Speakers

Aeshna Kapoor is a  Data Evangelist whose technical practice focuses on applying reproducible, explainable AI to reduce operational and regulatory risk in large, heterogeneous enterprise environments. She leads cross-functional efforts to design, build, and operationalize solutions that integrate complex legacy data sources into modern analytics platforms, create auditable Key Risk Indicator (KRI) pipelines, and embed automated policy-enforcement agents that accelerate detection and remediation workflows.

Her contributions fall into these core areas:

Scalable data engineering & integration- Architecting fault-tolerant pipelines, feature stores, and ETL strategies that convert fragmented, legacy data into timely, queryable assets for downstream models and dashboards.

Operational automation for controls & compliance- Building agentic automation that operationalizes regulatory rules—reducing manual effort, shortening remediation cycles, and improving audit readiness.

Aeshna’s methodology is deliberately pragmatic: combine robust engineering practices (CI/CD for data, continuous monitoring, retraining pipelines) with clear governance artifacts (data lineage, model cards, runbooks) to ensure adopted models are reliable, auditable, and actionable. Her public-facing thought leadership covers big-data infrastructure for regulated domains, and she regularly mentors engineers and data scientists—with an emphasis on enabling women and technologists from under-resourced communities to build practical AI skills.