BTech CSE’26 ||AI ML||Ex-Intern @DRDO
I am currently pursuing BTech in computer science and engineering. Hard work and determination are the main aspects in defining who i am.I discovered this in my high school when I managed my football and my academics and also acing in both the fields.From this experience,I realised that the only limit to my success is myself.I carry this ideology with me and will for the rest of my life.Prioritised long term goals of mine are to develop a career as a space scientist.I am confident that my skillset, competitive edge, attention to details and passion in space research will contribute in me achieving these career goals.I value a harmonious work environment, effective leadership and creating meaningful relationships. These values are vital in the career that I am striving for. I look forward to connecting with you here on LinkedIn
Defence Research and Development Organisation (DRDO)
2 mos
SheCodes
6 mos
Google Cloud Arcade Facilitator Program
6 mos
iTrain Asia Pte. Ltd. (Singapore)
4 mos
mentee at Code; without barriers
Headstarter AI
2 mos
Girl Up Coders
2 mos
Defence Laboratory, Jodhpur (DRDO)
06/2025 to 07/2025 · Jodhpur, India
Collaborated on a geospatial visualization platform using the ArcGIS API to render large-scale map datasets; developed interactive modules for coordinate-based polygon and line rendering; integrated search functionality for geospatial elements; and improved performance by streamlining complex geospatial layer integration and data rendering.
Bachelor of Technology - BTech, Computer science and engineering
Nov 2022 - Dec 2026
High school, Science with CS(python)
Apr 2020 - Mar 2021
88.20%
High School/Secondary Certificate Programs
Apr 2018 - Mar 2019
91.0%
Bachelor of Technology, Computer Science Engineering
Expected 06/2026
Current CGPA: 8.4
12th, PCM
03/2021
Scored 88.20%
10th
03/2019
Collaborated on a custom Convolutional Neural Network architecture to classify synthetic facial images. Built a data pipeline using the 140K Real and Fake Faces dataset with 80/10/10 training, validation, and testing splits; applied data augmentation; and achieved 94% training accuracy and 87.65% test accuracy, evaluated with Precision, Recall, and F1-Score.
Developed a health-tech platform for predicting diseases including diabetes and heart disease from clinical parameters. Implemented Logistic Regression and Random Forest using Scikit-Learn and managed feature scaling and normalization pipelines.
In progress. Researching CNN architectures for land-use classification using multi-spectral imagery and implementing pipelines to process and normalize large-scale satellite datasets for terrain identification.