Jacob Mattingley
Ph.D., Former Researcher · Stanford University (Former advisor: Professor Stephen Boyd)
Completed Ph.D. in Electrical Engineering at Stanford under Stephen Boyd, focusing on automatic code generation for embedded convex optimization. Created CVXGEN, an online tool for generating extremely fast solvers for embedded systems—directly translating academic optimization research into production-ready code generation infrastructure.
Why: Nearly perfect match: Ph.D.-level optimization specialist who built CVXGEN for automatic code generation of embedded convex solvers. Research focus on 'extremely fast solvers' for embedded systems mirrors Adam's work exactly. Slightly less evidence of solo founding or extensive technical writing, but the core technical DNA is nearly identical.
Sergey Pudov
Expert in Sparse BLAS & Sparse solvers · Huawei
19+ years as a sparse BLAS and sparse solvers expert, currently at Huawei. Deep specialist in the foundational computational layer of sparse linear algebra systems—the exact mathematical infrastructure that underlies optimization and simulation workloads.
Why: Perfect match on sparse linear algebra expertise—the exact computational foundation Adam works with. Strong on building solver infrastructure for engineers. However, works within large company (Huawei) rather than as solo founder, and less evidence of embedded optimization focus or technical writing/education.
Byron Tasseff
Optimization Engineer · Gurobi
Builds optimization software for battery storage, electricity markets, infrastructure networks, and decision systems. Connects mathematical models, physical systems, solver behavior, and production realities into trustworthy software—bridging theory and implementation in optimization.
Why: Strong optimization specialist who explicitly bridges mathematical models and production software. Works at Gurobi (major solver company) building optimization infrastructure. Less focus on embedded systems or solo founding, but excellent match on translating academic optimization into production tools.
Feodor Pisnitchenko
Principal R&D Engineer · Huawei RRI
Applied mathematician and principal-level R&D engineer with 17 years experience working at the intersection of mathematical optimization & solvers, NPU/GPU kernels, AI accelerator software, and high-performance computing. Focuses on making advanced algorithms run efficiently on specialized hardware.
Why: Excellent match on optimization solvers + performance on constrained hardware (NPU/GPU kernels). Applied mathematician building solver infrastructure. Works at large company rather than solo, and less evidence of technical writing, but core technical profile is very similar.
Caleb Magruder
Research Scientist and Director of Engineering
Director of Engineering and Research Scientist specializing in scaling optimization solver algorithms from research to production. Focuses on scientific computing and optimization software, delivering $48.5M in combined annual savings through production optimization systems.
Why: Strong match on scaling optimization solvers from research to production—exactly Adam's bridge between academic rigor and product. Less focus on embedded systems specifically, and works in director role rather than solo founder, but excellent on core optimization and productization.
Mina Farhan
Software Architect · Cadence Design Inc.
Research scientist and R&D leader with deep expertise in scientific machine learning, numerical linear algebra, and high-performance computing. Proven track record of translating mathematical models and ML research into scalable production systems.
Why: Strong on numerical linear algebra and translating mathematical research into production. Works at Cadence (EDA company) building infrastructure. Less specific focus on optimization solvers or embedded systems, and no solo founding, but solid match on computational mathematics to product.
Oleg Shirokikh
Principal Software Engineer
Ph.D. principal engineer building commercial-grade platforms and orchestration layers for complex mathematical workloads including ML, optimization, simulation, and decision models. Focuses on platform architecture that makes advanced mathematical systems production-ready.
Why: Builds infrastructure for optimization and mathematical workloads—strong toolmaker for engineers. Ph.D. background translating to commercial systems. Less specific focus on embedded performance or sparse linear algebra, and no solo founding, but good match on building platforms for complex math.
Leon Sering
Mathematician and Computer Scientist · HERE
Mathematician and computer scientist developing a rich-VRP solver at HERE with theoretical research background in Nash flows over time. Strong passion for fast optimization algorithms, programming in Rust for performance, safety, and concurrency.
Why: Mathematician building optimization solvers (VRP) with passion for fast algorithms. Bridges theory and implementation. Uses Rust for performance. Less focus on embedded systems or sparse linear algebra specifically, and works at company rather than solo, but strong on optimization and performance.
