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Developing AI Models
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Developing AI Models
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samresume/README.md

MohammadReza "Sam" EskandariNasab

PhD Candidate in Computer Science · Utah State University
Generative modeling for time series  ·  Multimodal learning  ·  Space weather forecasting

Personal website Google Scholar LinkedIn

👨‍🎓 About Me

I'm MohammadReza EskandariNasab — also known as Sam, a name borrowed from the legendary Persian hero of the Shahnameh.

🔬 Research

Generative Modeling for Time Series

Diffusion models, generative adversarial networks, and adversarial autoencoders for synthesizing and augmenting time-series data.

Published at IEEE ICDM, SIAM SDM, IEEE BigData, and IEEE ICMLA.

Multimodal Learning — UNISON

An NSF-funded project (Award No. 2530946) extending my generative work across modalities: generating and translating between images, text, vector data, and time series through the proposed UNISON framework.

Space Weather Forecasting

Machine-learning pipelines that forecast solar flares, solar energetic particle (SEP) events, and coronal mass ejections (CMEs).

Published in The Astrophysical Journal Supplement Series, with three manuscripts under review at Space Weather and The Astrophysical Journal.

🚀 GitHub Activity

Profile summary

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  1. ChronoGAN ChronoGAN Public

    This advanced framework integrates the benefits of an Autoencoder-generated embedding space with the adversarial training dynamics of GANs for time series generation..

    Jupyter Notebook 10 1

  2. SeriesGAN SeriesGAN Public

    We introduce an advanced framework that integrates the advantages of an autoencoder-generated embedding space with the adversarial training dynamics of GANs for time series generation.

    Jupyter Notebook 11 2

  3. FlaPLeT FlaPLeT Public

    FlaPLeT is an open-source, full-stack web platform that enables end-to-end machine learning workflows for solar flare prediction through an interactive user interface. Its modular design also provi…

    JavaScript 2 1

  4. SEP_Ensemble_Triplets SEP_Ensemble_Triplets Public

    An Innovative end-to-end pipeline that forecasts Solar Energetic Particle (SEP) events from pre-flare multivariate time series under a 103:1 class imbalance.

    Jupyter Notebook