I am a farmer and Chief AI Scientist at UrbanKisaan. Earlier, I was the Chief Scientist at Cropin, where I led the Cropin AI Labs team. The focus is on integrating Earth Observations and AI to revolutionize agriculture and promote sustainable land and resource use. My multidisciplinary team includes Earth observation scientists, data scientists, field scientists, and agronomists.

I also led the Data Science and Machine Learning team at Corteva Agriscience (formerly Dow-DuPont Agriculture) in Hyderabad. My experience also includes roles as a Data Analytic Scientist at Shell Technology Center Bangalore and as a Researcher and Technical Manager at Samsung Advanced Institute of Technology (SAIT).

I currently serve as an adjunct faculty member at AMMACHI Labs at Amrita Vishwa Vidyapeetham, where I advise and mentor Master's and Ph.D. students. I am part of the apex committee responsible for setting up the four AI Centers of Excellence in India and part of the committee member of the AWS Impact Computing Project with the Harvard Data Science Initiative (HDSI). Additionally, I am a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE).

My academic background includes a Ph.D. in Computer Vision, Control, Signal, and Image Processing from Ariana, a joint research team of INRIA, CNRS, and UNS INRIA; a Master's in Electrical Engineering from Texas A&M University, College Station; and a Bachelor's degree in Electrical Engineering from the Indian Institute of Technology (IIT) Roorkee. I was also a contractual researcher during my postdoctoral years at the Pasteur Institute in Paris.

Beyond my professional work, I am passionate about biodiversity and practice natural and organic farming at my own farm. I am also a certified Heartfulness Meditation trainer.

🔬 Google Scholar Profile: View my research

In 2022, I recognized that scaling Natural Farming Techniques and Agroforestry requires open-sourcing all related work in agriculture, satellite imaging, science, and machine learning. I created several repositories on GitHub to provide blueprints for individuals and organizations to reproduce these methods. By sharing my experiments and learnings from my farm, Roc Tranquil, I aim to foster collective learning and avoid repeating mistakes, especially those impacting nature.

Here are some of my notable projects across AI, remote sensing, materials science, and agriculture:

• Natural Farming: Experiments and blueprints for grid-wise farming setups.
• Plant Disease Classification: AI-based classification of plant diseases using hyperspectral imaging.
• Cloud and Shadow Masking: Code for IEEE IGARSS 2023 on "Cirrus Cloud and Shadow Masking in Optical Satellite Using Deep Learning."
• Spatio-Temporal Segmentation: AI-driven methods for spatio-temporal segmentation in satellite imagery.

• AI4Materials: Machine learning-driven approaches for materials discovery and characterization.

• Satellite Imaging and ML Tutorial: A comprehensive guide to satellite imaging and ML techniques in hyperspectral space.
• Spatial-Spectral Classifier for HSI: Classifier for hyperspectral imaging data.
• AR-STAR: Advanced remote sensing techniques for agricultural monitoring.

• Kalman Filter for Phase Estimation: Implementation of Kalman filtering for phase estimation in signal processing.

• Aksara: Small Language Model for Climate Smart Advisory for farmers across five countries and for nine crops.

I am always open to collaborations in AI, remote sensing, agriculture, and materials science. If you are a student, educator, or researcher interested in contributing, collaborating, teaching, or learning, feel free to reach out to me through LinkedIn. Let's work together to advance sustainable agriculture and technology.