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Education

PES UNIVERSITY

Nov 2022 - May 2026

B.tech in Computer Science and Engineering

IIT MADRAS

Jan 2023 - May 2024

Diploma in Data Science

Christ Junior College

Sept 2020 - May 2022

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Employs advanced deep-learning techniques, specifically Convolutional Neural Networks (CNNs), to analyze images and videos in real-time as they are uploaded to social media platforms.
Prioritizing prevention over remediation. Instead of waiting for deepfake content to cause harm and then addressing it, DeepWatcher stops it at the source, ensuring that manipulated content never reaches the public domain.
DeepWatcher enhances user trust and engagement, ultimately benefiting both individuals and social media platforms alike.

Designed gene sequencing algorithms to analyze the genetic sequences of prospective parents, providing a comprehensive understanding of potential genetic diseases their offspring might inherit.
Examining both parental and grandparental genetic data, it offers a thorough assessment of recessive genetic disorders, enabling a more accurate prediction of disease probabilities in future generations.
Instead of manual examination of nucleotide combinations, Unhigh-gene employs algorithms for each genetic disease, streamlining the detection process. This automated computational approach significantly reduces the time and effort required by geneticists, ensuring faster and more efficient analysis of genetic disorders.

Enables citizens to securely report incidents through a decentralized private blockchain platform (Hyperledger fabric), ensuring anonymity and protection for witnesses.
Utilizing blockchain technology, the project establishes an immutable and transparent record of evidence collection storage, increasing inclusion in court cases.
Leveraging Differentiated Case Flow Management (DCM) by prioritizing cases based on their resource requirements and processing time using ML and NLP.

Developed a real-time fraud detection system for digital transactions using streamlit and Natural Language Processing (NLP).
Implemented algorithms to analyze user-generated reports to detect fraudulent activity with high accuracy.
Utilized NLP techniques to extract relevant information from text data, enabling timely intervention to prevent fraudulent transaction.

Engineered TensorFlow-based model image classification to detect and predict presence of esophageal cancer related diseases.
Trained the model on a diverse dataset of medical images to achieve high accuracy in identifying abnormalities indicative of esophageal cancer.
Implemented advanced image processing techniques to extract key features and patterns, enabling early detection and diagnosis of potential health risks.

Developed machine learning model to identify criminal transactions within Bitcoin network, enhancing security measures in financial transactions.
Leveraged advanced techniques to analyze transaction patterns and detect anomalous behavior associated with illegal activities.
Implemented the model to assist in preventing fraudulent transactions and ensuring compliance with regulatory standards in cryptocurrency transactions.

Proficient in Python, R, Javascript, C.
Interested in Data Science, Machine Learning, AI, NLP, Computer Vision, Blockchain and Deep-learning.