01
Brain-inspired
learning
Exploring learning mechanisms inspired by the brain, with an emphasis on local learning and alternatives to conventional backpropagation.
ALGORITHMSPHD RESEARCHER · TEXAS A&M UNIVERSITY
Howdy, I’m Saquib.
Curious about
how intelligence
takes shape.
I’m Nazmus Saadat As-Saquib, a PhD student in Computer Engineering at Texas A&M University. My research connects brain-inspired learning, neuromorphic computing, and analog in-memory computing to build more efficient learning systems.
College Station, TexasSCROLL TO EXPLORE
Brain-inspired computingLocal learningAnalog in-memory computingHardware–software co-design
01 / A LITTLE ABOUT ME
Connecting ideas.
Across disciplines.
My work lives at the intersection of learning algorithms and physical computing.
My experience with semiconductor materials and device fabrication led me toward hardware–software co-design. At Texas A&M, I study how insights from the brain can inform more efficient ways for machines to learn.
Beyond the lab, I enjoy traveling, spending time with family and friends, and contributing to my community. Those experiences keep me curious—and remind me why the work matters.
More about my background02 / RESEARCH DIRECTIONS
Learning from the brain.
Building for the real world.
Connecting biological inspiration
with practical computation.
Brain-inspired
02
Analog in-memory
computing
Investigating how emerging memory devices can bring computation closer to stored data and support efficient learning systems.
HARDWARE03
Hardware–software
co-design
Connecting device-level constraints with algorithm design, informed by hands-on experience in semiconductor fabrication.
SYSTEMS