Contact

Phone: 470-578-4311

Email: lle13@kennesaw.edu

Office Location: J 339

Office Hours:
office hours TBD

Research

In brief, my scholarship focuses on designing and developing core deep learning and artificial intelligence technologies, as well as specialized DL/AI frameworks for specific tasks in critical application domains such as next generation networks, education, and transformative industrial applications. 

SPONSORED PROJECTS, CONTRACTS AND GRANTS

  1. Suntrust Research Fellowship (2022-current) Objective: Enhancing Reliability and Confidentiality for Information Sharing over 5G Radio Access Network Slicing. Role: co-PI
  2. Cognira Data Science Research Lab (2021-2022) Objective: Detection of Halo effects in retailing; Amount of grants: $40,000; Role: Lead PI
  3. Motion Intelligence (2020-2022) Objective: Human pose detection and application in cloud edge computing; Amount of grants: $50,000; Role: co-PI
  4. Alcon Data Science Research Lab (2020-2021) Objective: Anomaly detection in production lines; Amount of grants: $60,000; Role: co-PI
  5. Equifax Data Science Lab (2016-2018) Objective: Deep learning in biometric verification; Amount of grants: $75,000; Role: Lead Researcher
  6. Blue Ridge Global Data Science Research Lab (2015-2016) Objective: Graph-based multivariate forecasting model; Amount of Contract: $30,000; Role: Lead Researcher
  7. Kimberly Clark project (2015-2016) Objective: Challenges of incontinence among elders; Amount: $30,000; Role: Researcher.

PUBLICATIONS

US Patent:

  1. Xie, Y., Le, L., Dual Deep Learning Architecture for Machine-Learning Systems, U.S. Patent Application No. 16/141,152, Equifax Inc.

Journals:

  1. Le, L., Tran, D. (2023). Metric Learning for Detection of Large Language Model Generated Contents (under review)
  2. Le, L., Nguyen, T. (2022). Efficient embedding VNFs in 5G network slicing: a deep reinforcement learning approach. (under review)
  3. Nguyen, T., Ambarani, K., Le, L., Djordjevic, I., Zhang, Z. (2022). A Multiple-Entanglement Routing Framework for Quantum Networks (under review)
  4. Le, L., Nguyen, T. (2022). DQRA: Deep Quantum Routing Agent for Entanglement Routing in Quantum Networks. IEEE Transactions on Quantum Engineering
  5. Nguyen, T., Le, L. (2021). An Efficient Hybrid Webshell Detection Method for Webserver of Marine Transportation Systems. IEEE Transactions on Intelligent Transportation Systems, 1-13. 
  6. Le, L., Xie, Y., & Raghavan, V. V. (2021). KNN Loss and Deep KNN. Fundamenta Informaticae, 182(2), 95-110.
  7. Le, L., Xie, Y. (2019, April), Deep Embedding Kernel, Neurocomputing (pp. 292-302), Elsevier., Volume 339, ISSN 0925-2312, doi: https://doi.org/10.1016/j.neucom.2019.02.037.

Chapters in Books:

  1. Xie, Y., Le, L., Zhou, Y., & Raghavan, V. (2018). Deep Learning for Natural Language Processing. In V. Gudivada & C. Rao, Handbook of Statistics (pp. 317-328). Elsevier. Retrieved from http://www.sciencedirect.com/science/article/pii/S0169716118300026  doi: https://doi.org/10.1016/bs.host.2018.05.001

Proceedings:

  1. Le, L., Hebbar, S., Nguyen, M. (2024). A Low-Resource Framework for Detection of Large Language Model Contents. To appear in ICLR 2024
  2. Le, L., Nguyen, T., Suo, K., He, J. (2022). 5G Network Slicing and Drone-Assisted Applications: A Deep Reinforcement Learning Approach. Mobicom 2022 
  3. Le, L., Nguyen, T. (2022). Entanglement Routing for Quantum Networks: A Deep Reinforcement Learning Approach. In the 2022 IEEE International Conference on Communications
  4. Le, L., Xie, Y., Charkravaty, S., Hales, M., Johnson, J., & Nguyen, T., (2021, December). Analyzing Students’ Concentration Levels from Webcam Feed. In 2021 IEEE International Conference on Big Data (Big Data). IEEE.  
  5. Li, L., Peltsverger, S., Zheng, J., Le, L., & Handlin, M. (2021, October). Retrieving and Classifying LinkedIn Job Titles for Alumni Career Analysis. In Proceedings of the 22nd Annual Conference on Information Technology Education (pp. 85-90). 
  6. Le, L., Mallapragada, S., Hebbar, S., (2021). One-Class Self-Attention Model for Anomaly Detection in Manufacturing Lines.  In proceedings of 2021 Intelligent Systems Conference (to-appear).
  7. Chakravarty, S., Xie, Y., Le, L., Johnson, J., & Hales, M. (2021, September). Comparison Between Active and Passive Attention Using EEG Waves and Deep Neural Network. In International Conference on Brain Informatics (pp. 287-298). Springer, Cham. https://link.springer.com/chapter/10.1007/978-3-030-86993-9_27 
  8. Le, L., Xie, Y., & Alagapan, S., (2020, December). Deep Pose Alignment. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 5366-5372). IEEE. 
  9. Hada, R. J., Jin, M., Xie, Y., & Le, L. (2019, September). Link Prediction Based Minimum Cost and Balanced Partition of Large Online Social Networks. In 18th IEEE International Symposium on Network Computing and Applications (NCA). IEEE.
  10. Le, L., Xie, Y., & Raghavan, V. V. (2018, December). Deep Similarity-Enhanced K Nearest Neighbors. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 2643-2650). IEEE. doi: https://10.1109/BigData.2018.8621894 
  11. Le, L., & Xie, Y. (2018, December). Recurrent Embedding Kernel for Predicting Stock Daily Direction. In 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT) (pp. 160-166). IEEE. doi: https://10.1109/BDCAT.2018.00027 
  12. Le, L., Xie, Y. (2019). Deep Learning with SAS® and Python: A Comparative Study. In Proceedings of SAS Global Forum 2019.
  13. Le, L., Xie, Y. (2019). Modeling with Deep Recurrent Architectures: A Case Study of Using SAS and Python for Deep Learning. In Proceedings of SAS Global Forum 2019.
  14. Gadidov, B. & Le, L. (2018). A Case Study of Mining Social Media Data for Disaster Relief: Hurricane Irma, In Proceedings of SAS Global Forum 2018 https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2018/2695-2018.pdfY. 
  15. Xie, Y., Le, L., & Hao, J. (2017, May). Unsupervised deep kernel for high dimensional data. In 2017 International Joint Conference on Neural Networks (IJCNN) (pp. 294-299). IEEE. doi: https://10.1109/IJCNN.2017.7965868 
  16. Le, L. Hao, J. Xie, Y., & Priestley J. (2017). Deep Kernel: Learning the Kernel Function from Data Using Deep Neural Network. In Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies https://dl.acm.org/citation.cfm?id=3006312 
  17. Xie, Y., Pooja, C., & Le, L. (2017). Visualization of High Dimensional Data in a Three-Dimensional Space. In Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies https://dl.acm.org/citation.cfm?id=3006340 
  18. Le, L. & Priestley, J. (2017, April). Using the OPTGRAPH Procedure: Transformation of Transactional Data into Graph for Cluster Analysis. In Proceedings of SAS Global Forum 2017. http://support.sas.com/resources/papers/proceedings17/1065-2017.pdf
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