My research is primarily focused on four main thrusts in information sciences: big data analytics, sparsity-based learning, controlled sensing and verifiable planning. I seek to understand the fundamental limits of learning, data processing and acquisition, and reliable decision-making, as well as design scalable, robust and provable algorithms to extract knowledge from big data, and verifiable control policies in multi-agent and networked systems. I study the fundamental research questions in the context of numerous applications ranging from machine learning and video analytics, and security of cyberphysical systems, to optical imaging and brain signal processing.
ONR: Development of Diffraction-Free Space-Time Optical Beams (CoPI), Awarded June 2017.
NSF CAREER: Inference-Driven Data Processing and Acquisition: Scalability, Robustness and Control, Awarded Feb 2016.
DARPA UNCOVER: Unconstrained Natural light Coherency Vector-field-imaging by Exploiting Randomness (CoPI), DARPA, Awarded Feb 2016.
NSF CIF: Advanced Ion Channel Models for Neurological Signal Processing -- Theory and Application to Brain-Computer Interfacing (PI), NSF, Awarded August 2015.
ONR: Exploiting Multidimensional Classical Optical Entanglement for Enhanced Spatial Scene Recognition (CoPI), Office of Naval research (ONR), Awarded June 2014.
NSF CIF: A Unifying Approach for Identification of Sparse Interactions in Large Datasets (PI), NSF CCF/CIF, Awarded August 2013.
NSF I/UCRC Multi-functional Integrated System Technology (MIST) (CoPI), Sep 2014.
Fundamental Limits of Sparse Signal and Information Processing, In-House Award (UCF), Feb. 2013.