معرفی
Tade Souaiaia serves as Assistant Professor of Cell Biology at SUNY Downstate Health Sciences University's School of Graduate Studies. His research integrates computational biology with mental health investigations, focusing on gene expression dynamics in development and psychiatric disorders. He teaches graduate-level statistics and algorithm analysis courses delivered virtually via YouTube.
Dr. Souaiaia's primary research centers on computational method development for multi-omics integration (gene expression, isoform-level data, microRNA, ChIP-Seq) to unravel schizophrenia etiology. His secondary research applies kinematic modeling to athletic performance (sprint/long jump), connecting cellular mechanisms with environmental training models. This dual focus bridges molecular neuroscience with practical biomechanics applications.
Analysis of his 2020-2025 publications reveals three dominant research trajectories: (1) Psychiatric genetics through multi-ancestry studies of autism/schizophrenia, (2) Polygenic risk score innovation (notably BridgePRS for cross-ancestry portability), and (3) Primate neurogenomics investigating anxious temperament via orbitofrontal cortex and amygdala transcriptomics. His work consistently emphasizes translational applications of computational genomics to mental health.
No scientific awards were documented in the provided materials.
Dr. Souaiaia teaches Scientific Computing in Python (GRSC 6974) and Graduate Statistics (GRSC 0120), with virtual instruction accessible on YouTube. While specific grant details and student advising records are unreported, his laboratory actively develops computational frameworks for psychiatric genomics. His research program demonstrates strong interdisciplinary collaboration between neuroscience, psychiatry, and bioinformatics teams.
He leads a research laboratory specializing in computational analysis of multi-omics datasets, with particular emphasis on primate brain tissue studies. The lab develops novel algorithms for integrating genomic, transcriptomic, and epigenomic data to model mental health disorders, maintaining active partnerships with neuroscience and clinical psychiatry research groups.
