About GSoC Contributions Blog Skills Contact
Google Summer of Code 2026 · Selected

Harsh Singh

Pre-final year IT undergraduate focused on scientific computing, numerical methods, and high-performance Julia. Contributor to OrdinaryDiffEq.jl, SciMLBenchmarks.jl, and ODEInterfaceDiffEq.jl.

Building infrastructure for scientific computing

I'm an IT undergraduate at Sarvajanik College of Engineering and Technology (expected 2027), focused on numerical methods, solver infrastructure, and performance engineering.

As a Codeforces Specialist and competitive programmer, I bring algorithmic rigour to every problem. My open-source work centres on the Julia ecosystem — specifically the SciML organization — where I contribute high-performance solver code used by researchers and engineers worldwide.

My interests span scientific computing, differential equations, high-performance computing, and building robust, zero-allocation numerical infrastructure at scale.

Education

B.Tech IT — SCET, Gujarat (2023–2027)

Competitive Programming

Codeforces Specialist — algorithmic problem solving

Research Interests

Numerical methods, solver design, performance

Open Source

Active contributor across the SciML / Julia ecosystem

Recent posts

View all posts

Native Julia ODE, SDE, DAE, DDE & PDE solvers

Refactoring the core of OrdinaryDiffEq.jl with generic tableau-driven solver infrastructure.

OrganizationSciML (NumFOCUS)
Year2026
StatusActive

The project refactors OrdinaryDiffEq.jl by introducing generic tableau-driven solver infrastructure to replace duplicated perform_step! implementations across Runge–Kutta and IMEX solver families. Targeting ~3,300 lines of duplicated stepping code for removal while maintaining performance parity with handwritten kernels.

Key Goals

  • Introduce generic IMEXTableau abstraction
  • Replace duplicated perform_step! implementations
  • Refactor RK-based solver families
  • Add multirate methods (MREIL, MIS, MRGARK)
  • Ensure zero-allocation implementations
  • Maintain performance parity with handwritten kernels
Generic Tableau Dispatch IMEX Runge–Kutta Multirate Integration Zero-Allocation Kernels Performance-Critical Julia Scientific Computing
Mentors Chris Rackauckas Kanav Gupta Utkarsh Oscar Smith

Project Timeline

May 2026

Community Bonding

Deep-dive into OrdinaryDiffEq.jl internals, align with mentors on design, benchmark existing solvers.

June 2026

IMEXTableau Stabilization

Finalize the generic IMEXTableau type, implement core dispatch infrastructure, first solver migrations.

July 2026

RK Family Migration

Migrate explicit, implicit, and IMEX Runge–Kutta solvers to the generic tableau framework.

August 2026

Multirate Implementations

Implement MREIL, MIS, and MRGARK multirate time integration methods.

September 2026

Benchmarks & Documentation

Comprehensive performance benchmarks, documentation, and final polish.

Open-source impact

Pull requests merged across the SciML ecosystem.

Contribution Activity Less More

Notes from the field

Deep dives into numerical computing, Julia performance, and GSoC progress.

Technical toolkit

Languages

Julia C++ Python Go Scala JavaScript

Domains

Scientific Computing Numerical Methods Differential Equations High Performance Computing Open Source Development Performance Optimization Solver Design Data Structures & Algorithms

Milestones

Google Summer of Code 2026

Selected contributor under SciML (NumFOCUS)

Codeforces Specialist

Competitive programming — algorithmic excellence

Top 100 All-India (Physics)

CUET — national-level ranking

JEE Main Qualified

National engineering entrance examination

35+ Merged PRs in SciML

Active contributor across the ecosystem

Let's connect

Open to collaboration, research discussions, and opportunities.