Navodita Mathur

Navodita Mathur

AI Research intern at Dugree

Navodita Mathur

AI Research Intern, Machine learning Engineer
Location: San Mateo, CA
Contact: +1(412) 954-7877
Email: navoditamathur1998@gmail.com

About Me

I am an AI researcher with expertise at the intersection of AI, life sciences, and geospatial analytics. With over a year of experience in machine learning and three years in software development, my work focuses on building AI-driven solutions for biomedical and environmental challenges. My key projects include:

Agentic AI for Disease Prediction – Integrating EHR, medical imaging, and knowledge graphs to enhance clinical decision-making.
Medical Image Analysis – Optimizing Swin-UNETR for 3D brain tumor segmentation.
Wildfire Risk Classification – Developing smoke detection models for fire-prone region assessment.
Environmental Monitoring – Leveraging Sentinel-2 satellite imagery to detect deforestation.

Key Research Projects

  • Brain Tumor Image Segmentation: Developed an advanced classification model utilizing Swin transformers and attention mechanisms to accurately segment and classify brain tumors.
  • Road Crack Image Segmentation: Implemented a novel approach using discrete wavelet transforms and self-attention mechanisms to detect and segment road cracks with high precision. Further details in portfolio

Research Experience

As a Machine Learning Engineer (Volunteer) at Omdena, I contribute to AI-driven solutions addressing critical social and environmental issues. My work involves developing end-to-end ML pipelines for projects such as:

  • Yield Prediction & Flood Forecasting – Enhancing agricultural productivity and disaster preparedness using AI.
  • Deforestation Monitoring – Leveraging Sentinel-2 imagery and vegetation indices for forest conservation.

I specialize in computer vision, remote sensing, and machine learning, developing solutions that drive real-world impact. Explore my GitHub repository for a comprehensive list of notebooks and code related to my ecological AI experiments For my detailed implementations of research papers, please refer to this repo.