The Julia Programming Language Courses

Hands-on Julia for High-Performance Computing on GPUs and CPUs

Hands-on workshop exploring Julia for high-performance computing on CPUs and GPUs. Learn resource management, distributed computing, code optimization, and develop a parallel application on NERSC's Perlmutter supercomputer.

• YouTube
• 3 hours 35 minutes
• Self-Paced
• Free Video

Julia and Rust - Safer Abstractions and FFI Workshop

Explore Rust integration with Julia, covering LSP setup, common pitfalls, binding generation, benchmarking, and performance optimization techniques for seamless interoperability.

• YouTube
• 3 hours 11 minutes
• Self-Paced
• Free Video

Julia for Systems Biology - Generating Personalized Models

Hands-on workshop on creating personalized computational models in Julia for biomedical applications, covering ODE implementation, parameter estimation, and model selection strategies.

• YouTube
• 2 hours 50 minutes
• Self-Paced
• Free Video

Scientific Machine Learning Using Functional Mock-Up Units - JuliaCon 2024

Hands-on workshop integrating physical system knowledge into machine learning using Functional Mock-Up Units. Explore hybrid modeling with a robotic arm case study, combining simulation and neural networks.

• YouTube
• 3 hours 3 minutes
• Self-Paced
• Free Video

Productive Parallel Programming with Dagger.jl - JuliaCon 2024 Workshop

Learn to write fast, parallel Julia code using Dagger.jl. Explore various programming interfaces, build example programs, and gain insights into parallelism through logging and profiling capabilities.

• YouTube
• 3 hours 1 minute
• Self-Paced
• Free Video

Writing and Maintaining Exceptional Documentation - JuliaCon 2024 Workshop

Learn to create stellar documentation for your Julia codebase. Master core principles, write effective docstrings, and use Documenter.jl to generate and deploy professional-quality docs online.

• YouTube
• 2 hours 55 minutes
• Self-Paced
• Free Video

Applied Optimization with JuMP at SINTEF

Explore JuMP for flexible optimization modeling at SINTEF, focusing on hydrogen infrastructure and circular logistics. Learn about complementary packages enhancing JuMP's functionality.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

Convex Network Flows - Optimization Framework and Applications

Introducing a general framework for modeling flow problems over hyper graphs, with applications in network optimization, power flow, and wireless networks. Includes fast, parallelizable algorithm implementation in Julia.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

Solving the Market-to-Market Problem in Large Scale Power Systems

Explore novel algorithms for geographic decomposition in large-scale power systems, addressing inefficiencies in inter-regional interconnections using JuMP and PowerSimulations.jl.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

QUBO.jl - A Julia Ecosystem for Quadratic Unconstrained Binary Optimization

Explore Julia's QUBO.jl ecosystem for solving binary optimization problems, featuring solver interfaces, benchmarking tools, and quantum hardware integration for advanced optimization techniques.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

Pyomo: Design Paradigms and Lessons Learned

Explore design paradigms and lessons from Pyomo, covering model composition, optimization workflows, and new ideas in algebraic modeling languages for efficient problem-solving.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

InfiniteExaModels.jl - Accelerating Infinite-Dimensional Optimization on CPU and GPU

Accelerate infinite-dimensional optimization with InfiniteExaModels.jl, leveraging repetitive patterns for efficient derivative evaluations on CPU & GPU.

• YouTube
• 26 minutes
• Self-Paced
• Free Video

The State of JuMP - Progress and Developments in Julia Optimization

Explore recent advancements and developments in JuMP, the Julia-based mathematical optimization package, over the past year.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Why You Should Stop Using the Monomial Basis

Explore StarAlgebras.jl for computing with arbitrary bases, improving polynomial optimization through symmetry-adapted bases and better-conditioned alternatives to monomials.

• YouTube
• 24 minutes
• Self-Paced
• Free Video

Teaching Optimization: Comparing cvxpy, pyomo, and JuMP

Explore convex optimization modeling frameworks, comparing cvxpy, pyomo, and JuMP. Learn about common student challenges and how JuMP addresses them in undergraduate and graduate courses.

• YouTube
• 21 minutes
• Self-Paced
• Free Video