I am a graduate student currently pursuing my Ph.D. in the Remote Sensing Ecohydrology Group following the completion of my masters in Environmental Fluid Mechanics and Hydrology at Stanford University. After graduating with a bachelors in Civil engineering in South India, I worked for two years in the oil and gas industry before commencing my graduate studies.

Honors & Awards

  • NASA Earth and Space Science Fellow, NASA (2018)
  • J.N. Tata Fellowship, J. N. Tata endowment (2016)
  • K.C. Mahindra Fellowship, K.C. Mahindra Educational Trust (2016)
  • Institute Blues, Indian Institute of Technology Madras (2014)
  • Larsen & Toubro ECC Endowment Fellow, Indian Institute of Technology Madras (2014)

Professional Affiliations and Activities

  • Vice-president of Education, Stanford Toastmasters (2017 - 2018)
  • President, Hindu Students Association (2018 - Present)

Education & Certifications

  • Master of Science, Stanford University, Civil and Environmental Engineering (2018)
  • Bachelor of Technology, Indian Institute of Technology Madras, Civil Engineering (2014)

Personal Interests

In my free time I ride my bicycle to remote areas and write about my experiences here

Current Research and Scholarly Interests

My research focuses on developing machine learning algorithms to remotely monitor vegetation health by using microwave remote sensing. I am interested in answering questions such as:
- How can we leverage the vast amounts of data our satellites are capturing to understand the health of our forests?
- How can we obtain information about hydraulic health of vegetation with little or no ground-collected data


  • Effects of heterogeneity on group behaviour, Ouellette Lab, Stanford University (October 10, 2016 - March 31, 2017)

    In nature, group behaviour exists widely in the form of flocks, swarms, herds and so on. This groups rarely consist of purely homogenous members. As part of this project, we studied the effects of physical and relationship based heterogeneity among the members of a group. We also numerically modelled the growth disorder in a flock due to individual asymmetries.


    stanford, california

  • Remotely Sensed Vegetation Optical Depth as an Indicator of Drought-driven Tree Mortality, Stanford University (April 1, 2017 - July 30, 2018)


    Stanford University

  • A nonparametric approach for estimating high-resolution fuel moisture content across the Western United States using synthetic aperture radar, Stanford University (July 31, 2018 - Present)

    Fuel moisture content (FMC) is a key determinant of wildfire ignition and propagation- and thus fire risk. However, FMC has high spatial heterogeneity, making it cumbersome and expensive to gather field measurements at sufficient density. Alternatively, microwave backscatter measurements from synthetic aperture radar (SAR) can be used. These measurements are sensitive to canopy moisture and thus contain information about fuel moisture. Previous efforts to estimate FMC from SAR use highly detailed radiative transfer models that rely on a number of geometric properties such as stand height, crown width, leaf density, orientation and so on. Such properties vary widely across species and forests. As a result, existing models are impossible to apply at large scales. In this project, I am developing a nonparametric model powered by machine learning - an artificial neural network (ANN) - to estimate FMC from SAR backscatter.


    Stanford University

Work Experience

  • Wireline Field Engineer, Schlumberger (11/16/2014 - 6/1/2016)

    Krishna worked in Schlumberger as a Wireline Field Engineer. After obtaining his theoretical training in geology and petrophysics and practical exposure to field scenarios, Krishna worked in North-East India for two of the biggest clients there. On the field, Krishna designed, executed, and delivered several service projects related to reservoir characterization and production.


    Abu Dhabi, UAE

All Publications