Sirisha Rambhatla

Theory-guided Machine Learning for the Real World

RTH 321, 3710 McClintock Ave

Los Angeles, CA 90089

I am a Postdoctoral fellow at the Melady Lab mentored by Prof. Yan Liu in the Computer Science Department, Viterbi School of Engineering, at the University of Southern California. I undertook my doctoral studies with Prof. Jarvis Haupt in the Department of Electrical and Computer Engineering at the University of Minnesota-Twin Cities.

Curriculum Vitae

     

Areas of Interests

| Statistical Machine Learning | Design of Provable Learning Algorithms | Interpretability of Deep Learning Models | AI for Healthcare | Spatiotemporal Data Analysis |

news

Jun 14, 2021 DL4Burn: Burn Surgical Candidacy Prediction using Multimodal Deep Learning accepted to AMIA Annual Symposium 2021! :star:
Jun 1, 2021 Predicting Burn Surgical Candidacy Using Deep Learning on Photographic Images accepted to Plastic Surgery: the Meeting 2021! :rotating_light:
May 16, 2021 Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling accepted to KDD 2021! :rotating_light:
May 14, 2021 I am now the proud recipient of the USC WiSE Merit Award for Excellence in Postdoctoral Research! :trophy:
May 5, 2021 PolSIRD: Modeling Epidemic Spread under Intervention Policies accepted to Journal of Healthcare Informatics Research 2021! :rotating_light:

selected publications

  1. ICLR
    🏆 Travel Award
    NOODL: Provable Online Dictionary Learning and Sparse Coding
    Rambhatla, S., Li, X., and Haupt, J.
    International Conference on Learning Representations (ICLR) 2019
  2. NeurIPS
    Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
    Rambhatla, S., Li, X., and Haupt, J.
    Advances in Neural Information Processing Systems (NeurIPS) 2020
  3. NeurIPS
    How does this interaction affect me? Interpretable attribution for feature interactions
    Tsang, M., Rambhatla, S., and Liu, Y.
    Advances in Neural Information Processing Systems (NeurIPS) 2020
  4. TSP
    A Dictionary-Based Generalization of Robust PCA With Applications to Target Localization in Hyperspectral Imaging
    Rambhatla, S., Li, X., Ren, J., and Haupt, J.
    IEEE Tran. on Signal Processing 2020