Sinjini Mitra

PhD-trained machine learning scientist specializing in generative modeling, deep learning, and multimodal data analysis. Experienced in applying AI to healthcare, scientific computing, and document intelligence, with expertise in scalable ML pipelines using Python, PyTorch, and Docker. Open to international collaboration across Europe and eligible for the Blue Card.

Resume Screener AI

Built a resume screening application using NLP and LLM-based retrieval models to assess candidate-job alignment

• Developed modular APIs and deployed using Docker with CI/CD workflows
• Focused on end-to-end ML deployment, reproducibility, and scalable production design

Histopathology Image Classification 

Designed and trained deep learning models (CNNs, attention-based architectures) for large-scale histopathology slide classification

• Achieved 98% prediction accuracy on a dataset of approximately 387,000 high-resolution image patches
• Developed patch-level feature extraction pipelines to improve slide-level cancer prediction from sparse imaging data
• Implemented scalable PyTorch workflows with reproducible training and evaluation pipelines for imaging tasks.

• Date of birth: 11/15/94
• Gender: Female
• Nationality: Indian

Engineering IMPACT award (2020)

Awarded for excellence in contributions made to Fulton Schools of Engineering (ASU) in both academia and volunteering efforts.