Work Experience

Worked on Single-Cell Chromatin Accessibility Prediction using Single Cell ATAC-Seq, which involves predicting single-cell chromatin accessibility by analyzing individual cells' ATAC-Seq data.

Collaborated with researchers from the Eastern Virginia Medical School in the Department of Microbiology and Molecular Cell Biology to develop a machine learning-based framework for ML-Powered Molecular Analysis of TNBC Disparity, in constructing a cell atlas of breast cancers, automating the mapping of query profiles to the cell atlas using multi-omics data, and continually improving the framework with more data becoming available in the future.

Conceptualized and implemented a product application for steam leakage detection using Neural Style Transfer Learning models. My responsibilities included working on deep learning models, hyperparameter-tuning, model selection, error-metrics analysis, and integration with Jetson Nano.

Designed a product for a research project for prostate cancer detection using Neural Style Transfer Learning models. My responsibilities included data pre-processing (conversion of Dicom images to appropriate format), image segmentation, designing and deploying suitable deep learning models, performing error-metrics analysis and working on the research paper.

Worked on a Machine Learning model that involves recommendation system. My responsibilities includes analyzing the collected data, designing and developing recommendation system, error-metrics analysis and improving the model performance.