Shivansh Jyotishi

Developing an explainable AI framework for ML-based material classification from LIBS spectra, applying DeepSHAP to map feature importance to NIST atomic emission line assignments. Built a spectral preprocessing pipeline for LIBS and Raman data including baseline correction, spike removal, and normalization, deployed as a Tkinter application.

Conducted LIBS experiments on solid energetic material samples using CCD-based optical emission spectroscopy. Performed spectral processing including baseline correction, normalization, and spectral line selection for elemental identification, and estimated plasma temperature and electron density.

Processed INSAT-3DR satellite rainfall data from the MOSDAC portal using MATLAB, generating quantitative colourmap visualizations for active and break monsoon spell analysis. Contributed to the atmospheric remote sensing data pipeline.

Studied the wave formalism for geometric phase representation including Jones matrices, Stokes parameters, and Poincaré sphere. Implemented graphical visualizations of polarization ellipse and geometric phase evolution.

Currently preparing for JLPT N5/N4 in Japanese Language.

Grade: 9.2/10

Pursuing an Integrated M.Sc. in Physics with a focus on physical sciences, maintaining a CGPA of 9.2/10.

Completed courses in Intro to Programming, Python, and Intro to Machine Learning.

Grade: 97.5%

Completed Class 12th in the Science stream with an overall percentage of 97.5%.