About
Final-year B.Tech Computer Science student with hands-on experience building full-stack applications (React, FastAPI, PostgreSQL) and machine learning systems (PyTorch, scikit-learn, Transformers).
Built a multimodal content-verification system combining NLP, computer vision, and REST API design, and a full-stack learning platform with authentication and third-party API integration.
Comfortable working across the stack from database schema design to model integration to deployment.
Experience
Final-Year Major Project
Jun 2025 – Present
Developed a multimodal AI system to analyze URLs, text, images, and videos for misinformation, manipulated media, AI-generated content, and deepfakes.
Implemented DistilBERT-based NLP, ELA-based image forensics, MTCNN face detection, and MobileNetV3 for modality-specific content analysis.
Integrated OCR, Vosk speech-to-text, and semantic similarity to extract and verify textual information from images, videos, and online content.
Designed a centralized decision engine to combine analysis results and generate authenticity scores, confidence, risk levels, supporting/contradicting evidence, and explanations; validated the system with 11 passing automated tests.
PythonPyTorchFastAPI
Hybrid AI-Based Multimodal Fake Content Detection System
Network Anomaly Detection System
Jan 2026 – Apr 2026
Developed an unsupervised machine learning system to identify anomalous network traffic and potential intrusion patterns from network flow data using the CICIDS2017 dataset.
Implemented K-Means clustering and DBSCAN to group normal traffic and detect unusual network behavior without requiring labeled attack classes.
Applied PCA (Principal Component Analysis) for dimensionality reduction and visualized traffic clusters and detected anomalies using Matplotlib Performed data preprocessing, feature analysis, scaling, and exploratory analysis using Pandas and Scikit-learn to prepare network traffic data for anomaly detection.
PythonPandasScikit-learn
Network Anomaly Detection System
Machine Learning / Computer Vision Project
Aug 2025 – Aug 2025
Developed a computer-vision-based monitoring system to analyze student activity during online classes and help identify inattentive or inactive behavior.
Implemented real-time webcam-based face and activity detection to monitor student presence, attention, and activity during online learning sessions.
Used OpenCV and Python for video capture, image processing, face detection, and activity analysis on laptop-based systems.
Designed the system to record and present student activity information, providing instructors with insights into participation and engagement during online classes. Focused on practical application of computer vision for improving online classroom monitoring.
PythonOpenCVComputer Vision
Student Activity Monitoring System for Online Classes
Education
Teegala Krishna Reddy Engineering College, Hyderabad
B.Tech · Computer Science and Engineering
2023
Pragathi Junior College, Nalgonda
MPC
2021 – 2023
Certifications
Oracle Generative AI Certification
Oracle
Skills
OpenCVpandasscikit-learnTensorFlowPyTorchSQLMongoDBPostgreSQLResponsive Web DesignStreamlitReactAuthenticationREST APIFastAPICSS3HTML5JavaJavaScriptPython
