Hello, I am అభిరామ్ ముళ్ళపూడి (Abhiram Mullapudi). I build digital water systems. Akin to autonomous cars, these digital water systems autonomously monitor and operate themselves to "squeeze" more performance out of our physical water networks and create resilient water infrastructure. I work as a Senior Data Scientist at Xylem in the Hydroinformactics team. At Xylem, I design machine learning-based analytical methodologies, develop software infrastructure, and build wireless sensor networks to help cities and utilities better manage their water infrastructure. I have a Ph.D. in intelligent infrastructure systems from the University of Michigan, Ann Arbor.
I am a design nerd. I like typesetting documents in LaTex. I am passionate about open-source science and software; I extensively use and contribute to open-source tools. I enjoy tinkering with microcontrollers and sensors. I am obsessed with coffee and all its derivatives. I enjoy traveling, hiking, and exploring different cuisines. I love playing video games, reading fiction, and occasionally non-fiction.
I am interested in optimization, real-time control, and machine learning, and their application for addressing water challenges. I also enjoy building wireless sensor networks: designing backend infrastructure, prototyping sensors, and laying out custom hardware.
In preparation, 2022
pystorms provides a curated collection of stormwater control scenarios to enable the development and quantitative comparison of stormwater control algorithms.
Urban Water Journal, 2022
Real-time control improves the nutrient capture efficiency of bioretention cells, and thus reducing the size of bioretention cells needed for nutrient removal.
Environmental Modelling and Software, 2021
StormReactor is a python package for updating pollutant concentrations in EPA-SWMM durng simulations.
Journal of Open Source Software, 2020
pyswmm is a python wrapper for interfacing with US EPA's Stormwater Management Model.
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.
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.
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.