Developed and validated complex machine learning and deep learning models on medical images (Chest X-Rays, Mammograms, and Digital Hand Images) to identify racial bias.Developed various modules in Niffler - A DICOM Framework for Machine Learning and Processing Pipelines.Working with a client to develop a Deep Learning Pipeline to detect cancers in Mammogram Images.

Kick-Started a Machine Learning project to cluster the customers based on their interests and market to their needs accordingly. Worked with a client on Data Analysis, Visualizations, and automated operations to analyze the data quality. We built a unified data repository of customers to apply Machine Learning models and extract insights from the data.Developed applications to store, access, retrieve, analyze and build models on the data from the unified data repository.

Implemented a time-series forecasting model to forecast the blood-glucose levels of diabetic patients to treat hyperglycemia and hypoglycemia. The research paper has been accepted at ECAI - 2020.Implemented a Few-Shot Learning algorithm to fine-tune a few image triplets created from CheXpert dataset and improved pathology classification results by decreasing false positives and false negatives. We have experimented with the usual Few-Shot Learning and Incremental Few-Shot Learning models. MarginRakingLoss is used as the loss function to implement the Few-Shot Learning model. This research was performed at PLHI Lab - IUPUI under the supervision of Prof. Saptarshi Purkayastha.Worked on developing language models to identify and reduce the time taken to diagnose rare diseases from patient notes. This involves extracting symptoms, diseases/disorders of a given patient over a period of time to narrow down the probability of the sickness being a certain disease. The research is being conducted at Data Lab - IUPUI under the esteemed guidance of Prof. Sunandan Chakraborty. Used Entity and Information Extraction, and techniques to extract the relevant entities from patient notes and model them accordingly.Worked on a POC to develop and implement a Natural Language Processing algorithm to detect and explain the presence of fake and false new statements collected from various media outlets in the US.

Responsible for developing data science pipelines for problems of various domains including healthcare, banking, and HR Analytics. The pipeline development includes data mining/collection, data processing, applying machine learning and deep learning models, model improvement. Worked extensively on Natural Language Processing Problems and Deep Learning Algorithms.

Responsible for research on machine learning algorithms and perform proof of concept on open-source datasets.