Sai Bhargav Damarla

Computer Science graduate · Machine Learning, Deep Learning & Generative AI

Vijayawada, Andhra Pradesh, India

@bhargav07

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About

Computer Science graduate with hands-on experience in Machine Learning, Deep Learning, NLP, and Generative AI. Experienced in building and evaluating Transformer, GAN, and YOLO-based models using Python, TensorFlow, PyTorch, and Hugging Face.

Education

2026

B.Tech in Computer Science and Engineering

SRM University, Andhra Pradesh

Computer Science · 2022 – 2026

Computer Science · 2022 – 2026
2022

Intermediate (MPC)

FIITJEE Junior College

MPC · 2020 – 2022

MPC · 2020 – 2022
2020

SSC

FIITJEE International School

2020

Skills

<3 months

C Oop Java Yolo LLMs NumPy Python Pandas OpenCV ChatGPT PyTorch JavaScript TensorFlow scikit-learn Transformers Deep Learning Machine Learning Computer Networks Operating Systems Natural Language Processing Data Structures and Algorithms CNN (Convolutional Neural Network) Generative Adversarial Networks (GANs) Generative AI (ChatGPT, Copilot, Gemini)

Tools / apps / platforms

<3 months

Git GitHub Hugging Face

Projects

Image Generation Using DCGAN

Python, TensorFlow

Developed and trained DCGAN models on MNIST, SVHN, and CelebA datasets for synthetic image generation. Designed custom generator and discriminator architectures optimized using Adam optimizer and Binary Cross Entropy loss. Achieved high-quality synthetic image generation after 100+ training epochs, validated through visual evaluation.
Real Time Object Detection in Aerial Surveillance Using YOLO Models

Python, PyTorch, YOLO Models

Implemented and trained multiple YOLO architectures (v8, v10, v11) for real-time object detection on aerial imagery using the VisDrone2019 dataset. Conducted comparative performance analysis using mAP@0.5, mAP@0.5:0.95, precision, and recall to evaluate detection accuracy. Identified YOLOv11 as the most effective model for detecting small and densely packed objects in aerial surveillance scenarios.
TITAN: Triple-Stream Integrated Attention Network for Title-Based Fake News Detection

Python, NLP

Developed a Transformer-based model for title-only fake news detection using semantic, emotional, and syntactic attention streams. Integrated a pre-trained DeBERTa-v3-base encoder with gated multi-view fusion and diversity-aware regularization. Fine-tuned the model using PyTorch and Hugging Face Transformers, with NumPy, Pandas, and Scikit-learn for preprocessing and evaluation. Achieved 85.0% accuracy on GossipCop and 96.41% on WELFake.

Courses & certifications

MongoDB · SmartBridge
Google AI Essentials · Google (Coursera)
The Joy of Computing Python · NPTEL
Cyber Security · Google (Coursera)

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