Tools, books and links I keep coming back to, for writing papers, doing data science and staying curious.
My toolbox
- Datawrapper, charts, maps and tables for the web
- BioRender, scientific figures and illustrations
- LaTeX Tables Generator
- Academic Phrasebank (University of Manchester)
- Gliffy, online diagram software
My bookshelf
- Interpretable Machine Learning, Molnar (free online)
- Practical Guide to Applied Conformal Prediction in Python, Manokhin
- Pattern Recognition and Machine Learning, Bishop
- An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cristianini & Shawe-Taylor
- Practical Statistics for Data Scientists, Bruce, Bruce & Gedeck
- Exploratory Data Analysis, Tukey
- Networks of the Brain, Sporns
- Netter’s Atlas of Neuroscience, Felten et al.
- Knowledge is Beautiful, McCandless
My favorite links
- Random Forests, the original page by Leo Breiman and Adele Cutler
- MLU-Explain, visual, interactive explanations of machine learning concepts
- Christoph Molnar, interpretable machine learning
- IFLScience
- The Mary Sue
My projects
- Five editions of XAIB (2021–2025)
- K-Surfer, my free KNIME plugin for FreeSurfer brain MRI data
- Special Session XAIB, Brain Informatics 2021
- Research Topic: Brain Hemispheric Specialization and its Pathological Change
- Special Issue “Machine Learning in Healthcare and Biomedical Application”, Algorithms (MDPI)
- A Machine Learning Neuroimaging Challenge
Favorite quotes
If you torture the data long enough, it will confess.
Attributed to Ronald H. Coase
Roughly 80% of the effects come from 20% of the causes.
The Pareto principle, after Vilfredo Pareto
The goal is not interpretability, but accurate information.
Leo Breiman, Statistical Modeling: The Two Cultures (2001)
