Zeal Shah
Ph.D. Candidate
STIMA Lab (Systems Towards Infrastructure Measurement and Analytics)
University of Massachusetts Amherst
Advisor: Jay Taneja
I am a 5th year Ph.D. candidate in Electrical and Computer Engineering at UMass Amherst, where I am part of the STIMA lab (Systems Towards Infrastructure Measurement and Analytics) and the e-GUIDE initiative (Electricity Growth and Use in Developing Economies). I am fortunate to be advised by Professor Jay Taneja. Broadly, my Ph.D. research lies at the intersection of data science and sustainable development.
During my tenure as a graduate student, I have worked on a variety of practical data engineering and data science projects as an intern at Meta Reality Labs, Atlas AI and SparkMeter.
I hold a MS in Energy Science, Technology & Policy (with a concentration in ECE) from Carnegie Mellon University, Pittsburgh and a BS in Electrical Engineering from PDEU, India.
I am actively seeking full-time industry opportunities for summer/fall 2023!
PhD Data Engineering Intern in the Privacy DE team at Meta Reality Labs. (Summer 2022)
Project: Development of a dynamic event inventory for consistent cataloging and easy discovery of metadata related to the events emitted by all the Reality Labs apps and devices.
Tech stack: Python, Dataswarm (Airflow), Presto, Daiquery, and Unidash
AI Engineering Intern in the AI Innovation team at Atlas AI. (Summer 2020)
Project: Development of monthly electrification data layers for the entire continent of Africa from 2012-20 using publicly available remote sensing data and machine learning.
Tech stack: Python, and GCP tools – Compute Engine, Earth Engine, BigQuery, Bucket.
Data Science Intern in the Systems Engineering team at SparkMeter. (Spring & Summer 2018, Summer 2017)
Project(s): (i) Development of smart meter data intelligence reports to periodically deliver insights into technical and commercial operations of customer grids, (ii) Generating dashboards for real-time monitoring of deployed metering systems.
Tech stack: Python, SQL, and Grafana.
Product Engineering Intern at Nikola Power(now WattMore). (Summer 2018)
Project: Assist the engineering team in development of their proprietary energy management system.
Tech stack: Python.
Graduate Teaching Assistant at Carnegie Mellon University. (Spring & Fall 2017)
Courses: (i) Fundamental Electrical Power Systems, (ii) Embedded Control Systems\
My research focuses on developing cost-effective and pervasive electric grid sensing solutions using existing infrastructure such as digital cameras, satellite imagery, and computing tools such as machine learning and deep learning. I explore and develop both localized and wide-area monitoring solutions. Localized monitoring uses cameras to evaluate the phase and quality of electricity at the edges of distribution grids, with the aim of creating an economical solution for utilities that cannot afford expensive smart meter deployments. However, scaling localized camera-based monitoring to a larger area such as a country-wide scale can be challenging, which is where wide-area monitoring comes in. My wide-area monitoring work combines large amounts of satellite images and ground-collected data to estimate electrification status, power supply inconsistencies, and their impacts at high spatiotemporal resolution across a wide area. This research aims to generate historical, high-resolution outage maps of 500 cities in the US and 300 cities in Africa and make them publicly accessible, enabling global comparisons of utility performance, tracking the success of grid investments, and complementing various infrastructure surveys and research studies.
The Inequitable Distribution of Power Interruptions During the 2021 Texas Winter Storm Uri.
Environmental Research: Infrastructure and Sustainability
Zeal Shah, Juan Pablo Carvallo, Feng-Chi Hsu, Jay Taneja
PowerScour: Tracking Electrified Settlements Using Satellite Data
ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys’22)
Santiago Correa, Zeal Shah, Yuezi Wu, Simon Kohlhase, Philippe Raisin, Nabin Raj Gaihre, Vijay Modi, Jay Taneja
EffiSenseSee: Towards Classifying Light Bulb Types and Energy Efficiency With Camera-Based Sensing
ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys’22)
Alex Yen, Zeal Shah, Benjamin Ochoa, Pat Pannuto, Jay Taneja
A Higher Purpose: Measuring Electricity Access Using High-Resolution Daytime Satellite Imagery (Top 3 workshop papers)
Machine Learning for the Developing World (ML4D) Workshop at NeurIPS 2021
Zeal Shah, Simone Fobi, Gabriel Cadamuro, Jay Taneja
Frameworks for Considering RESs and Load Uncertainties in VPP Decision-Making (Book chapter)
Scheduling and Operation of Virtual Power Plants (Elsevier)
Zeal Shah, Ali Moghassemi, Panayiotis Moutis
The Electricity Scene from Above: Exploring Power Grid Inconsistencies Using Satellite Data in Accra, Ghana.
Applied Energy 2022
Zeal Shah, Noah Klugman, Gabriel Cadamuro, Feng-Chi Hsu, Christopher D. Elvidge, Jay Taneja
This Little Light of Mine: Electricity Access Mapping Using Night-time Light Data (Short paper)
ACM International Conference on Future Energy Systems (e-Energy’21)
Santiago Correa, Zeal Shah, Jay Taneja
Mapping Disasters & Tracking Recovery in Conflict Zones Using Nighttime Lights
IEEE Global Humanitarian Technology Conference (GHTC’20)
Zeal Shah, Feng-Chi Hsu, Christopher D Elvidge, Jay Taneja
GridInSight: Monitoring Electricity Using Visible Lights (Best Paper Nominee)
ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys’19)
Zeal Shah, Alex Yen, Ajey Pandey, Jay Taneja
Frozen Out in Texas: Blackouts and Inequity
Rockefeller Foundation
Zeal Shah, JP Carvallo, Feng-Chi Hsu, Jay Taneja
Lighting the Way: Nighttime Lights for Electrification Planning
Energy for Growth Hub
Stephen Lee, Zeal Shah, Brian Min, Jay Taneja
What is Temporal Resolution?
Atlas AI
Zeal Shah
Exploring Power Grid Inconsistencies Using Satellite Data in Accra, Ghana.
UMass Energy Forum 2021
Monitoring Electric Grid Reliability Using Satellite Data.
ACM BuildSys 2019 (Best Poster Award)
GridInSight: Monitoring Electricity Using Visible Lights.
ACM COMPASS 2019
Smart Metering Data For Tracking Access to Electricity.
Keynote at Microgrid Global Innovation Forum 2018
Presented by Jon Thacker
Occupancy Prediction Based on the Power Consumption Patterns.
CMU Symposium on Machine Learning in Science and Engineering 2017
Operation and Analysis of a Bi-directional DC-DC Converter for Efficient Charge Control of Battery in a Microgrid.
IEEE IAS Annual Meeting 2015