Sheikh Shams Azam bio photo

Sheikh Shams Azam

M.S/Ph.D Student, Purdue ECE

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Conference

[C4] Azam, Sheikh Shams, Seyyedali Hosseinalipour, Qiang Qiu, and Christopher G. Brinton. “Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank?” In International Conference on Learning Representations (ICLR), 2022. [paper] [preprint] [poster] [slides]

[C3] Azam, Sheikh Shams, Taejin Kim, Seyyedali Hosseinalipour, Christopher G. Brinton, Carlee Joe-Wong, and Saurabh Bagchi. “Can we Generalize and Distribute Private Representation Learning?” In International Conference on Artificial Intelligence and Statistics (AISTATS), 2022. [preprint] [poster] [slides]

[C2] Lin, Frank Po-Chen, Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton, Nicolo Michelusi. “Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Sampling.” In IEEE Global Communications Conference (GlobeCom), 2021. [preprint] [slides]

[C1] Azam, Sheikh Shams, Manoj Raju, Venkatesh Pagidimarri, and Vamsi Chandra Kasivajjala. “CASCADENET: An LSTM Based Deep Learning Model for Automated ICD-10 Coding.” In Future of Information and Communication Conference (FICC), 2019. [paper] [preprint] [slides]

Journal

[J3] Hosseinalipour, Seyyedali, Sheikh Shams Azam, Christopher G. Brinton, Nicolo Michelusi, Vaneet Aggarwal, David J. Love, and Huaiyu Dai. “Multi-Stage Hybrid Federated Learning over Large-Scale Wireless Fog Networks.” IEEE/ACM Transactions on Networking (TON), 2020. [paper] [preprint]

[J2] Lin, Frank Po-Chen, Seyyedali Hosseinalipour, Sheikh Shams Azam, Christopher G. Brinton, and Nicolo Michelusi. “Semi-decentralized federated learning with cooperative D2D local model aggregations.” IEEE Journal on Selected Areas in Communications (JSAC) (2021). [paper] [preprint]

[J1] Azam, Sheikh Shams, Manoj Raju, Venkatesh Pagidimarri, and Vamsi Chandra Kasivajjala. “Q-Map: Clinical Concept Mining from Clinical Documents.” World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering (2018). [paper] [preprint] [slides]

Workshop

[W1] Azam, Sheikh Shams, Taejin Kim, Seyyedali Hosseinalipour, Christopher G. Brinton, Carlee Joe-Wong, and Saurabh Bagchi. “A Generalized and Distributable Generative Model for Private Representation Learning.” In NeurIPS Workshop on Deep Generative Models and Downstream Applications, 2021. [paper] [poster]