అభిరామ్ ముళ్లపూడి

Inframark

Leading the development of machine learning-based solutions for optimizing the operation of collection systems and wastewater treatment plants.

Xylem

Designed and implemented end-to-end machine learning-based solutions that inform decision-making in urban water infrastructure systems. Led the development of statistical and machine learning-based methodologies for time-series filtering and anomaly detection for predictive maintenance and operational decision-making. Developed a Flyte-based MLOps platform to streamline end-to-end machine learning model development, deployment, and maintenance for Xylem's digital water products.

Xylem

Pioneered advanced machine learning and data engineering solutions for urban water infrastructure, transforming raw sensor data into actionable intelligence that optimizes water network performance, predicts critical operational challenges, and enables data-driven decision-making for utilities and municipalities. Developed a 1D-CNN model that leverages NOAA rainfall forecasts and near-real-time flow measurements to accurately predict 24-hour inflow to water treatment plants. Engineered an advanced 1D-CNN interpolation framework for processing spatially distributed river level data, enabling comprehensive environmental monitoring and regulatory compliance reporting. Designed a high-performance real-time processing system leveraging symbolic programming and advanced statistical techniques to detect network irregularities across 600+ concurrent data streams. Created machine learning-powered visualization platforms that translate complex water network dynamics into intuitive, actionable insights, including predictive treatment plant inflow dashboards and public-facing Combined Sewer Overflow event tracking.