Herbert Wertheim College of Engineering
Data Informatics for Systems Improvement and Design Laboratory (DISIDE)
Led by Mostafa Reisi-Gahrooei · Industrial and Systems Engineering
What we study
The DISIDE Lab develops methods to analyze high-dimensional data for monitoring systems in agriculture, manufacturing, healthcare, and transportation. They use computational models and algorithms to improve system performance.
What working here is like
- Computational
- Coding required
- Papers
- 3
Recent papers, explained
- Deep learning‐based MRI analysis reveals Lewy body co‐pathology accelerates brain aging in Alzheimer's disease
2026 · Alzheimer s & Dementia
Uses deep learning to study brain aging in Alzheimer's and Lewy body disease patients.
- Federated Conformal Approach for Trustworthy Uncertainty Quantification in Distributed Fleet Prognostics
2026 · Technometrics
Develops a method to predict uncertainty in distributed fleet data without sharing it.
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- Beyond Global and Individual Models: Tensorized Multitask Regression for Personalized Learning from High-Dimensional Data
2026 · INFORMS Journal on Data Science
Builds a model to predict outcomes for different groups using shared and individual data.
Plain-language summaries are AI-written and reviewed by us; check the paper for details.
How to reach out
Mostafa Reisi-Gahrooei hasn't set up this page yet. You can also look them up in the UF directory (search “Mostafa Reisi-Gahrooei”).