SHORT BIO

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RESEARCH INTERESTS

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RESEARCH

My current research focuses on designing distributed/decentralized algorithms for personalized machine Learning for IoT, where only access to partial information is assumed. Supervised and unsupervised ML algorithms for decentralized setting are being investigated. Gossip learning will be compared to federated learning under different proposed conditions such as homogeneity and security vs trustiness in the network. Furthermore, we plan to extend our research to explore decentralized context-aware learning will be explored. Leveraging information from the surrounding context can be very useful. For this part of the project, a key assumption and motivation is that incorporating contextual information can improve the results of the model globally for the network, and more personally for the nodes. Towards that end, different applications and tasks are intended to be explored such as personalized recommendation and graph representation learning in decentralized fashion. Among the machine learning algorithms, the graph neural networks are to be explored, as well.   

PUBLICATIONS