I am a Vellore Institute of Technology (VIT) graduate, honored as the Best Outgoing Student of 2022 with a CGPA of 9.42. My academic journey was fueled by a deep passion for AI research, leading to the publication of 8 research papers under the guidance of distinguished professors and collaborators. My technical prowess extends beyond the classroom, with victories in over 10 internationally recognized hackathons, a World Finalist spot at Google Hashcode, and a feature in Forbes India as one of the Best Engineering Students. Currently, as a Data Scientist at Walmart, I continue to push the boundaries of AI, contributing multiple research papers and securing two patents. These experiences have shaped my strong commitment to advancing AI research and pursuing a career in academia.
| July 31st, 2024 | Promoted to Data Scientist III at Walmart |
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| May 12th, 2024 | Grand Winner at Hack Bangalore' 24 |
| Jul 15th, 2023 | Paper Early Disease Detection Of Leaves Using Deep Learning And Drones-Cyber Physical Systems Approach This paper presents a novel drone design for capturing images of the rear side of leaves to detect diseases such as Mildew and Cabbage Looper, which are often missed by traditional aerial imagery. By using deep learning models like VGG16 and ResNet, the system classifies leaf health, achieving a validation accuracy of 98%. Early detection through this approach reduces pesticide use and improves crop yield quality. Published in SSRN Journal |
| Nov 4th, 2022 | Paper Does Social Media Sentiment Predict Bitcoin Trading Volume? This research investigates the predictive power of public sentiment on Bitcoin trading volume. Using a novel sentiment analysis pipeline for Bitcoin-related tweets, it achieves state-of-the-art accuracy and incorporates both textual and non-textual features. The study reveals a nonlinear relationship between sentiment and Bitcoin trading volume, identifying an optimal predictive horizon and providing a foundation for future research on cryptocurrency market dynamics. Published in International Conference on Information Systems (ICIS) |
| Jul 26th, 2022 | Joined Walmart as Data Scientist II |
| Jul 23rd, 2022 | Winner of Rakuten's Flagship Hackathon - Rakathon'22 |
| Mar 8th, 2022 | Paper Developing A Transdisciplinary Citizen Science Tool For Experiential Learning In Undergraduate Education: Squirrel Life In A Nutshell The Squirrel Life project uses a transdisciplinary approach to address educational inequities, combining Higher Education, Ecology, Data Science, and Software Engineering. By focusing on Active Learning, Accessibility, and Usability, the project promotes inclusivity and experiential learning. The citizen science mobile app engages students and communities in environmental monitoring, with its public release set to expand its reach. Published in International Technology, Education and Development Conference |
| Mar 2nd, 2022 | Paper Design of waste management system using ensemble neural networks This paper presents a deep neural network-based method for waste detection, classification, and size quantification, aimed at improving waste management. Integrated into a mobile app, it enables users to geotag trash via images, notifying nearby cleaners. Experimental results show the method achieves over 90% accuracy, outperforming existing approaches on public datasets. Published in Designs Journal |
| Jan 18th, 2022 | Joined Walmart as Data Science Intern |
| Dec 31st, 2021 | Winner of Route Mobile RAPID International Hackathon |
| Dec 9th, 2021 | Winner of Optum Stratethon Season 3 International Hackathon |
| Aug 19th, 2021 | Paper Adoption of precision agriculture by detecting and spraying herbicide using UAV Farmers in India face challenges in adopting UAV technology for weed management due to small farm sizes, low incomes, and high costs. A Drone-as-a-Service (DaaS) model using UAVs with VIS/NIR sensors and precise sprayers offers a cost-effective solution, achieving up to 95.4% accuracy in weed detection. Published in Basrah Journal of Agricultural Sciences |
| Mar 14th, 2021 | Google HashCode World Finalist: International Rank 30 (AIR 3) |
| Feb 16th, 2021 | Paper A Comprehensive Analysis on the Efficient Mechanisms to Detect Obstructive Sleep Apnea Using AI and Heuristic Algorithms Our study reviews recent advancements in predicting Obstructive Sleep Apnea (OSA) using AI algorithms and signal analysis. A self-developed VAD algorithm achieved 97% accuracy, with other methods reaching around 86%. The research identifies current limitations to guide future improvements in OSA detection. Published in International Journal of Current Research and Review |
| Dec 11th, 2020 | Runners Up of Optum Stratethon Season 2 International Hackathon |
| Oct 16th, 2020 | Paper AI crop predictor and weed detector using wireless technologies: a smart application for farmers The paper presents a smart agriculture system using IoT, WSN, and AI for crop recommendations and drone-based weed detection, enhancing farm productivity. It aims to reduce labor, crop failures, and weed-related damage. Published in Arabian Journal for Science and Engineering |
| Jul 31st, 2020 | The Site is Live |