I’m an Automotive Data Scientist at UST Global, where I work on blending machine learning with physics-based modeling to create intelligent, data-driven solutions for automotive systems. My work focuses on developing 3D representations from abstract 2D sketches, designing simulation test benches, and validating AI-powered algorithms that enhance performance and automation in real-world vehicle applications. Before this, I was a Research Intern at IIT Madras in the Advanced Geometric Computing Lab, where I worked on geometry-informed deep learning for point cloud denoising — bridging the gap between geometry processing and modern neural networks. I’m deeply passionate about AI for science and engineering, with research published in IEEE and Elsevier (Computers & Graphics). My academic projects — like CUDA-accelerated Siamese Neural Networks for ECG classification and LSTM Autoencoder-based anomaly detection — reflect my interest in building efficient, deployable models that push the boundaries of what AI can achieve in healthcare and engineering. With strong foundations in Python, PyTorch, and TensorFlow, I enjoy working on projects that combine mathematical intuition, data-driven insights, and practical implementation. At my core, I’m driven by curiosity and the desire to turn complex ideas into tangible, high-impact AI solutions that make technology more intelligent and accessible.