Statistical Modeling: The Two Cultures is an influential essay by Leo Breiman that delineates two approaches to statistical modeling: the "data modeling" culture, which emphasizes formal statistical inference and model fitting, and the "algorithmic modeling" culture, which prioritizes predictive accuracy and computational efficiency. Breiman argues for a shift towards the latter culture, advocating for the development and use of robust algorithms and machine learning techniques that focus on prediction rather than solely on theoretical statistical inference.

Paper exploration on SMOTE, or Synthetic Minority Over-sampling Technique, which was introduced to tackle class imbalance. Currently, it is widely adopted by practitioners and researchers alike.

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: An exploration on how ViT can be used for vision.

A Unified Approach to Interpreting Model Predictions is a research paper that presents a comprehensive framework for interpreting the predictions made by machine learning models. The main goal of this approach is to provide a unified and systematic way to understand why a model makes specific predictions. The paper discusses various methods and techniques that can be applied across different types of models, such as linear models, decision trees, neural networks, etc., to gain insights into their decision-making processes. This approach is important because it helps address the "black-box" nature of complex models by making their predictions more transparent and interpretable.

Demo for Cheers AI, an AI enabled tool for prediction of Diabetic Retinopathy and Glaucoma based on fundus images. This tool is being used by BP Koirala Eye Foundation (Hospital for Children, Eye, ENT and Rehabilitation Services or CHEERS) to detect blindness caused by diabetic retinopathy (which is preventable if detected early).

Supervised Fraud detection in unbalanced banking data.

Image classification on self generated dataset using PyTorch