Machine Learning for Mathematics

Apply machine learning techniques to mathematical objects such as polynomials and polytopes.

normal master phd

We explore how machine learning methods can be applied to mathematical objects (polynomials, polytopes, integer programs) to improve algorithmic performance or discover new structure.

Milestones

ID Title Due
M1 Replicate PO counting on toy knapsack 2025-11-25
M2 Bound extraction & correctness tests 2025-12-20
M3 Benchmark suite + analysis 2026-01-30
M4 Final report & release 2026-02-28

Tasks

ID Title Start End Status
T1 Implement counting for parametric families 2025-11-05 2025-11-25 todo
T2 Extract bounds from generating functions 2025-11-26 2025-12-20 todo
T3 Benchmark vs. B&B across sizes 2025-12-21 2026-01-30 todo
T4 Ablations + write-up 2026-01-31 2026-02-28 todo

Deliverables

  • PO-bound computation module
  • Benchmark across parametric families
  • Report with comparisons vs. branch-and-bound
5

We explore how machine learning methods can be applied to mathematical objects (polynomials, polytopes, integer programs) to improve algorithmic performance or discover new structure.

Milestones

ID Title Due
M1 Replicate PO counting on toy knapsack 2025-11-25
M2 Bound extraction & correctness tests 2025-12-20
M3 Benchmark suite + analysis 2026-01-30
M4 Final report & release 2026-02-28

Tasks

ID Title Start End Status
T1 Implement counting for parametric families 2025-11-05 2025-11-25 todo
T2 Extract bounds from generating functions 2025-11-26 2025-12-20 todo
T3 Benchmark vs. B&B across sizes 2025-12-21 2026-01-30 todo
T4 Ablations + write-up 2026-01-31 2026-02-28 todo

Deliverables

  • PO-bound computation module
  • Benchmark across parametric families
  • Report with comparisons vs. branch-and-bound