అభిరామ్ ముళ్లపూడిAbhiram Mullapudi

Inframark

Lead Inframark's data science team, working with product, executive leadership, and utility operations to build the machine learning models behind its digital water products. Leading the design and development of Inframark's shared ML platform, covering data ingestion, model training, deployment, and retraining. Deployed ML-based chemical dosing control for phosphorus removal and chlorine disinfection across multiple utilities, and leading the development of an inflow & infiltration detection system that attributes wet-weather flow to specific pipe segments and ranks rehabilitation candidates.

Xylem

Developed the machine learning platform for Xylem's digital water products. Led the development of statistical and machine learning methods for time-series filtering and anomaly detection, used for predictive maintenance and plant operations. Built an MLOps platform on Flyte that Xylem's product teams used to train, deploy, and maintain their models, and that ran large SWMM simulations at scale on AWS clusters for optimization studies and model development.

Xylem

Built machine learning and data engineering systems for Xylem's wastewater network optimization products, used daily by utility operators. Developed a 1D-CNN model that combines NOAA rainfall forecasts with near-real-time flow measurements to predict 24-hour inflow to wastewater treatment plants, and a real-time anomaly detection system covering 600+ concurrent data streams. Helped develop a real-time controller for an in-line storage site in a combined sewer system, and built operator-facing dashboards including the City of Richmond's public CSO notification map.