The Parallel Computing and Data Science Lab (Saeed Lab) is a computational research lab based at Knight Foundation School of Computing and Information Sciences (KFSCIS), Florida International University in Miami, Florida, led by Dr. Fahad Saeed. 

The Saeed Lab is located on FIU’s MMC Campus in the Computing, Arts, Sciences & Education (CASE) building (Room 261).

The Saeed Lab develops machine-learning models, combined with high-performance computing, and data science approaches, to study the functional genomics and organization of the human brain.

Our work focuses on understanding the stochastic difference between identified peptides from high-throughput mass spectrometry data for applications related to human health, disease, and environment. In addition, our work focuses on understanding brain function in the context of prediction, diagnosis and characterization of biomarkers specific to disorders such as epilepsy, ADHD, Autism, and Alzheimer’s.

To achieve these goals, we embrace open science principles and adopt and develop best practices to promote reproducible computational results. If you would like to know more about specific projects, you are welcome to visit us on GitHub and Software pages.

If you are a prospective PhD student please read and fill out the details here
If you are a prospective Post-Doctoral Fellow, please read and fill out the details here.
You can send me a follow-up email at after you have filled out the relevant forms. Non-adherence to this procedure does not guarantee a response.

Recent Papers

Muhammad Haseeb, and Fahad Saeed, GPU-Acceleration of the Distributed-Memory Database Peptide Search of Mass Spectrometry Data, Nature Scientific Reports, Vol 12, Article 18713, 2023

Usman Tariq, and Fahad Saeed , “SpeCollate: Deep cross-modal similarity network for mass spectrometry data based peptide deductions“, PLoS ONE, Vol. 16, Issue 10, Oct 2021

Muhammad Haseeb, and Fahad Saeed, “High performance computing framework for tera-scale database search of mass spectrometry data“, Nature Computational Science, Vol. 1, 550–561, August 2021

Taban Eslami, Vahid Mirjalili, Alvis Fong, Angela R. Laird, and Fahad Saeed, “ASD-DiagNet: A Hybrid Learning Approach for Detection of Autism Spectrum Disorder Using fMRI DataFrontiers of Neuroinformatics, vol. 13, pages 70, Nov 2019


Predicting Epileptic Seizures
Can Machine-learning models predict seizures before they happen.

Predicting onset of Alzheimer’s
Can combination of neuro-imaging and machine-learning predict Alzheimer before onset of symptoms?

Characterization of Autism Spectrum
Can machine-learning and neuroimaging enable heterogeneous characterization of ASD?

ML Ecosystem for Mass Spectrometry Data
An interconnected set of open-source machine-learning tools for mass spectrometry based omics

HPC Engine for Mass Spectrometry Data
An interconnected open-source HPC tools using heterogeneous architectures for scalable engines


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