Notable Projects
Optimal Sensor Placement
Using Machine learning to learn the optimal number and placement of sensors in an IoT environment. Analysed past sensor data and extended concepts of Diensionality Reduction and Sparsity to reduce the number of sensors and devise an optimal arrangement.
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Content Based Medical Image Retrieval
Worked on training a Convolutional Neural Network to classify organs present in PET/CT/MRI scans anatomically. Wrote code to source and process medical image data accordingly. Used a transfer learning approach making use of the RensNet architecture.
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Predicting Socioeconomic Well-being using Machine learning and Remote Sensing
Ongoing project, training Deep learning models to estimate the socioeconomic status of a region from high resolution satellite imagery. Working on using segmentation algorithms to identify features indicative of an index pertaining to socioeconomic well being.
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IoT enabled Smart Parking System
Parking system to bring about an efficient and dynamic system for parking vehicles in such complexes where the time spent searching for a parking spot is eliminated and congestion is reduced. IoT model was adopted to dynamically relay signals for real time monitoring.
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