Mission · Team · Values
A research-driven engineering company working on deterministic signal processing and AI frameworks, both built on the same mathematical foundation.
SparseTech is a research-driven engineering company organized around two areas of work that share a single mathematical foundation: deterministic signal processing for next-generation telecommunications and sensing, and AI frameworks for edge deployment and high-reliability systems.
Both areas share the same idea, that real-world systems contain more structure than randomness, and the same discipline: find what carries information, work only on that, and keep the result reproducible enough to be testable and certifiable.
"Understanding where systems strain is the first step toward building ones that don't."
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.
SparseTech's long-term vision is a computing landscape where efficiency, predictability, and verifiability are first-class considerations across both signal-centric systems and model-based computation, guided by shared mathematical foundations rather than domain-specific hacks.
Mathematical and engineering discipline over novelty.
Real-world limits as design inputs.
Predictable, testable system behavior.
Careful publication and communication.
Licensed Professional Engineer with a background in structural engineering, forensic evaluation, and the delivery of high-reliability systems in regulated environments.
His work focuses on the boundary between theory and real-world deployment, where mathematical models, physical constraints, codes, and standards intersect.
Through SparseTech, he conducts research into how established computational techniques behave as energy, bandwidth, safety, and infrastructure constraints become dominant design factors.
Intellectual property strategist and research engineer with a Ph.D. from the University of Wisconsin–Madison, specializing in mathematical rigor, system design, and defensible technical disclosure.
His work focuses on shaping research programs so that fundamental ideas are expressed clearly, protected effectively, and translated into durable intellectual property.
At SparseTech, Shawn guides intellectual property development alongside internal research validation, emphasizing clarity of invention and strategic restraint.