About me

Ashrya Agrawal is a Data Science Intern at JP Morgan. He is also a undergraduate Computer Science student at BITS Pilani, Pilani Campus. He is passionate about mitigating bias in Machine Learning models. His journey of fairness began with JSoC 2020 where he developed a toolkit for bias audit and mitigation.

Research in Fairness. While working on the debiasing algorithms in the JSoC toolkit, he realized that the debiasign algorithms often fail in practice to generalize out-of-sample. This piqued his interest, and he was curious to understand these failures. In his research he demonstrated that the failure of debiasers can be explained as a consequence of large variance due to the bias-variance trade-off. To mitigate this large variance and improve generalization, he formulated the notion of partial debiasing.

Research in Causality. After studying debiasing from an observational viewpoint, he studied it from a causal perspective to analyze the behavior of debiasing methods and draw deeper insights than what is provided by statistical analysis of fairness. He studied the effect of debiasing methods on causal path-specific fairness metrics. Then he explained these effects from an Information-theoretic perspective using partial information decomposition.

Apart from fairness and causality, he is interested in exploring promising research areas in Machine Learning. He has worked in ADAPT Lab on Video Captioning using knowledge bases. Ashrya is also passionate about ML reading groups, open collaborations and teaching.