Academia | Bas Nijholt

PhD Thesis Cover Design 🎨

Sat, 14 Dec 2024 00:00:00 +0000

When most people think of open source, they think of software. But what about open-sourcing your PhD thesis and creating a generative art system for its cover?

📖 Origin Story #

With my PhD defense approaching and needing around 100 copies of my thesis, I faced a design challenge. As someone more comfortable with Python than Photoshop, I turned what started as a simple visualization idea into a full-blown generative art project with machine learning-based clustering.

Multiple printed copies of the thesis

🔧 Technical Highlights #

Generated 3,671 unique covers from quantum physics simulation data
Used VGG16 neural network to extract features from covers
Implemented K-means clustering to group similar designs
Created interactive IPywidgets for design selection
Built a Binder app for real-time color scheme adjustment
Added unique edition numbers (1/120, 2/120, etc.)
Made both thesis and cover design code fully open source

The project combines:

Adaptive sampling for physics simulations
Neural networks for image feature extraction
Interactive visualization tools
Parallel processing for batch generation
Version control for scientific writing

📊 Impact #

Thesis repository: 14 GitHub stars
Cover design repository: 36 GitHub stars
Real-world adoption: Dr. Tomas Pippia from TU Delft used the code to create his own thesis cover
~100 unique printed copies, each with its own generated cover and edition number

🎯 Challenges and Solutions #

Managing 3,671 unique PDF files and their conversions
Creating a clustering system for visually similar designs
Building interactive tools for non-technical collaborators
Ensuring consistent print quality across designs
Balancing artistic appeal with scientific relevance

💡 Lessons Learned #

Open source isn’t just about code - it’s about sharing knowledge
Python can be a powerful tool for creative projects
Machine learning can help solve unexpected problems
Good visualization requires both technical skill and artistic sense
Interactive tools make collaboration with non-programmers easier

📚 Fun Fact #

Due to COVID restrictions, I defended my thesis without any guests present. The silver lining? I still have about 100 beautifully unique copies at home, each with a different generated cover and edition number. If you’d like a physical copy of this piece of quantum physics meets generative art, just let me know!

Want to create your own thesis cover or see how I wrote mine? Check out the thesis cover project and thesis source!

#OpenSource #PhD #Python #MachineLearning #DataVisualization

yaml2bib 📚

Mon, 09 Dec 2024 00:00:00 +0000

(See my intro post)

Every academic writing in LaTeX knows the pain: managing BibTeX files with hundreds of citations, each potentially formatted slightly differently, with inconsistent journal abbreviations, and duplicated entries across chapters. While procrastinating on writing my PhD thesis, I became obsessed with solving this universal academic headache.

📖 Origin Story #

When writing a PhD thesis with multiple chapters that share citations, maintaining consistency is crucial but tedious. Different chapters might cite the same paper but with slightly different BibTeX entries - one using the full journal name, another using abbreviations, or worse, having slightly different titles or author lists. Rather than manually standardizing ~350 citations, I created yaml2bib: give it a list of DOIs in YAML format, and it generates a perfect BibTeX file with consistent journal abbreviations and formatting.

The pain of manual BibTeX management

🔧 Technical Highlights #

Fetches citation data from CrossRef using DOIs
Ensures consistent journal name abbreviations
Caches API responses to avoid repeated queries
Supports custom text replacements
Combines multiple YAML sources
Works as both CLI tool and Python library
Zero manual citation formatting needed

📊 Impact #

While this tool has only 11 GitHub stars, it saved me countless hours during thesis writing and helped ensure professional consistency in my citations. It’s particularly useful for:

Large academic documents with many citations
Multi-chapter works sharing references
Collaborative papers needing citation standardization
Anyone tired of manually formatting BibTeX entries

💡 Lessons Learned #

Procrastination can be productive if channeled right (story of my life…)
A single source of truth prevents inconsistencies
Good tools make you care about details you’d otherwise skip
Every LaTeX user has fought with BibTeX at some point

Want perfectly consistent citations? Check out yaml2bib on GitHub!

#OpenSource #Python #Academia #LaTeX #Research

Open Science Publications 🔬

Fri, 06 Dec 2024 00:00:00 +0000

(See my intro post)

Today’s post is about something I have strong opinions on: making scientific research reproducible. While many researchers talk about open science, actually making your work reproducible for anyone requires significant effort.

📖 Origin Story #

During my Ph.D. and subsequent research, I noticed a frustrating pattern in scientific publications: claims of “code available upon request” that often led nowhere, or code snippets that wouldn’t run without substantial modification. I made an effort that my publications would be different – each includes complete, runnable code that reproduces every figure and result.

🔧 Technical Highlights #

Multiple repositories covering quantum physics experiments and simulations:
- orbitalfield - “Orbital effect of magnetic field on the Majorana phase diagram” (94 citations)
- supercurrent-majorana-nanowire - “Supercurrent interference in few-mode nanowire Josephson junctions” (78 citations)
- zigzag-majoranas - “Enhanced proximity effect in zigzag-shaped Majorana Josephson junctions” (56 citations)
- azure-quantum-tgp - “Protocol to identify a topological superconducting phase in a three-terminal device” (52 citations)
- spin-orbit-nanowires - “Spin-orbit protection of induced superconductivity in Majorana nanowires” (81 citations)

🔄 Evolution of Best Practices #

What I consider best practices has evolved over time. Today, I would:

Use Pixi for universal lock files across operating systems and programming languages
Provide a minimal Docker container that just installs the pixi lock file
Create self-documenting Jupyter notebooks that reproduce every result
Include clear figure-to-code mapping for paper reproducibility

🎯 Challenges and Solutions #

Balancing code cleanliness with research deadlines
Managing large datasets efficiently
Ensuring long-term reproducibility
Making complex physics simulations accessible

💡 Lessons Learned #

Clean code takes time but saves more in the long run
Documentation is as crucial as the code itself
Lock files are essential for true reproducibility
Making code public improves its quality

Want to explore quantum physics simulations? Check out the repositories above, each linked to their corresponding papers with full reproduction instructions.

#OpenSource #OpenScience #Physics #QuantumComputing #Python

Crafting unique Ph.D. thesis covers with Python 🎨

Sat, 04 Apr 2020 13:40:00 +0000

Originally posted on quantumtinkerer.tudelft.nl/blog/thesis-cover on 2020-04-04 13:40.

I will hand out around a hundred copies of my thesis to my defense committee, colleagues, and my family and friends. Let’s be honest, most people will probably not get further than attempting to read the summary and appreciating the cover. Four years of work has gone into generating the content of the thesis, so I figured, at least some thought has to go into the design of its cover. Unfortunately, I am by no means an expert on a graphical design or even competent enough to attempt to use any kind of graphic design software. But luckily for me, I do consider myself an expert in Python , and why not make the thesis design a fun process?

Initial Design: Adaptive Sampling and Data Visualization #

My vague plan was to somehow incorporate a plot resulting from a simulation run with Adaptive [source ], the software I wrote during my Ph.D. to adaptively sample functions in parallel and my proudest work. I even had a potential simulation result in mind that I would use for it, specifically, one that has been on the background of my good friend’s phone for some years and one that has earned me quite some internet points on Reddit. This plot (below) is one of the results of Chapter 4 of my thesis [source ] and this paper.

Your browser does not support the video tag.

I went back to the code that generates the plot and started hacking away in Matplotlib. Quickly, I realized that not only the plot above was going to look cool, but that many other data sets that I had also resulted in beautiful patterns. I hit the paradox of choice: I had a total of 3671 unique data sets (all for Majorana devices with either different parameter sweeps or different physical effects that were included in the model) and no idea how I was going to make a selection.

This is an early attempt (and rather ugly in hindsight)

I decided to postpone the choice of which data to use and just focus on how to plot the data first. I experimented with different colormaps, fading colormaps that gradually reveal the underlying triangulation better, and more. On a weekend night, after telling a friend I was with about my plans, we (as usually happens) started to program some of the ideas we came up with. We settled on a design where the triangulation would slowly fade from the top to bottom and where each triangle simultaneously transitioned from a solid color (the average of the values at its vertices) to densely interpolated values. See this Jupyter notebook which generated the final version of the cover. After we also chose a nice colormap (by simply creating the cover for each possible colormap,) the following was suggested: “You know what would be even cooler?! To make every cover unique.” Ten minutes later, I had started 3671 jobs on our computational cluster. When the files slowly started to appear on my computer (I have my local files synced with the cluster), we could not have been more excited. There were simply so many cool looking alternatives. Too many to manually select now, to be honest—a problem for later I thought.

Overcoming the Paradox of Choice: Clustering and Neural Networks #

Four months later, with the rest of my thesis completely done and about a day left on the deadline to submit my thesis, I found myself facing the selection problem I had successfully ignored for so long. There must be a smarter way to select the covers, I thought. Many of them look roughly similar (and some of them are even quite boring or not ecstatically pleasing), so it does not make sense to scroll through all of them. I could cluster them by similarity and then I would just have to choose the nicely looking clusters while being able to instantly discard many.

Finally, a good application to use neural networks!

I still had the 3671 covers in pdf format in some folder, so using the wonderful GNU Parallel, I converted and rasterized all of them to jpeg with

find . -iname \*pdf -print0 | parallel -0 sips -s format jpeg {} --out {.}.jpg

Then I used a pre-trained convolutional neural network model (VGG16) to extract features in a lower-dimensional representation. I fed the feature arrays to a K-Means clustering algorithm that sorted them in 80 clusters. To procrastinate a little more, I created an interactive widget using ipywidgets (see video below) to facilitate the selection process.

Your browser does not support the video tag.

These are the clusters that I selected in a grid

The selection process was done using this Jupyter notebook.

Finally, I copied the selected clusters into different folders and used the OS’s file explorer to manually select a few covers from each cluster.

Final Touches: Fine-Tuning Colors and Adding Edition Numbers #

Then I printed an initial version of my thesis, however, I wasn’t really happy with the colors.

To choose the right color scheme I got help from a non-tech savvy friend who went to art school. Due to the COVID-19 crisis, we couldn’t sit side by side, so I created this Binder “app” with sliders.

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Additionally, since all of them were different anyway, I gave them an identifier and added

edition n of 120

to the back.

These are a few examples of the final result (click on one of the images to open gallery view)

A few days later, this was delivered to my doorstep

And finally I mailed them to my Ph.D. committee

Now it’s time to start preparing for my defense at May 11, 2020 at 15:00!

Notes #

The data and code to generate all printed covers is available at basnijholt/thesis-cover
The simulation code that generates the data is available at basnijholt/spin-orbit-nanowires
The video of the Reddit post is generated using this code