurent El Ghaoui, “Learnable features for predicting properties of metal-organic frameworks with deep neural networks”, (2024), Cell Reports Physical Science, 5, 8.
Van-Quyen Nguyen, Viet-Cuong Nguyen, Tien-Cuong Nguyen, Nguyen-Xuan-Vu Nguyen, Tien-Lam Pham, “Pairwise interactions for Potential energy surfaces and Atomic forces with Deep Neural network”, (2022), J. Com. Mat, 209, 111379
Tien-Cuong Nguyen, Van-Quyen Nguyen, Van-Linh Ngo, Quang-Khoat Than, Tien-Lam Pham, “Learning hidden chemistry with deep neural network”, (2021) J. Com. Mat. 200, 110784.
Tien-Lam Pham, Duong-Nguyen Nguyen, Minh-Quyet Ha, Hiori Kino, Takashi Miyatake, Hieu-Chi Dam, “Explainable machine learning for materials discovery: predicting the potentially formable Nd–Fe–B crystal structures and extracting the structure–stability relationship”, (2020) IUCrJ
Pham Tien Lam, Nguyen Van Duy, Nguyen Tien Cuong, “Machine Learning Representation for Atomic Forces and Energies”, (2020) VNU Journal of Science: Mathematics-Physics
Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Hiori Kino, Takashi Miyake, Hieu-Chi Dam “Ensemble learning reveals dissimilarity between rare-earth transition binary alloys with respect to the Curie temperature”, (2019) J. Phys. Mater. 2 034009
Van-Doan Nguyen, Tien-Lam Pham, Hieu-Chi Dam, “Application of materials informatics on crystalline materials for two-body terms approximation”, 2019, Computational Materials Science 166, 155-161
Tien-Lam Pham, Tran-Thai Dang, Van-Doan Nguyen, Hiori Kino, Takashi Miyake, Hieu-Chi Dam, “Learning Materials Properties from Orbital Interactions”, (2019) Journal of Physics: Conference Series 1290 (1), 012012.
Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Anh-Tuan Nguyen, Hiori Kino, Takashi Miyake, Hieu-Chi Dam, “A regression-based model evaluation of the Curie temperature of transition-metal rare-earth compounds”, (2019) Journal of Physics: Conference Series 1290 (1), 012009
Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Hiori Kino, Takashi Miyake, DAM Hieu-Chi, “Ensemble learning reveals dissimilarity between rare-earth transition binary alloys with respect to the Curie temperature”, (2019), J. Phys. Mater. in press. https://doi.org/10.1088/2515-7639/ab1738
Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Tuan-Dung Ho, Truyen Tran, K Takahashi, Hieu-Chi Dam, “Committee machine that votes for similarity between materials”, (2018), IUCrJ 5 (6).
Introduction to Computing
Deep Learning
Computer Vision
Introduction to AI and DS
Machine learning
Big-Data
Data Visualization