Selected
RateLimitEngine: rate limiting for .NET systems
A .NET 8 rate-limiting library with four algorithms, in-memory or Redis state, and explicit concurrency and failure semantics.
Peyman Salimi · Software Engineer & Solution Designer
From complex problems to workable solutions
I take software, computational, and intelligent-system problems from understanding the need and problem structure through modeling, design, implementation, and validation—combining software engineering with quantitative thinking.
01
Understand
02
Model
03
Design & implement
04
Usable outcome
What kind of problem are you facing?
Choose the description closest to the problem, constraint, or outcome you have in mind.
Selected evidence
A real example of the work
A real project is shown here to make the problem, technical decisions, and inspectable output concrete.
- Selected
Elasticsearch Search Platform: engineering search and relevance
A search platform built with Elasticsearch for relevance, filtering, facets, pagination, and explainability.
- Selected
Convex QP Solver: numerical engineering for constrained optimization
A Python solver for convex QP with interior-point, Mehrotra, numerical validation, and reference-solver comparison.
Research & scientific collaboration
Research evidence
A few published works and research notes that make this side of the work concrete and inspectable.
Selected research
Modified α-ψ-contractive mappings with applications
First-author work on generalized contractive conditions and fixed-point theorems, with applications to integral equations.
Selected research
Best Proximity Point Results in Non-Archimedean Fuzzy Metric Spaces
Existence and uniqueness of best proximity points in non-Archimedean fuzzy metric spaces.
Selected research
Fixed point results for cyclic α-ψφ-contractions with application to integral equations
Fixed-point results for cyclic α-ψφ-contractions in metric-like spaces, with an application to integral equations.
Three complementary hubs
Research, Projects, and Learning form one connected path
Each hub has a distinct role, but they connect in both directions: Research shows scholarly work and ideas, Projects show technical execution and inspectable evidence, and Learning explains concepts, papers, and public projects for deeper understanding.
Knowledge & explanation
Learning
Mathematics, algorithms, and AI, plus explainers for referenced papers and public projects with direct source attribution.
Explore Learning →Technical evidence
Projects
Case studies, technical decisions, outcomes, and GitHub repositories for inspecting real implementation work.
Explore Projects →Scholarly evidence
Research
Publications, research notes, DOI records, and scholarly profiles that show research depth and provenance.
Explore Research →The three hubs remain independent; cross-links are used where the relationship is real and useful to the reader.
How I work
How I work
I choose the solution path based on the problem itself—from software engineering to quantitative modeling and intelligent systems, with attention to constraints and validation.
Software systems & architecture
From requirements and process analysis through architecture, implementation, and integration of reliable, changeable systems.
Modeling, algorithms & decision-making
When a problem needs quantitative formulation, I use modeling, algorithms, data analysis, and optimization to turn it into an executable solution.
Intelligent & adaptive systems
When system behavior or decisions need to become data-driven and intelligent, I focus on solutions that operate inside an engineered, evaluable system.
Capabilities
Core areas of my work
Software Development & Engineering
Designing, building, and improving real systems
Modeling, Algorithms & Optimization
Turning complex problems into models and computational methods
AI & Machine Learning
Building intelligent capabilities from data and models
Research & Technical Collaboration
Research analysis, modeling, and technical implementation
Software engineering with quantitative thinking
My background spans enterprise and commercial systems, architecture, algorithms, modeling, optimization, statistics, scientific computing, and machine learning. The value of that combination appears when the problem is more than simply writing code.
Have a problem where an off-the-shelf answer is not enough?
Describe the problem, goal, and constraints so we can discuss its structure and an appropriate path to a working solution.