SparseTech
Research in Computational Efficiency
"Sparsity isn't just a property of data. It's a design principle for intelligence."
— The SparseTech Research Team
What We Study
Most computational systems process more than they need to. We study the mathematical structures that enable selective computation—understanding what matters before you've done the full analysis.
Constraint-Driven Design
How do computational techniques behave when energy, bandwidth, and safety become the dominant design factors?
Mathematical Foundations
What structures and principles enable systems to focus resources where they matter most?
System Boundaries
Where do established approaches begin to strain, and what does that imply for future systems?
Our Perspective
We believe the next generation of computational infrastructure will be shaped less by brute-force scaling and more by careful alignment between mathematics, physical constraints, and real-world systems.
- →Efficiency over throughput: Scale, efficiency, and reliability increasingly matter more than raw performance.
- →Verifiability matters: Predictable, testable system behavior becomes essential as constraints tighten.
- →Shared foundations: Mathematical discipline, not domain-specific hacks, should guide system design.
"Understanding where systems strain is the first step toward building ones that don't."
How We Work
Rigor
Mathematical and engineering discipline over novelty.
Constraint Awareness
Real-world limits as design inputs.
Verifiability
Predictable, testable system behavior.
Restraint
Careful publication and communication.
Recent Writing
Notes on signal processing, computing constraints, and engineering trade-offs.
Read the Blog