Suvrat
Jain

• Led the development of a vision system integrating YOLOv8 and UNet segmentation models to achieve precise clothing dismantling and optimize downstream processes.
• Leveraged Scikit-learn and Python to build scalable image classification pipelines using TensorFlow. Implemented distributed training across multiple instances for improved efficiency.
• Implemented advanced data augmentation techniques, resulting in a 10% improvement in IoU score for the semantic segmentation model, showcasing expertise in enhancing data quality and accuracy.
• Developed and built ETL and data augmentation pipelines, to expand dataset by 30% and achieve a 12% lift in model accuracy due to improved generalization.
• Developed ready to use garment processing pipeline for efficient workflow integrated with vision system.
• Deployed a vision system prototype, configuring industrial grade cameras and lighting and establishing TCP/IP network communication for seamless integration with a control system.

• Developed performant CNN models for automobile part categorization (98.2% on 10k+ images, using OpenCV). Demonstrated expertise in techniques with broad AI applications, including image processing and transfer learning.
• Boosted automobile part classification accuracy by 73% by fine-tuning an Inception v3 model (TensorFlow), demonstrating skill in transfer learning for rapid solution development.
• Implemented a Siamese Network for similarity learning, combining distance metrics and statistical analysis techniques. Achieved 98% accuracy on out-of-training data, significantly improving model generalization.
• Developed a multi-stage ETL pipeline for object detection/classification for a sustainable remanufacturing vision system, reducing manual inspection time by 30% and boosting object classification accuracy by 10%.
• Enhanced ML pipeline efficiency through research and experimentation. Built clear data visualizations (Tableau, Power BI) to convey model behavior and drive improvements. (Project Demo)

• Deployed 100+ email campaigns for brands like Estée Lauder and MAC Cosmetics using Salesforce Marketing Cloud, delivering an average click-through rate of 25%.
• Developed automated test suites (Python, Selenium) for web applications, reducing manual testing effort by 24% and accelerating release cycles.
• Built API test automation scripts to validate functionality and data integrity. Increased test coverage by 25% for core back-end services.
• Performed SQL queries to extract and analyze user behavior data from Adobe Analytics.
• Analyzed and worked with customer data for audience segmentation and targeting, for 50+ email marketing campaigns.

This paper explores the application of image-based machine learning in the automotive industry for identifying and sorting used parts in remanufacturing processes. By training a neural network with images of various automotive parts and utilizing transfer learning and object detection algorithms, the study demonstrates a highly accurate automated sorting system capable of classifying automotive parts with over 95% accuracy, offering a potential solution to labor-intensive and error-prone manual sorting processes. https://doi.org/10.1002/9781394214297.ch39

• Hackathons: Eventify-U | Developed a Python-based web app for personalized event recommendations and alerts to nearby activities using machine learning.
• Applied Statistics
• Neural Networks and Architecture
• Time Series Analysis and Forecasting
• Data Visualization and Analytics

Specialization: Embedded Systems and Microcontrollers