N. K. Malakar

The University of Texas at Dallas, University at Albany, State University of New York

Papers

Total Citations

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About

N. K. Malakar’s research lies at the intersection of environmental sensing, machine learning, and robotics, with a focus on improving the accuracy and utility of sensor data. Their most impactful work, “Estimation and bias correction of aerosol abundance using data-driven machine learning and remote sensing” (2012, 17 citations), addresses a critical public health challenge by enhancing estimates of aerosol optical depth (AOD)—a key metric for air quality—through machine learning-based bias correction. This contribution demonstrates Malakar’s ability to integrate computational methods with remote sensing to produce more reliable environmental data. In related work, Malakar explores sensor modeling to overcome hardware limitations, as seen in “Modeling a Sensor to Improve Its Efficacy” (2013, 5 citations), where they show how low-cost sensors can be optimized for robotic systems. Their foundational study on the “Spatial Sensitivity Function of a Light Sensor” (2009, 3 citations) provides a framework for quantifying detector sensitivity, with implications for sensor design and calibration. Though early in their career, Malakar’s work has already influenced air quality monitoring and robotics, highlighting a commitment to making sensor technology both more accurate and accessible.