Swetha Reddy Ganta
AI Engineer with expertise in Machine Learning and Computer Vision
Swetha Reddy Ganta
AI Engineer
Location
Buffalo, New York, USA
Languages
Professional Summary
AI Engineer with comprehensive experience in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. Demonstrated expertise in developing intelligent systems with published research in multilingual NLP, fraud detection, and Computer Vision.
Passionate about leveraging AI to solve complex problems at scale, with strong technical foundation in Python, TensorFlow, PyTorch, and data analysis. Seeking to apply research experience and technical skills to develop innovative AI solutions.
My journey in AI began during my undergraduate studies where I developed a keen interest in how machines can learn from data. This fascination led me to pursue specialized research in multilingual NLP and Computer Vision, resulting in multiple published papers and practical applications that solve real-world problems.
Research Experience
Artificial Intelligence Research Intern
Kakatiya Institute of Technology and Science
Feb 2023 – Jun 2023
Tokenization Challenges in Multilingual GPT
Optimized tokenization in multilingual language models, improving efficiency by 60% for non-English languages. Developed language-specific preprocessing pipelines, reducing token usage per prompt from 70-100 to 18-25.
View Research Paper →Machine Learning Research Intern
Kakatiya Institute of Technology and Science
Mar 2023 – Jul 2023
Fraud Detection in Automobile Insurance Claims using Machine Learning Algorithms
Engineered ML pipeline for fraud detection achieving 78% accuracy and 81% AUC using ensemble methods. Implemented data balancing and feature engineering techniques to optimize model performance on imbalanced datasets.
View Research Paper →Computer Vision Research Intern
Kakatiya Institute of Technology and Science
Jan 2021 – Aug 2021
A Quantitative Analysis of Basic vs. Deep Learning-based Image Data Augmentation Techniques
Conducted research on deep learning-based image augmentation techniques, achieving 98.57% accuracy on MNIST dataset. Implemented and evaluated various data augmentation strategies to improve model robustness and generalization.
View Research Paper →Education Details
University at Buffalo, State University of New York
Master of Science in Engineering Science
Artificial Intelligence
Key Courses
Kakatiya Institute of Technology and Science, Warangal
Bachelor of Technology
Computer Science
Key Courses
Additional Information
Open Source Contributions
Active contributor to ML/AI projects on GitHub with 500+ commits, focusing on improving accessibility and performance of open-source AI tools.
TensorFlow Contributions
Contributed to documentation and example notebooks for TensorFlow's NLP modules.
PyTorch Ecosystem
Developed custom datasets and data loaders for computer vision applications.
Interests
Certifications
Deep Learning Specialization
Coursera - deeplearning.ai
Completed 5-course specialization covering neural networks, CNN, RNN, and ML project structuring.
TensorFlow Developer Certificate
Professional certification demonstrating proficiency in building TensorFlow models.