Research

SPONSORED PROJECTS, CONTRACTS AND GRANTS

  1. Cognira Data Science Research Lab (2021-current) Objective: Sales forecasting with deep learning; Amount of grants: $40,000; Role: Lead PI
  2. Motion Intelligence (2020-current) Objective: Human pose detection and application in cloud edge computing; Amount of grants: $50,000; Role: co-PI
  3. Alcon Data Science Research Lab (2020-2021) Objective: Optimizing manufacturing process with deep learning; Amount of grants: $60,000; Role: co-PI
  4. Equifax Data Science Lab (2016-2018) Objective: Deep learning in biometric verification; Amount of grants: $75,000; Role: Lead Researcher
  5. Blue Ridge Global Data Science Research Lab (2015-2016) Objective: Conducting industry forecasting; Amount of Contract: $30,000; Role: Lead Researcher
  6. Kimberly Clark project (2015-2016) Objective: Challenges of incontinence among elderly people; Amount: $30,000; Role: Researcher.

 

PUBLICATIONS

Journals:

  1. Le, L., Xie, Y., & Raghavan, V. V. (2021). Deep KNN and KNN Loss, Fundamenta Informaticae (in-press)
  2. 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., 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).
  2. 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. 
  3. 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.
  4. 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 
  5. 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 
  6. Le, L., Xie, Y. (2019). Deep Learning with SAS® and Python: A Comparative Study. In Proceedings of SAS Global Forum 2019.
  7. 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.
  8. 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. 
  9. 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 
  10. 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 
  11. 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 
  12. 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

Pending Patent:

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

In Review:

  1. Li L., Peltsverger, S., Zheng, J., Le, L., Handlin, M., (2021) Retrieving and Classifying LinkedIn Job Titles for Alumni Career Analysis. The 22nd ACM Annual Conference on Information Technology Education.
©