12-01, 10:55–11:10 (Europe/Amsterdam), Ernst-Curie
In this talk, we will reframe choosing a programming language as an
optimization problem in technical computing and define important variables
and constraints as well as the objective function in order for us to
choose the correct programming language as a researcher.
Then we will explore the advantages of using Julia, a high-performance
programming language that combines ease of use with the speed of static
languages.
Let's assume, that our competitor has more resources, time and knowledge than we do.
How can we succeed with such limited amount of time and money?
This talk does not offer any of the shelf solutions, but motivates a specific mindset for solving challenges in technical computing.
Our main focus lies on:
- How does Julia's increases developer productivity?
- What does it mean to be productive in scientific computing?
- Working with time and resource constraints and staying productive
- Does Julia mitigate the two-language problem?
- How do MicroBenchmarks allow us to improve as programmers?
Finally, we will showcase how Julia can be used rapid prototyping using Pluto's
reactive notebooks for sharing our research with the broader community.
By the end of this talk, you will have a better understanding how to choose
the correct programming language and software libraries
to be productive as a researcher.
I am a student, who is researching quantum computers at the University of Bremen. I mentor students in programming using different languages such as Python, Julia and Elixir. My Interests are automated electronic design, parallel, distributed and scientific computing, formal languages and natural languages processing.