Department of Electrical Engineering & Computer Science
Department of Electrical Engineering & Computer Science


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.

  • Big data analytics and machine learning
  • Statistical learning and signal processing
  • Verifiable planning in reinforcement learning
  • Controlled sensing for inference
  • Optical 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.

Research Books

University of Central Florida