Projects

Long-Term Risks in ML Systems
This project investigates how feedback loops in ML systems can cause long-term, harmful impacts, and develops tools to detect, analyze, and prevent them before deployment.
Long-Term Risks in ML Systems
Design and Architecture of Data Science Pipelines
We study, design, and analyze the DS pipeline architecture consisting stages such as preprocessing, modeling, training, evaluation, etc.
Design and Architecture of Data Science Pipelines
Safety Assurance of Predictive Systems
We built abstractions of ML systems and inferred preconditions to provide assurance in safety-critical predictions.
Safety Assurance of Predictive Systems
Verifying Neural Networks for Individual Fairness
A modular approach to formally verify neural networks. We specified individual property for SMT solver and verified fairness for specific subpopulations.
Verifying Neural Networks for Individual Fairness
Causal Fairness in Machine Learning Pipeline
We used causal reasoning to measure fairness of components and remove them from machine learning pipeline.
Causal Fairness in Machine Learning Pipeline
Fairness Engineering in ML Models
We have studied the software engineering concerns of fairness in real-world machine learning models.
Fairness Engineering in ML Models
ML Repo Dataset from GitHub
This dataset is created by mining 5M Python program snapshots. The code is transformed to AST for static analysis.
ML Repo Dataset from GitHub
Large-Scale Mining of Data-Science Software from GitHub
Mining and analyzing data-science repositories can provide insights from historical data.
Large-Scale Mining of Data-Science Software from GitHub