Media Summary: Seminar given by Rachel Kurchin ( to the CESMIX ( collaboration on ... Introducing Quarto's Native Julia Engine: Easier, Chris Rackauckas talks about his plan to extend the Julia SciML

The Juliagraphs Ecosystem Build Fast - Detailed Analysis & Overview

Seminar given by Rachel Kurchin ( to the CESMIX ( collaboration on ... Introducing Quarto's Native Julia Engine: Easier, Chris Rackauckas talks about his plan to extend the Julia SciML From logistics to bioinformatics or web analytics, graphs are versatile abstractions for modelling problems and solving them with ... The graph of Julia by Huda PreTalx: Julia's dynamism, extensive specialization, ... In this video we will take a deep dive into optimising away heap allocations from our code. In previous videos we discussed ...

LightGraphs defines abstractions to implement for any graph or network. Instead of re-inventing the wheel, we can share existing ... This poster was presented at JuliaCon2021. Abstract: The Jupyter and Julia communities provide all the tools required to set up a ... Questions? Please register for JuliaCon: and you will receive the link for Q/A via email. See you ... Julia is increasingly being recognized as one of the big three data science programming languages alongside R and Python.

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The JuliaGraphs ecosystem: build fast - don't break things | James Fairbanks
CESMIX: Building a Materials Computation Ecosystem in Julia by Rachel Kurchin
12. Optimisation Tips & Tricks [HPC in Julia]
Introducing Quarto’s Native Julia Engine: Easier, Faster, Better | Krumbiegel
FerriteCon 2024 Chris Rackauckas: Extending the Julia SciML Ecosystem to a Backbone for PDEs
Building and Analyzing Graphs at Scale | Workshop | JuliaCon 2020
The graph of Julia |  | JuliaCon Global 2025
11. Hunting Down Heap Allocations [HPC in Julia]
Graph Interfaces: Bespoke Graphs for Every Occasion | James Fairbanks | JuliaCon 2018
Build your own fast, multi-user Jupyter and Pluto server | Bart Janssens | JuliaCon2021
Modeling Marine Ecosystems At Multiple Scales Using Julia | Workshop | JuliaCon 2021
The Case for Julia — Not Harder, but Better, Faster, and Stronger
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The JuliaGraphs ecosystem: build fast - don't break things | James Fairbanks

The JuliaGraphs ecosystem: build fast - don't break things | James Fairbanks

The JuliaGraphs ecosystem

CESMIX: Building a Materials Computation Ecosystem in Julia by Rachel Kurchin

CESMIX: Building a Materials Computation Ecosystem in Julia by Rachel Kurchin

Seminar given by Rachel Kurchin (https://rkurchin.github.io/) to the CESMIX (https://computing.mit.edu/cesmix/) collaboration on ...

12. Optimisation Tips & Tricks [HPC in Julia]

12. Optimisation Tips & Tricks [HPC in Julia]

In this video we

Introducing Quarto’s Native Julia Engine: Easier, Faster, Better | Krumbiegel

Introducing Quarto’s Native Julia Engine: Easier, Faster, Better | Krumbiegel

Introducing Quarto's Native Julia Engine: Easier,

FerriteCon 2024 Chris Rackauckas: Extending the Julia SciML Ecosystem to a Backbone for PDEs

FerriteCon 2024 Chris Rackauckas: Extending the Julia SciML Ecosystem to a Backbone for PDEs

Chris Rackauckas talks about his plan to extend the Julia SciML

Building and Analyzing Graphs at Scale | Workshop | JuliaCon 2020

Building and Analyzing Graphs at Scale | Workshop | JuliaCon 2020

From logistics to bioinformatics or web analytics, graphs are versatile abstractions for modelling problems and solving them with ...

The graph of Julia |  | JuliaCon Global 2025

The graph of Julia | | JuliaCon Global 2025

The graph of Julia by Huda PreTalx: https://pretalx.com/juliacon-2025/talk/WMZ3FC/ Julia's dynamism, extensive specialization, ...

11. Hunting Down Heap Allocations [HPC in Julia]

11. Hunting Down Heap Allocations [HPC in Julia]

In this video we will take a deep dive into optimising away heap allocations from our code. In previous videos we discussed ...

Graph Interfaces: Bespoke Graphs for Every Occasion | James Fairbanks | JuliaCon 2018

Graph Interfaces: Bespoke Graphs for Every Occasion | James Fairbanks | JuliaCon 2018

LightGraphs defines abstractions to implement for any graph or network. Instead of re-inventing the wheel, we can share existing ...

Build your own fast, multi-user Jupyter and Pluto server | Bart Janssens | JuliaCon2021

Build your own fast, multi-user Jupyter and Pluto server | Bart Janssens | JuliaCon2021

This poster was presented at JuliaCon2021. Abstract: The Jupyter and Julia communities provide all the tools required to set up a ...

Modeling Marine Ecosystems At Multiple Scales Using Julia | Workshop | JuliaCon 2021

Modeling Marine Ecosystems At Multiple Scales Using Julia | Workshop | JuliaCon 2021

Questions? Please register for JuliaCon: https://juliacon.org/2021/tickets/ and you will receive the link for Q/A via email. See you ...

The Case for Julia — Not Harder, but Better, Faster, and Stronger

The Case for Julia — Not Harder, but Better, Faster, and Stronger

CSCI 202.

Towards Faster Sorting and Group-by operations | JuliaCon 2019

Towards Faster Sorting and Group-by operations | JuliaCon 2019

Julia is increasingly being recognized as one of the big three data science programming languages alongside R and Python.