For problems where a generic answer is not enough
Mathematical modeling, optimization, and algorithms for complex problems
Problem formulation, algorithm design, optimization, and numerical computation for resource allocation, scheduling, decision support, and specialized computational problems.
The problem
Sometimes the problem itself has to be modeled
In this kind of work, choosing an algorithm or library before understanding the problem structure can limit the result.
Resource or decision optimization
When multiple decisions, constraints, and objectives need to be balanced into a defensible solution.
Scheduling and allocation
When resources are limited, constraints are numerous, or objectives compete.
A problem that needs a specialized algorithm
When generic methods are not sufficient for the required scale, structure, or accuracy.
A numerical model that must be trustworthy
When conditioning, stability, validation, and numerical behavior are part of the problem.
Scope
What can this include?
Mathematical and computational modeling
Turning a real problem into variables, objectives, constraints, and a computable structure.
Algorithm design and implementation
Selecting, adapting, or designing a method around the problem structure and computational limits.
Optimization and metaheuristics
Approaches for allocation, scheduling, and difficult search problems that need measurable evaluation.
Numerical validation and benchmarking
Testing, comparison, sensitivity checks, and evaluation of algorithmic behavior and output.
Approach
From model to usable result
- 01
Formulate
Define objectives, variables, constraints, data, and success criteria in a computable form.
- 02
Choose the method
Select or design a numerical method or algorithm around the problem structure and computational limits.
- 03
Implement and test
Turn the model into executable code and evaluate behavior and solution quality through tests and benchmarks.
- 04
Validate
Document numerical diagnostics, reference comparisons, and the limits of the approach.
Evidence
Relevant evidence
These projects show the path from mathematical structure and algorithms to testable implementations.
Convex QP Solver
An inspectable case study covering QP formulation, primal-dual interior-point methods, numerical diagnostics, and validation against reference solvers.
View case studyEvent-Driven Metaheuristic Optimization
Inspectable code and documentation for event-driven optimization work around resource allocation and scheduling, with benchmarks and testability.
View code on GitHubResearch and modeling
Research background related to mathematical modeling, analysis, numerical methods, and algorithm design.
View research background
This is a close match when…
- Your problem has clear constraints and goals but no ready-made solution.
- Optimization, resource allocation, or scheduling is central to the problem.
- Data and formulation need a computational model or algorithm.
- Numerical correctness and defensible results matter, not only producing an output.
Describe the problem
You do not need to know the mathematical model or algorithm upfront. The problem, data, constraints, and desired outcome are enough to start the conversation.