ORCID ID: 0000-0002-1631-5336BDLP: 213/1551Semantic Scholar ID: 35719012Google Scholar ID: EFSi5FoAAAAJ
Associate Editor of
Expert Systems with Applications Journal (ESWA), Elsevier, 2022 - 2024 [link]
Reviewer of
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022 [link]
European Conference on Machine Learning and Data Mining (ECML PKDD), 2022 [link]
The Astrophysical Journal Supplement Series (ApJS), American Astronomical Society, 2021, 2022 [link]
Expert Systems with Applications Journal (ESWA), Elsevier, 2020, 2021 [link]
Computer Methods and Programs in Biomedicine journal, Elsevier, 2022 [link]
Earth Science Informatics Journal (ESIN), Springer, 2020 [link]
Defense Committee member of
M.S. Capstone, "An ML-Ready Augmentation Engine for Solar Filaments"
Shreejaa Talla - Summer 2022
M.S. Capstone, "TS-Dubuc: A Time Series Similarity Measure Inspired by Dubuc's Quantification of Fractal Dimension"
Krishna R. Puthucode - Summer 2022
M.S. Capstone, "Interactive Supervised Machine Learning Model Evaluation Using D3" [link]
Egill Gunnarsson - Summer 2021
M.S. Capstone, "Machine Learning on Multivariate Time Series for Solar Flare Prediction" [link]
Junzhi Wen - Summer 2021
Events
The ACM chapter at GSU
- Invited Talk -
Machine Learning for
Space-Weather Forecasting
Friday, March 4, 2022
1:00 p.m.-2:00 p.m.
Abstract.
Machine learning and data science can offer a lot more than what the job market advertises. If you are interested in one of such applications, I would be glad to share with you how it is helping “space-weather forecasting”: the real-world problem, the approaches, and of course, the impact. And if you find yourself fascinated by such problems, maybe you could join our lab—we are always looking for passionate students![acm.cs.gsu.edu.]IEEE BigData Tutorial 2020
- Tutorial -
Data Sources, Tools, and Techniques for Big Data-driven Machine Learning in Heliophysics
Abstract.
During the past decade, Georgia State University’s (GSU) Data Mining Lab (DMLab) has been conducting research on a wide range of topics centering on understanding, detection, and forecast of solar events, those of which can (directly or indirectly) have significant economic and collateral impacts on mankind, through electromagnetic radiation and energetic particles. The close collaboration of the Computer Scientists and Solar Physicists with the sole dedication to research on solar events using advanced statistical tools, machine learning (ML) and deep learning (DL), resulted in a couple of hundreds of in-depth studies in this domain. Many of these studies have been published in prestigious journals such as Nature’s Scientific Data and The Astrophysical Journal. We would like to prepare a tutorial on some of the methodologies we engineered, the challenges we faced, and the products we put together. We believe our solutions and products can stimulate new data-driven discoveries in heliophysics, as well as to serve and inspire communities of other domains.[http://bigdataieee.org/BigData2020/Tutorials.html#tutorial6][Slides]Role: Lead Presenter
IEEE BigData Cup Challenge 2020
Flare Prediction
Abstract.
The goal of this dataset competition is to introduce the machine learning/data mining community to an integrated dataset that can be utilized for predicting and understanding solar flares. Solar flares and Coronal Mass Ejections (CMEs) are events occurring in the solar corona and heliosphere that can have a major negative impact on our technology dependent society. Electromagnetic radiation and ionized particles from solar flares and eruptions tend to be filtered out by Earth’s atmosphere, but they can still pose a hazard to astronauts and sensitive equipment in space, as well as disrupt various high frequency radio communications that military and civilian customers become increasingly reliant upon each year. A strong enough CME can also cause significant enough fluctuations in Earth’s magnetosphere to induce currents in large networks of conductive materials such as power grids. These induced currents can lead to surges that have the potential to melt transformers of long distance transmission lines causing large scale blackouts. A 2008 report by the National Research Council concluded that a solar superstorm, similar to one observed in 1857 called the Carrington event, could cripple the entire US power grid for months and lead to an economic damage of 1 to 2 trillion dollars.[https://dmlab.cs.gsu.edu/bigdata/flare-comp-2020/]Role: Co-organizer
IEEE BigData Cup Challenge 2019
Flare Prediction
Abstract.
The goal of this dataset competition is to introduce the machine learning/data mining community to an integrated dataset that can be utilized for predicting and understanding solar flares. Solar flares and Coronal Mass Ejections (CMEs) are events occurring in the solar corona and heliosphere that can have a major negative impact on our technology dependent society. Electromagnetic radiation and ionized particles from solar flares and eruptions tend to be filtered out by Earth’s atmosphere, but they can still pose a hazard to astronauts and sensitive equipment in space, as well as disrupt various high frequency radio communications that military and civilian customers become increasingly reliant upon each year. A strong enough CME can also cause significant enough fluctuations in Earth’s magnetosphere to induce currents in large networks of conductive materials such as power grids. These induced currents can lead to surges that have the potential to melt transformers of long distance transmission lines causing large scale blackouts. A 2008 report by the National Research Council concluded that a solar superstorm, similar to one observed in 1857 called the Carrington event, could cripple the entire US power grid for months and lead to an economic damage of 1 to 2 trillion dollars.[https://dmlab.cs.gsu.edu/bigdata/flare-comp-2019/]Role: Kaggle Support