Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - contact me Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca on Twitter Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Lumondo Photography Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Pi Art Martin Krzywinski / Genome Sciences Center / mkweb.bcgsc.ca - Hilbertonians - Creatures on the Hilbert Curve
listen; there's a hell of a good universe next door: let's go.e.e. cummingsgo theremore quotes

curves: fun



In Silico Flurries: Computing a world of snow. Scientific American. 23 December 2017


art + design

Math geek? If you like the clean geometric design of the type posters, you may enjoy something even more mathematical. Design that transcends repetition: Art of Pi, Phi and e posters.

Visions of Type

the intertypes

Letters give a type weight. Intertypes give a type cohesion.

The intertypes (my own term) are the spaces between letters. This poster shows all 26 ×26 = 686 intertypes for Helvetica Neue.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
The intertypes of Helvetica Neue. (BUY ARTWORK)

type peep show—the private curves of letters

Sometimes to understand the whole, we need to look more closely at its parts.

Completely safe for work.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Bodoni type peep show. (BUY ARTWORK)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Century type peep show. (BUY ARTWORK)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Dax type peep show. (BUY ARTWORK)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Franklin Gothic type peep show. (BUY ARTWORK)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Futura type peep show. (BUY ARTWORK)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Gill Sans type peep show. (BUY ARTWORK)

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Helvetica type peep show. (BUY ARTWORK)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Minion type peep show. (BUY ARTWORK)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Platelet type peep show. (BUY ARTWORK)

I include Emigre's Platelet because it's such a goofy and fun font. One look at the lower case b and you know this isn't a type face that wears a tie.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Type peep show of nine faces. (BUY ARTWORK)
Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
Type peep show of nine faces. (BUY ARTWORK)
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news + thoughts

Machine learning: supervised methods (SVM & kNN)

Thu 18-01-2018
Supervised learning algorithms extract general principles from observed examples guided by a specific prediction objective.

We examine two very common supervised machine learning methods: linear support vector machines (SVM) and k-nearest neighbors (kNN).

SVM is often less computationally demanding than kNN and is easier to interpret, but it can identify only a limited set of patterns. On the other hand, kNN can find very complex patterns, but its output is more challenging to interpret.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Machine learning: supervised methods (SVM & kNN). (read)

We illustrate SVM using a data set in which points fall into two categories, which are separated in SVM by a straight line "margin". SVM can be tuned using a parameter that influences the width and location of the margin, permitting points to fall within the margin or on the wrong side of the margin. We then show how kNN relaxes explicit boundary definitions, such as the straight line in SVM, and how kNN too can be tuned to create more robust classification.

Bzdok, D., Krzywinski, M. & Altman, N. (2018) Points of Significance: Machine learning: a primer. Nature Methods 15:5–6.

Background reading

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

...more about the Points of Significance column

Human Versus Machine

Tue 16-01-2018
Balancing subjective design with objective optimization.

In a Nature graphics blog article, I present my process behind designing the stark black-and-white Nature 10 cover.

Nature 10, 18 December 2017

Machine learning: a primer

Thu 18-01-2018
Machine learning extracts patterns from data without explicit instructions.

In this primer, we focus on essential ML principles— a modeling strategy to let the data speak for themselves, to the extent possible.

The benefits of ML arise from its use of a large number of tuning parameters or weights, which control the algorithm’s complexity and are estimated from the data using numerical optimization. Often ML algorithms are motivated by heuristics such as models of interacting neurons or natural evolution—even if the underlying mechanism of the biological system being studied is substantially different. The utility of ML algorithms is typically assessed empirically by how well extracted patterns generalize to new observations.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Machine learning: a primer. (read)

We present a data scenario in which we fit to a model with 5 predictors using polynomials and show what to expect from ML when noise and sample size vary. We also demonstrate the consequences of excluding an important predictor or including a spurious one.

Bzdok, D., Krzywinski, M. & Altman, N. (2017) Points of Significance: Machine learning: a primer. Nature Methods 14:1119–1120.

...more about the Points of Significance column

Snowflake simulation

Tue 16-01-2018
Symmetric, beautiful and unique.

Just in time for the season, I've simulated a snow-pile of snowflakes based on the Gravner-Griffeath model.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
A few of the beautiful snowflakes generated by the Gravner-Griffeath model. (explore)

The work is described as a wintertime tale in In Silico Flurries: Computing a world of snow and co-authored with Jake Lever in the Scientific American SA Blog.

Gravner, J. & Griffeath, D. (2007) Modeling Snow Crystal Growth II: A mesoscopic lattice map with plausible dynamics.

Genes that make us sick

Wed 22-11-2017
Where disease hides in the genome.

My illustration of the location of genes in the human genome that are implicated in disease appears in The Objects that Power the Global Economy, a book by Quartz.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The location of genes implicated in disease in the human genome, shown here as a spiral. (more...)