Matrix and Tensor Factorization and their Applications

  1. S. Rambhatla and J. Haupt (2013). Semi-Blind Source Separation via Sparse Representations and Online Dictionary Learning. IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, pp. 1687-1691. doi 10.1109/ACSSC.2013.6810587blog slides pdf
  2. S. Rambhatla, N. D. Sidiropoulos, and J. Haupt (2018). TensorMap: Lidar-based Topological Mapping and Localization via Tensor Decompositions. IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, pp. 1368-1372. doi 10.1109/GlobalSIP.2018.8646665 pdf
  3. S. Rambhatla, X. Li and J. Haupt (2019). NOODL: Provable Online Dictionary Learning and Sparse Coding.International Conference on Learning Representations (ICLR), New Orleans, LA, USA. pdf Poster
    πŸ† Travel Award
  4. S. Rambhatla, X. Li, and J. Haupt (2020). Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning. (Manuscript Under Review). pdf

Matrix Demixing and their Applications

  1. S. Rambhatla, X. Li and J. Haupt (2016). A Dictionary Based Generalization of Robust PCA. IEEE Global Conference on Signal and Information Processing (GlobalSIP), Washington, DC, pp.1315-1319.doi 10.1109/GlobalSIP.2016.7906054 slides pdf
    πŸ† National Science Foundation (NSF) Travel Award
  2. S Rambhatla, X. Li and J. Haupt (2017). Target-Based Hyperspectral Demixing via Generalized Robust PCA. IEEE Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, pp. 420-424. doi 10.1109/ACSSC.2017.8335372demo pdf
    πŸ† Finalist, Student Best Paper Award
  3. X. Li, J. Ren, S. Rambhatla, Y. Xu, and J. Haupt (2018). Robust PCA via Dictionary Based Outlier Pursuit. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, pp. 4699-4703. doi 10.1109/ICASSP.2018.8461593 pdf
  4. S. Rambhatla, X. Li, J. Ren and J. Haupt (2020). A Dictionary-Based Generalization of Robust PCA With Applications to Target Localization in Hyperspectral Imaging. IEEE Transactions on Signal Processing, 68, pp.1760-1775. doi 10.1109/TSP.2020.2977458 pdf demo

Interpretability of Deep Learning Models

  1. L. Trinh, M. Tsang, S. Rambhatla, Y. Liu (2020). Interpretable Deepfake Detection via Dynamic Prototypes. (Manuscript Under Review). pdf
  2. M. Tsang, S. Rambhatla, Y. Liu (2020). How does this interaction affect me? Interpretable attribution for feature interactions. (Manuscript Under Review). pdf

Physics Inspired Machine Learning and Spatiotemporal Data Analysis

  1. S. Seo, C. Meng, S. Rambhatla, Y. Liu (2020). Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning. (Manuscript Under Review). pdf

COVID-19 Prediction and Misinformation

  1. N. Kamra, Y. Zhang, S. Rambhatla, C. Meng, Y. Liu (2020). PolSIRD: Modeling Epidemic Spread under Intervention Policies and an Application to the Spread of COVID-19. (Manuscript Under Review).
  2. K. Sharma, S. Seo, C. Meng, S. Rambhatla, Y. Liu (2020). COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations. (Journal Under Review at the Journal of Computational Social Science). pdf
    πŸ—£ Media Coverage

Other Publications

  1. S. Rambhatla (2012). Semi-blind source separation via sparse representations and online dictionary learning. Master’s Thesis pdf
  2. S. Rambhatla (2019). Provably Learning from Data: New Algorithms and Models for Matrix and Tensor Decompositions. Doctoral Dissertation pdf