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#ComputationalScience

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Also, half of them are women, which is much better than the averages in natural sciences (not to speak of engineering). The main reason for this is that the program taps into Bachelor programs with a large number of women, Biology for example.

My opinion today is that this is a very interesting strategy to increase the number of women in tech. Most talk is about trying to get the numbers in, say, Computer Science or Electrical Engineering up (which are abysmal). But there is the much lower hanging fruit of looking at programs with a large number of women, and to then increase the amount of programming and mathematical skills that these students learn.

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In autumn 2023, two new master programs started at the faculty of science at #LundUniversity namely Computational Science (strong math background) and Applied Computational Science (weak math background). The students are now working on their master thesis and recently, we had a full day where all reported on their progress. To formulate the goal of the program succinctly, it is to have the students learn state of the art techniques for modelling and simuation and apply these to interesting problems. And, what can I say, the concept really works. It was a joy to see.

Here are some of the applications they are working on:
Paper production
Cooling of data centers
Cancer detection
Effect of microparticles on pregnant women
Tree ring series
Tree modelling in climate simulations
4D X-Ray
Peptides in Pharmacology
Room acoustics

And the methods are extremely varied as well. From numerical solvers for partial differential equations, to numerical solvers for neural networks, to purely data driven neural network approaches, and from differential equation based modelling to pure data analysis.

The advisors come from all institutions of the faculty of science, and from local companies.

1/3

Revised preprint: "Establishing trust in automated reasoning"

osf.io/preprints/metaarxiv/nt9

More and more scientific reasoning is automated via software and machine learning. Users of these tools rarely understand their inner workings in detail. They are exempt from peer review. Can we trust them? On what basis? Under which conditions? Can we do better? These are the questions I address in this paper.

#MetaScience #ComputationalScience

A little summary 🧵

New paper out: The Nature of Computational Models
doi.org/10.1109/MCSE.2023.3286

Preprint: hal.science/hal-04148865

#ComputationalScience

Computational models lie at the heart of computational science, yet few scientists have a clear idea of what a computational model actually is. I argue that in the most general case, computational models are formal specifications. I explain the relations between specifications, algorithms, software, and mathematical functions and equations, and I discuss examples.

Ahhhh #Introduction
I'm a phd student in #hpc and #computationalscience.
Making computers go brrr for large scale data analytics (#clustering, nearest neighbors, #imageprocessing, #linearalgebra) & dabble in tools for task based #parallelism. Hope to see some of ya'll @ #SC22!

I listen to more #ska than is acceptable in 2022, and will lie about having better music tastes than the digimon movie soundtrack. @ me with loose leaf #tea and #book recs (always reading #fiction & #webserials)