Drishti Chauhan

WORK HISTORY

EDUCATION

ABOUT DRISHTI CHAUHAN

First class Electronics and Communications Engineering undergraduate undertaking an MSc Robotics Engineering degree the University of Manchester. I focus on autonomous systems and edge computing. My work integrates AI-driven automation with real-world robotics, solving complex challenges in healthcare, agriculture, and disaster management. My dissertation is the automation of Cranial Window Installation via Machine Learning and a Robotic Arm- Engineered a modified U-Net model with ResNet34 for medical image segmentation, achieving sub-millimeter precision in cranial surgeries using a CVR038 robotic arm- Achieved an IoU score of 0.81, outperforming existing models. Summary of experience- Developed an end-to-end deep learning model for kidney disease classification, from data ingestion and preprocessing to model training and deployment. This project involved using CNNs, transfer learning, and MLflow for experiment tracking. Deployed the model with Docker and AWS, creating a scalable pipeline for real-time predictions- Developed a Python-based industrial weighing system at Udaipur Cement Work Ltd, improving accuracy by 98% and reducing errors by 90%- Designed a lightweight face recognition algorithm for edge devices, boosting performance by 40% and reducing errors by 99%- Created and implemented an IoT-enabled smart agriculture system and obstacle-avoidance robot at Vyorius, improving field productivity. Key Achievements- I have written on robotics, AI, IoT, and machine learning, reaching a global technical audience- Secured in funding, organized 20+ workshops and trained 500+ students through workshops on IoT programming, machine learning, and PCB design- Won the MINeD 2023 Hackathon, developing a 99% accurate facial recognition system for crowd management and employee tracking. Summary of technical and leadership skills- Generative AI: Completed advanced training in Langchain and Huggingface, deploying models and optimizing pipelines- AI & Machine Learning: Specialized in deep learning, U-Net models, and residual networks for medical image segmentation tasks- Computer Vision: Developed 2D/3D recognition algorithms, achieving 99% accuracy- IoT & Edge Computing: Designed and implemented LoRa-based disaster management systems with 98% uptime- Robotic Manipulators: Led the development of a robotic arm-integrated LEO Rover for disaster response and environmental monitoring- Leadership: Organized multiple workshops for students and professionals, facilitating learning in advanced technical domains.