Priyanka Kaswan
University of Maryland, College Park
pkaswan@umd.edu
Bio
Priyanka Kaswan is a Postdoctoral Research Associate at Princeton University, in the Department of Electrical and Computer Engineering, where she is working with Prof. Andrea J. Goldsmith. She completed her Ph.D. at University of Maryland, College Park under the supervision of Prof. Sennur Ulukus. Prior to that, she completed her Bachelors in Electrical Engineering from Indian Institute of Technology, Delhi. Her research focus is on timely information dissemination, gossip algorithms, misinformation in large networks, resource allocation and machine learning. She is the recipient of 2024 ECE Distinguished Dissertation Fellowship Award at University of Maryland. She was also awarded the George Harhalakis Outstanding Graduate Student Award 2023, the Outstanding Research Assistant Award in 2023, the Kulkarni Summer Research Fellowship in 2022, the Deans Fellowship in 2019, and she was selected as a Future Faculty Fellow by Clark School of Engineering at the University of Maryland.
Areas of Research
- Information and System Science
Enhancement and Robustness of Large Timely Networks
Next generation wireless networks are anticipated to be characterized by hyperconnectivity between humans and machines. This will result in dense interconnected infrastructure, supporting applications, such as, autonomous driving, blockchains, internet of things (IoT), augmented reality (AR), virtual reality (VR), and remote healthcare (RH) applications, that demand real-time interaction. Due to network resource limitations and increasingly dynamic data generated by various sources, it is imperative that all nodes within these networks have timely information, i.e., the latest updates about the sources at all times for their seamless functioning. The overarching theme of my research works is enhancement and robustness of timely information dissemination in large networks. In my research so far, my focus has been on timeliness via decentralized gossip algorithms in large-scale dense wireless network graphs and machine learning. In particular, I have worked on optimizing goal oriented metrics, such as, the age of information metric, which is a recently proposed measure of information freshness in time-sensitive applications. In my research, I show how the very mechanisms that make these networks efficient, such as age-based packet exchange and gossiping, can become vulnerabilities under certain attacks. I have investigated the unique threats that large time-sensitive systems are vulnerable to, such as timestomping attacks, jamming attacks, and the propagation of misinformation, and how gossiping in such networks both acts as a shield against threats and also enables adversaries to propagate attacks effectively.