Research

My research group seeks to understand how much information and intervention are fundamentally necessary for intelligent systems to perceive, infer, and act. We address this question through sparse signal processing, developing mathematical frameworks for resource-efficient sensing and control. By exploiting sparsity, we aim to identify the essential information in increasingly complex sensing systems and determine when and where intervention is necessary to achieve desired objectives. This research provides theoretical foundations for intelligent systems that operate with fewer measurements, less communication, and fewer physical interventions, with applications ranging from autonomous driving and structural health monitoring to large-scale communication networks.

Some of my past and ongoing projects are on the following problems:

Sensing/Mapping

Environment-awareness for intelligent vehicles: Sensor fusion for automotive application using spatial sparsity and correlation
Image recovery: Sparse Bayesian dictionary learning algorithms for image denoising; one-bit compressed sensing for image compression
Structural health monitoring: Sparse anomaly mapping and sensor placement
Missing data: Sparse recovery and guarantees

Network Control

Controllability of network opinion using a manipulative agent with limited (sparse) influence on the network
Deep reinforcement algorithms for anomaly detection with sparse sensing
System analysis (observability, controllability, and stabilizability) with sparsity constraints

Wireless Communication

IRS-aided wireless channel estimation exploiting angular sparsity
Online Bayesian algorithms for wideband OFDM wireless channel estimation exploiting sparsity in the lag domain
Spectrum cartography algorithms for estimating the intensity map of a radio frequency map exploiting spatial sparsity

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ORCID

Publons

Grants/Project funding:

2026: Energy-Efficient Signal Processing and Computing
- 4TU.NIRICT Community Funding Grant with Anastasia Lavrenko, Ghayoor Gillani (Twente), and Chang Meng (TU Eindhoven)
2025-26: Control-Communication Co-Design for Complex Networked Systems
- IIT Delhi and TU Delft Collaborative Research Grant 2025 with Gourab Ghatak (IIT Delhi)
2024-2030: Atmospheric Turbulence Informed Machine Learning for Laser Satellite Communications (DAILSCOM)
- NWO TTW Open Technology Programme with Rudolf Saathof (AE), Justin Dauwels, Sukanta Basu (CiTG)
2023-28: Signal Processing for Environment-Aware Radar (SPEAR)
- Top consortium for Knowledge and Innovation (TKI) programme jointly with Nitin Myers and NXP semiconductors
2022-23: Statistical Inference and Control Design for Sparsity-constrained Linear Dynamical Systems
- IISc and TU Delft Collaborative Research Grant 2022 with Chandra R. Murthy (IISc, Bangalore)

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