Profile Name

Hello, I am అభిరామ్‌ ముళ్ళపూడి (Abhiram Mullapudi). I develop autonomous water systems; think self-driving cars, but for water infrastructure. These autonomous water systems will monitor and operate themselves to mitigate flash floods, ensure access to safe drinking water, and improve the efficiency of water treatment processes. I currently work at Xylem Vue as a Hydraulic Optimization and Control Engineer. At my job, I design machine learning algorithms and develop software for distilling actionable intelligence from data. In addition, I develop firmware and hardware for wireless monitoring of water networks. I have a Ph.D. in civil engineering, specializing in intelligent systems, from the University of Michigan, Ann Arbor, where I was fortunate to have been advised by Prof. Branko Kerkez. I did my bachelor's in civil engineering at Amrita Viswavidhya Peetham, Coimbatore.

I am a design nerd. I enjoy learning about urban systems, graphic design (especially fonts), and architecture. I like typesetting documents in LaTex. I also collect fountain pens and mechanical pencils. 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 and reading fiction and occasionally non-fiction. I am currently learning French and Chess.

Email  /  CV  /  Github  /  Google Scholar  /  LinkedIn


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.



Bayesian optimization for shaping the response of stormwater systems

Abhiram Mullapudi, Branko Kerkez

In preparation, 2022


Bayesian optimization is a 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, Sara C. Troutman, Gregory Ewing, Jeffrey M. Sadler, Bryant E. McDonnell, Ruben Kertesz, Jonathan L. Goodall, Jon M. Hathaway, Branko Kerkez

In preparation, 2022

code / manuscript/ webpage

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 bioretention cells using real-time control

Brooke E. Mason, Abhiram Mullapudi, Cyndee L. Gruden, Branko Kerkez

Urban Water Journal, 2022

code/ paper

Real-time control improves the nutrient capture efficiency of bioretention cells, and thus reducing the size of bioretention 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

code/ manuscript

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

code / manuscript

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

code / manuscript

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

code / manuscript

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

code / manuscript

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.