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Articles by Nadia
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Volunteer Experience
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Machine Learning Mentor: Heliophysics Innovation in Technology and Science Community-Driven SDO Project
Southwest Research Institute
- 1 year
Science and Technology
I mentor city college, undergraduates, and citizen scientists and co-develop machine learning software for data preparation and self supervised machine learning pipelines with my community for Southwest Research Institute heliophysics collaborators with the intention of exposing the wider public to applied machine learning research and innovators from the national labs.
Publications
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Developing a Disaster-Ready Power Grid Agent Through Geophysically-Informed Fault Event Scenarios
IEEE Power & Energy Society General Meeting (PESGM)
Management of the nation’s power grid over the coming decades will need to consider multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the…
Management of the nation’s power grid over the coming decades will need to consider multiple climate-driven threats to the power system that can be stressful or even detrimental to its operations. During disasters, grid operators suffer from cognitive overload where the electric grid is severely impacted by the weather, yet grid operators have limited awareness of these factors. Meanwhile, the electric grid is facing an explosion of data coming from a variety of sources which can enable the operator to evaluate the risks and develop mitigation strategies against hazardous events. In this work, we processed heterogeneous environmental and power grid data to learn and model grid behaviour caused by extreme weather events. In this study, we focused on two weather-driven hazards, hurricanes and wildfires, which were analysed for the electric grid of Texas. We then used this data to train a Reinforcement Learning (RL) agent by analysing and predicting future behaviour of the grid during those hazard events. All these parts are incorporated in one framework to have a geophysically informed power simulators that can be used to train RL agents.
Other authors
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Detecting Spatiotemporal Lightning Patterns: An Unsupervised Graph-Based Approach
ML for Physical Sciences NeurIPS
Accurate measures of lightning activity can be used to predict extreme weather events in advance, saving lives and property. However, the current hand-crafted filtering algorithm for identifying true lightning events from data captured by the GLM onboard NOAA’s GOES-R satellites is only 70% accurate, with a 5% false alarm rate. This work applies unsupervised learning techniques to the large volume and high temporal resolution GLM dataset in an effort to detect lightning within raw data signals.…
Accurate measures of lightning activity can be used to predict extreme weather events in advance, saving lives and property. However, the current hand-crafted filtering algorithm for identifying true lightning events from data captured by the GLM onboard NOAA’s GOES-R satellites is only 70% accurate, with a 5% false alarm rate. This work applies unsupervised learning techniques to the large volume and high temporal resolution GLM dataset in an effort to detect lightning within raw data signals. We present a novel data processing pipeline for the GLM Level 0 products and case study comparison of two approaches to dimensionality reduction and clustering to sort the data by similar patterns. These clusters could then be labeled by a domain expert to accurately distinguish between noise and true lightning events. We demonstrate that autoencoders with graph convolution layers can learn a translationally invariant representation of the dataset which allows for k-means clustering to group samples that have similar spatiotemporal patterns together. This is a first step towards building a machine learning pipeline for improving false event filtering to identify lightning and enhance predictive abilities in the face of increasingly frequent extreme weather events.
Other authors
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Leveraging Lightning with Convolutional Recurrent AutoEncoder and ROCKET for Severe Weather Detection
AI For Earth Sciences at NeurIPS
Previous studies have shown that increases in flash rates detected in ground-based lightning data can be a precursor to severe weather hazards. Lightning data from the Geostationary Lightning Mapper (GLM) aboard the GOES-R satellite is not part of an operational model used by forecasters and is underutilized in severe storm research. The Advanced Baseline Imager’s (ABI) visible imagery also shows cloud features, such as overshooting tops and above-anvil cirrus plumes, which have been associated…
Previous studies have shown that increases in flash rates detected in ground-based lightning data can be a precursor to severe weather hazards. Lightning data from the Geostationary Lightning Mapper (GLM) aboard the GOES-R satellite is not part of an operational model used by forecasters and is underutilized in severe storm research. The Advanced Baseline Imager’s (ABI) visible imagery also shows cloud features, such as overshooting tops and above-anvil cirrus plumes, which have been associated with severe weather hazards. We introduce a generative video frame prediction methodology using a convolutional recurrent autoencoder, to leverage these spatio-temporal patterns in GLM and ABI, along with ground-based severe weather data. An initial case study is presented and contrasted with a time series classification of GLM data. Through this study, we seek to highlight the value of GLM data to assist meteorologists in time-constrained nowcasting (15-30 minute lead time) of severe hazards.
Other authors
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Data Driven Optimization of Energy Management in Residential Buildings with Energy Harvesting and Storage
MDPI Energies
This paper presents a battery-aware stochastic control framework for residential energy management systems (EMS) equipped with energy harvesting, that is, photovoltaic panels, and storage capabilities. The model and control rationale takes into account the dynamics of load, the weather, the weather forecast, the utility, and consumer preferences into a unified Markov decision process. The embedded optimization problem is formulated to determine the proportion of energy drawn from the battery…
This paper presents a battery-aware stochastic control framework for residential energy management systems (EMS) equipped with energy harvesting, that is, photovoltaic panels, and storage capabilities. The model and control rationale takes into account the dynamics of load, the weather, the weather forecast, the utility, and consumer preferences into a unified Markov decision process. The embedded optimization problem is formulated to determine the proportion of energy drawn from the battery and the grid to minimize a cost function capturing a user-defined tradeoff between battery degradation and financial expense by user preferences. Numerical results are based on real-world weather data for Golden, Colorado, and load traces. The results illustrate the ability of the system to limit battery degradation assessed using the Rain flow counting method for lithium ion batteries.
Other authors
Honors & Awards
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Steely Eyed Operator Award
Frontier Development Lab
"In every FDL project there's a moment when everything can go wrong. This is the implicit contract of high risk high reward research. If you don't go through this then your work isn't new enough. Remaining clear-eyed and clear-headed in this moment is a rare quality and without it FDL wouldn't deliver. The steely-eyed operator acknowledges the efforts of a faculty member who has regularly been a calm voice in the storm quite literally."
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Grace Hopper Celebration Faculty Scholar
AnitaB.Org
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Women in Machine Learning Travel Grant
Women in Machine Learning
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Best Teaching Assistant Award
UC Irvine Engineering Student Council
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Grand Prize Student Award
US Department of Energy
Apps for Energy
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Graduate Seed Grant
UC Irvine Center for Global Peace and Conflict Studies
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Commencement Speaker
Henry Samueli School of Engineering
Organizations
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PyLadies Orange County
Founder
- Present
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