Hello! I am Abhiram Mullapudi (అభిరామ్ ముళ్లపూడి). I build digital water systems that integrate machine learning, wireless sensor networks, and physical modeling to improve the resilience of urban water infrastructure. I am currently working at Inframark as a Lead Data Scientist, where I develop scalable machine learning solutions for small and medium-sized water utilities. My interests lie in signal processing, probabilistic methods, and optimization for cyber-physical infrastructure systems. I write about some of these at randomstorms.substack.com. I am based in Washington, DC 🌸. During my free time, I row with the amazing crew at Capital Rowing Club, work on improving my espresso skills, and try to keep my plants alive."
Email / CV / GitHub / Google Scholar / LinkedIn
Publications
Identification of stormwater control strategies and their associated uncertainties using Bayesian Optimization
Abhiram Mullapudi, Branko Kerkez
In preparation, 2023
Bayesian optimization is an automated data-driven approach for identifying a control strategy that achieves the desired response from the stormwater network.
pystorms : Simulation sandbox for the evaluation and design of stormwater control algorithms
Sara P. Rimer, Abhiram Mullapudi, et al.
Environmental Modelling and Software, 2023
pystorms provides a curated collection of stormwater control scenarios to enable the development and quantitative comparison of stormwater control algorithms.
Improvement of phosphorus removal in bio retention cells using real-time control
Brooke E. Mason, Abhiram Mullapudi, Cyndee L. Gruden, Branko Kerkez
Urban Water Journal, 2022
Real-time control improves the nutrient capture efficiency of bio retention cells, and thus reducing the size of bio retention cells needed for nutrient removal.
StormReactor: An open-source Python package for the integrated modeling of water quality and water balance
Brooke E. Mason, Abhiram Mullapudi, Branko Kerkez
Environmental Modelling and Software, 2021
StormReactor is a python package for updating pollutant concentrations in EPA-SWMM durng simulations.
PySWMM : The Python Interface to Stormwater Management Model (SWMM)
Bryant E. McDonnell, Katherine Ratliff, Michael E. Tryby, Jennifer Jia Xin Wu, Abhiram Mullapudi
Journal of Open Source Software, 2020
pyswmm is a python wrapper for interfacing with US EPA's Stormwater Management Model.
Deep Reinforcement Learning for the Real Time Control of Stormwater Systems
Abhiram Mullapudi, Matthew J. Lewis, Cyndee L. Gruden, Branko Kerkez
Advances in Water Resources, 2020
Reinforcement learning can be used for creating autonomous stormwater systems that can dynamically change their behavior based on the state of the watershed for achieving system scale objectives.
rrcf : Implementation of the Robust Random Cut Forest algorithm for anomaly detection on streams
Matt D. Bartos, Abhiram Mullapudi, Sara C. Troutman
Journal of Open Source Software, 2019
Anomalies in the streaming data can be detected by estimating the shift in the structure of the random forest caused by the addition of a new data point.
Shaping streamflow using a real-time stormwater control network
Abhiram Mullapudi, Matt D. Bartos, Brandon P. Wong, Branko Kerkez
Sensors, 2018
Response of a stormwater network can be precisely shaped with the data from a wireless sensor network.
Building a theory for smart stormwater systems
Abhiram Mullapudi, Brandon P. Wong, Branko Kerkez
Environmental Science: Water Research and Technology, 2017
By re-imagining physical watersheds as a network of interconnected systems, they can be dynamically reconfigured in real-time to target the removal of specific pollutants.