Hello, I'm
Nirek Sharma
ML Fairness @ Upstart
I research how to make machine learning systems fair and equitable.
I'm a research scientist at Upstart where I focus on Machine Learning Fairness. I work to detect and mitigate disparate impacts of ML models across protected groups.
My career has been spent working on ML systems to solve problems that help people. But as these models have become more capable, I've become more passionate about addressing the unintended effects of ML models.
Highlighted Work
Algorithmic Debiasing via Post-Processing
A novel technique for algorithmically debiasing general ML models — developed at Upstart and applied to core underwriting systems. Published on arXiv, 2025.
Read the paper →
Previous Research
- Algorithmic Debiasing via Post-Processing
- Same Data, Different Conclusions: Radical Dispersion in Empirical Results When Independent Analysts Operationalize and Test the Same Hypothesis
- Model for HIV/AIDS Incorporating Pre- and Post-Exposure Treatments & Reproduction Number Derivation
- Development of a New Inter-Institutional Partnership to Assess Health Literacy Disparities in the Context of Kidney Cancer and Smoking
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