Shinjini Halder

Passionate about contributing to research labs and industries with a solid foundation in implementing protocols and algorithms. Strong leadership, management, and communication skills developed through teaching assistant experience. Proficient in problem-solving, data analysis, and collaborative teamwork with a commitment to advancing technology through innovative research.

1. MTSE U-Net: an architecture for segmentation, and prediction of fetal brain and gestational age from MRI of brain

(Published: 12/11/2022, Network Modeling Analysis in Health Informatics and Bioinformatics, Springer, DOI: https://doi.org/10.1007/s13721-022-00394-y )

The project includes segmenting 2D MRI images of fetal brain images into 7 parts, and predicting age and type.

2. Fetal Brain Component Segmentation Using 2-Way Ensemble U-Net (Published: 29/5/2023, Springer LNNS conference, DOI: https://doi.org/10.1007/978- 981-99-1414-2_28 )

This paper presents a work on the segmentation of the fetal brain from MRI into its 7 major components. This is performed using a 2-way Ensemble U-Net, a deep learning model based on U-Net.

• “Quantum Computing using Indigenous Quantum Simulator QSim”, organized by C-DAC Hyderabad and IIT Roorkee
• Qiskit Fall Fest 2022, included a stepwise introduction to quantum computing and quantum machine learning
• 2022-2023 Quantum Explorers learning program and unlocked the “Quantum and Qiskit 101" achievement (Quantum Fundamentals)