Rokkam Nithin Kumar.
Internship Quality Assurance(RLHF) Intern
Focuses on ensuring the quality of the model’s performance and reliability by testing various scenarios and edge cases with Reinforcement Learning with Human Feedback (RLHF) and reporting bugs or inconsistencies.
Work Experience
Internship Quality Assurance(RLHF) Intern
Quizzy
Focuses on ensuring the quality of the model’s performance and reliability by testing various scenarios and edge cases with Reinforcement Learning with Human Feedback (RLHF) and reporting bugs or inconsistencies. Working with data scientists and machine learning engineers to understand model requirements. Providing insights based on testing results to improve model training. Suggesting enhancements based on observed patterns in feedback or testing. Staying updated with new QA methodologies and RLHF advancements.
Skills
Projects
RealTime - News - Recommendation - system
Developed a news recommendation system using Sentence-BERT embeddings, cosine similarity algorithms, and hybrid recommendation engine combining pre-trained MIND dataset with live News API integration. Built full-stack application using Fast API backend with RESTful APIs, React.js frontend with Vite build system. Integrated News API for live news data and Unsplash for dynamic, query-based imagery, enhancing user engagement.
Flood disaster prediction using satellite imagery
Developed a flood prediction system using satellite imagery with VGG16-based feature extraction and CNN classification. Applied KMeans clustering to group similar images and improve model training efficiency and accuracy. Trained the model on real flood datasets from Kerala, Mandi, and Godavari regions, achieving 78% classification accuracy.
Smart - Pest Detection
Developed a smart pest detection system using YOLOv5 deep learning model trained on the IP102 dataset. Attained 70%+ accuracy in multi-class pest detection and deployed the system through Ngrok for real-time mobile access with camera and upload functionality. Enabled real-time pest identification for smart farming applications.
Education
Computer Science and Engineering
Lovely Professional University
CGPA: 7.56
12th with Science
Narayana Junior College
Percentage: 80.6%
10th with Science
St. Joseph’s Sec School
CGPA: 86%