Prathamesh Dinkar

Spearheaded custom benchmarking initiatives for SOTA algorithms across classification, object detection, and segmentation, ensuring top-tier performance and accuracy. Engineered SOTA AI models for 3D segmentation of custom 3D characters, achieving an exceptional mIOU of 99%, setting a new standard in precision. Pioneered AI-driven 2D and 3D generation models, delivering results with remarkable fidelity on custom data, pushing the boundaries of content creation. Developed proof-of-concept (POC) solutions for AI-based motion capture and custom 3D character animation, demonstrating innovative approaches to dynamic character modeling.

Implemented SOTA CNN models, including VGG16, ResNet50, and YOLO V3, to achieve high-performance benchmarks in various projects. Designed and built custom CNN models using TensorFlow and PyTorch, incorporating ensemble methods and transfer learning to boost model performance by 15%. Applied a range of machine learning algorithms, including regressions, tree-based models, and anomaly detection techniques, to address diverse analytical challenges. Executed anomaly detection algorithms and clustering techniques, successfully reducing Non-Revenue Water by 55%, showcasing significant operational impact. Proficient in data visualization tools like Tableau, Power BI, and Python libraries such as Seaborn, Plotly, and Folium, transforming complex data into actionable insights.