Soukaina Filali Boubrahimi

Soukaina Filali Boubrahimi

Associate Professor of Computer Science

Utah State University

Biography

Dr. Soukaina Filali Boubrahimi is an Associate Professor in the School of Computing of the College of Engineering at Utah State University. Her research advances machine learning for geoscience and space-weather applications, with emphasis on time-series analysis, multimodal data integration, and graph-based models. Her group builds interpretable, data-driven models for water resources management and space-weather forecasting: Preview Data Mining Lab work. Her work is supported by the National Science Foundation through NSF CAREER, SHINE, and CAIG grants. Dr. Filali Boubrahimi has secured over $1.9M in extramural funding and received the 2024 USU College of Science Researcher of the Year Award and the 2023 USU School of Computing Outstanding Faculty Researcher Award.

đź“„ CV (Last updated August 2025)

Interests
  • Time Series Data Mining
  • Spatiotemporal Machine Learning
  • Interpretable AI
  • Space Weather Prediction
Education

  • PhD in Computer Science, 2020

    Georgia State University

  • MS in Computer Science, 2019

    Georgia State University

  • MS in Software Engineering, 2015

    Al Akhawyan University, Ifrane, Morocco

  • BSc in Computer Science, 2014

    Al Akhawyan University, Ifrane, Morocco

Projects

CAIG : Synthetic Data Generation for Solar Energetic Particle Events by Multimodal Augmentation

CAIG : Synthetic Data Generation for Solar Energetic Particle Events by Multimodal Augmentation

Award #2530946
Role: Co-PI

CAREER: End-to-End Active Region-based Heliospheric Forecasting System Using Multi-spacecraft Data and Machine Learning

CAREER: End-to-End Active Region-based Heliospheric Forecasting System Using Multi-spacecraft Data and Machine Learning

Award # 2240022
Role: PI

Combining Physics and Machine Learning-based Models for Full-Energy-Range Solar Energetic Particles Events Prediction

Combining Physics and Machine Learning-based Models for Full-Energy-Range Solar Energetic Particles Events Prediction

Award # 2204363
Role: PI

Recent News

Publications

Quickly discover relevant content by filtering publications.
Santosh and Hamdi, Shah Muhammad and Boubrahimi, Soukaina Filali Chapagain (2025). Advancing Hate Speech Detection with Transformers: Insights from the MetaHate.

Santosh and Cascalheira, Cory J and Hamdi, Shah Muhammad and Boubrahimi, Soukaina Filali and Scheer, Jillian R Chapagain (2025). Advancing minority stress detection with transformers: insights from the social media datasets.

Pouya and Boubrahimi, Soukaina Filali and Hamdi, Shah Muhammad Hosseinzadeh (2025). An End-to-end Ensemble Machine Learning Approach for Predicting High-impact Solar Energetic Particle Events Using Multimodal Data.

MohammadReza and Muhammad Hamdi, Shah and Filali Boubrahimi, Soukaina EskandariNasab (2025). AVATAR: Adversarial Autoencoders with Autoregressive Refinement for Time Series Generation.

Omar and Li, Peiyu and Boubrahimi, Soukaina Filali and Hamdi, Shah Muhammad Bahri (2025). Predicting Solar Energetic Particle Events with Time Series Shapelets.

See all publications

Teaching

 
 
 
 
 
AGO
CS 5800 - Introduction to Database Systems
Aug 2022 – Present The course introduces modern database systems through hands-on SQL and core principles of design and implementation. Students build robust schemas from ER/EER models and write complex queries (nested, aggregates, views) while mastering functional dependencies, normalization, integrity constraints, and performance essentials such as file organization and indexing.
Fall 2022, Fall 2023, Fall 2024
 
 
 
 
 
ADMLOGO
CS 6675/7675 – Advanced Data Mining
Jan 2021 – Present The course provides a deep dive into advanced topics in mining texts, graphs, time-series data, vector datasets, and frequent itemset and association rules applied to real-world datasets in a semester-long project.
Spring 2021, Spring 2022, Spring 2024, Spring 2025
 
 
 
 
 
TDM
CS 5080/6080 - Time Series Data Mining
Aug 2020 – Present The course provides an overview on state-of-the-art research on mining time series data. Topics covered include data representation, rule discovery, similarity search, classification, clustering, motif discovery, and visualization of time series data. Key topics include Node2Vec and Word2Vec for embedding, vector space models, time series classification, representation learning, data reduction, and association rule mining.
Fall 2020, Fall 2021, Fall 2022, Fall 2023

Contact