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My primary research interests belong to the general area of information and communication theory.
More specifically, I am interested in the coding of information-bearing signals for transmission over noisy communication networks. This problem is addressed at two levels. One objective is to understand and investigate the Shannon theoretic aspect of this problem -- i.e., to determine the fundamental limits of how efficiently one can encode information and still be able to recover it with negligible loss. Another vital objective is to develop efficient coding techniques and algorithms for achieving reliable data transmission over wireless networks.

I also have a long-standing interest in probability problems and their applications, including random processes with reinforcement, contagion models and generalized Polya urns, stochastic modelling and analysis of network epidemics, game theory, and effective bounds for the probability of a union of events under partial information.

Research Areas:

Information Theory and Communications: The coding of information bearing signals for transmission over noisy communication channels is studied. One objective is to establish fundamental Shannon-theoretic limits (via coding theorems) on how e ciently one can encode information and still be able to recover it with negligible loss. Another objective is to develop e ective coding techniques and algorithms for achieving reliable data communication over wireless networks.

Reinforcement and Contagion in Networks: Reinforcement processes and contagion phenomena are ubiquitous in real life. Examples include error bursts in communication channels, disease propagation, computer malware spread, cascading failures in nance and rumor dissemination in social networks. Stochastic reinforcement models and contagion mitigation strategies for epidemics and opinion dynamics in networks based on generalized Polya urns are investigated.

Information-Theoretic Machine Learning: As AI technology gets widely deployed in society, it is of critical importance to ensure that machine learning algorithms are both fair (by not discriminating based on users sensitive attributes) and private (by not disclosing users personal information) while remaining robust and accurate. Information-theoretic tools such as the information bottleneck method, the privacy funnel and the rate-privacy function are employed to develop and analyze data privacy and AI fairness mechanisms in representation learning algorithms that protect against the leakage of private data and guarantee unbiased outcomes. Generalized loss functions via the judicious use of information-theoretic measures are also examined for improving the performance and stability of deep learning generative adversarial networks.