About Me
CV Engineer with a 58% EPE reduction on a production robotics dense correspondence model (UFM/DINOv2), achieving a new SOTA of 1.40 EPE via DoRA + STRING RoPE, outperforming full encoder fine-tuning while reducing trainable parameters by over 90%. Open-source contributor to Google Keras Hub and experienced in developing production DICOM AI pipelines.
Skills & Technologies
Work Experience
Deep Vision and Model Optimization Intern
Achieved a 58% EPE reduction through extensive ablation studies on UFM, discovering optimal feature extraction points and validating performance improvements. Implemented DoRA and STRING RoPE techniques to achieve a new state-of-the-art EPE of 1.40 while significantly reducing trainable parameters. Resolved critical MLOps bottlenecks by architecting efficient data pipelines.
DICOM AI/ML Engineer Intern
Engineered a safety monitoring layer for live DICOM streams and implemented algorithms for data drift detection to prevent model failures. Developed a validation pipeline for 3D medical segmentation models to ensure compliance with regulatory standards.
Projects
End-to-End 3D Medical Segmentation Framework for the Medical Segmentation Decathlon
Developed a config-driven MLOps framework supporting multiple 3D architectures, implementing custom training strategies to address class imbalance and achieving high performance on medical segmentation tasks.
AI Study Assistant: Personalized LLM-Powered Tutor
Created a retrieval-augmented generation system that transforms handwritten and typed notes into an interactive study platform, leveraging advanced models for contextual Q&A and automated quiz generation.
Built a TPU-Distributed Semantic Segmentation Benchmarking Framework
Implemented various segmentation models from scratch on the Cityscapes dataset using a modular TPU-distributed training pipeline, developing custom loss functions and evaluation metrics.
Education
BE
Artificial Intelligence & Machine Learning