| 2023 | ICML | Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs. | Raif M. Rustamov, Subhabrata Majumdar |
| 2020 | KDD | Characterizing and Learning Representation on Customer Contact Journeys in Cellular Services. | Shuai Zhao, Wen-Ling Hsu, George Ma, Tan Xu, Guy Jacobson, Raif M. Rustamov |
| 2020 | SECON | Cellular Network Traffic Prediction Incorporating Handover: A Graph Convolutional Approach. | Shuai Zhao, Xiaopeng Jiang, Guy Jacobson, Rittwik Jana, Wen-Ling Hsu, Raif M. Rustamov, Manoop Talasila, Syed Anwar Aftab, Yi Chen, Cristian Borcea |
| 2018 | AAAI | Interpretable Graph-Based Semi-Supervised Learning via Flows. | Raif M. Rustamov, James T. Klosowski |
| 2017 | CISS | Distributed learning of human mobility patterns from cellular network data. | Tong Wu, Raif M. Rustamov, Colin Goodall |
| 2014 | CVPR | Stable and Informative Spectral Signatures for Graph Matching. | Nan Hu, Raif M. Rustamov, Leonidas J. Guibas |
| 2014 | ICML | Wasserstein Propagation for Semi-Supervised Learning. | Justin Solomon, Raif M. Rustamov, Leonidas J. Guibas, Adrian Butscher |
| 2014 | MICCAI | Compact and Informative Representation of Functional Connectivity for Predictive Modeling. | Raif M. Rustamov, David Romano, Allan L. Reiss, Leonidas J. Guibas |
| 2013 | CVPR | Graph Matching with Anchor Nodes: A Learning Approach. | Nan Hu, Raif M. Rustamov, Leonidas J. Guibas |
| 2007 | SGP | Laplace-Beltrami eigenfunctions for deformation invariant shape representation. | Raif M. Rustamov |