• Building scalable web and mobile apps with React, Node.js, Express, and MongoDB, supporting real-time data processing.• Designed and launched the official website of EV Bots (evbots.tech), enhancing the company’s online presence.• Enhancing fleet management with intuitive app interfaces and a teleoperations control panel for remote monitoring and control of the bot.

• Engineered and fine-tuned convolutional neural networks (CNNs) within the TensorFlow framework, resulting in a 20% increase in detection accuracy for precise identification of oil spills in satellite and drone imagery.• Implemented real-time monitoring capabilities, resulting in a 32% increase in the speed of detection and response to emerging oil spill incidents, thereby mitigating potential environmental hazards.• Collaborated with experts, achieving an 18% improvement in discriminating between actual spills and false positives.

• Led a 12-member team to develop autonomous modules for traffic sign and pedestrian detection, and steering control, using advanced computer vision, neural networks, and control algorithms.• Integrated LiDAR and cameras for environmental perception, ensuring seamless interaction with vehicle actuators.• Secured the winning position at Formula Hybrid 2021 in New Hampshire, USA, demonstrating cutting-edge module development.

• Developed a user-friendly front-end with React.js providing customers with an interactive shopping experience.• Managed state with Redux/React Context API for smooth data flow and built RESTful APIs with Express.js, boostingcustomer satisfaction by 32%.• Designed MongoDB schema for efficient product, user, and order management, reducing query response time by 25%.• Wrote unit and integration tests for the backend API, resulting in a 41% reduction in post-deployment bugs, ensuring thereliability of the application.

• Implemented cutting-edge Time Series Forecasting (TSF) techniques, with a focus on enhancing rainfall prediction accuracy in Austin, TX, leveraging Transformer models.• Integrated and meticulously analyzed local rainfall datasets, contributing to a nuanced understanding of temporal patterns and trends, leading to an impressive 15% enhancement in predictive performance.• Conducted thorough comparisons of various TSF architectures, showcasing proficiency in RNN, LSTM, GRU, and Transformer models. Transformer models demonstrated a notable 25% advantage in predictive precision over other architectures.