Presentations

This poster was presented in ICASSP 2025- IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India. In this study, we address the challenge of packet based information routing in large-scale wireless communication networks. We approach this scenario by framing the problem as a statistical learning problem, where each node in the network relies only on the local data. Our exploration focuses on the idea of opportunistic routing, which exploits the broadcast nature of wireless communication to select the optimal relay node and transmit information packets to the destination node via multiple relay nodes. We present a distributed optimization method based on state augmentation (SA) that aims to maximize the total information on different source nodes of the network. Our formulation of the problem deploys graph neural networks (GNNs) to perform graph convolution on the topological connections between the network nodes. Using unsupervised learning, we derive optimal routing policies for the source nodes across multiple flows from the GNN output. Numerical results show the superiority of our proposed method by comparing a GNN model trained against standard algorithms. Please find the link to the paper here.

This paper was presented in ICASSP 2024- IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, South Korea. We consider the problem of routing network packets in a large-scale communication system where the nodes have access to only local information. We formulate this problem as a constrained learning problem, which can be solved using a distributed optimization algorithm. We approach this distributed optimization using a novel state-augmentation (SA) strategy to maximize the aggregate information packets at different source nodes, leveraging dual variables corresponding to flow constraint violations. The construction is based on graph neural networks (GNNs) that employ graph convolutions over the underlying communication network topology. We devise an unsupervised learning algorithm to transform the output of the GNN architecture into optimal routing decisions. The proposed method takes advantage of only the local information available at each node and efficiently routes the desired packets to the destination. Please find the link to the presentation slides here and the paper can be found here.

This poster was presented in the Annual User meeting in the Singh Center of Nanotechnology at the University of Pennsylvania, Philadelphia. The poster summarizes my overall research on the design and fabrication of an OTFT with complete process flow. The electrical characterization and properties of the transistor were studied keeping the ink as PEDOT:PSS.

This poster was presented in the Annual Review meeting of Intel Science and Technology Center (ISTC) on Wireless Autonomous Systems (WAS) at the Head quarters of Intel in Santa Clara, California. It summarizes my findings on the implementation of Joint Design of Communication and Control for a fleet of F1/10 cars.