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
syncopation & accordionCafe de Flore (Doctor Rockit)like France, but no dog poopmore quotes

gravity: fun


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


visualization + design

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The 2017 Pi Day art imagines the digits of Pi as a star catalogue with constellations of extinct animals and plants. The work is featured in the article Pi in the Sky at the Scientific American SA Visual blog.

`\pi` Day 2016 Art Posters


Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2017 `\pi` day

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2016 `\pi` approximation day

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2016 `\pi` day

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2015 `\pi` day

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2014 `\pi` approx day

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2014 `\pi` day

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
2013 `\pi` day

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Circular `\pi` art

On March 14th celebrate `\pi` Day. Hug `\pi`—find a way to do it.

For those who favour `\tau=2\pi` will have to postpone celebrations until July 26th. That's what you get for thinking that `\pi` is wrong.

If you're not into details, you may opt to party on July 22nd, which is `\pi` approximation day (`\pi` ≈ 22/7). It's 20% more accurate that the official `\pi` day!

Finally, if you believe that `\pi = 3`, you should read why `\pi` is not equal to 3.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
All art posters are available for purchase.
I take custom requests.

This year's `\pi` day art collection celebrates not only the digit but also one of the fundamental forces in nature: gravity.

In February of 2016, for the first time, gravitational waves were detected at the Laser Interferometer Gravitational-Wave Observatory (LIGO).

The signal in the detector was sonified—a process by which any data can be encoded into sound to provide hints at patterns and structure that we might otherwise miss—and we finally heard what two black holes sound like. A buzz and chirp.

The art is featured in the Gravity of Pi article on the Scientific American SA Visual blog.

this year's theme music

All the art was processed while listening to Roses by Coeur de Pirate, a brilliant female French-Canadian songwriter, who sounds like a mix of Patricia Kaas and Lhasa. The lyrics Oublie-moi (Forget me) are fitting with this year's theme of gravity.

Mais laisse-moi tomber, laisse-nous tomber
Laisse la nuit trembler en moi
Laisse-moi tomber, laisse nous tomber
Cette fois

But let me fall, let us fall
Let the night tremble in me
Let me fall, let us fall
This time


Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
5 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
5 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
14 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
14 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
14 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
44 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
56 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
65 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
98 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
153 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
147 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
335 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
1,000 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
1,000 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
300 digits of `\pi`, `\phi` and `e`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
100 digits of `\pi`, `\phi` and `e`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
49 instances of 3 digits of `\pi`. (zoom, BUY ARTWORK)

Pi Day 2016 Art Posters
 / Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca buy artwork
49 instances of 3 digits of `\pi`. (zoom, BUY ARTWORK)
VIEW ALL

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...)

Ensemble methods: Bagging and random forests

Wed 22-11-2017
Many heads are better than one.

We introduce two common ensemble methods: bagging and random forests. Both of these methods repeat a statistical analysis on a bootstrap sample to improve the accuracy of the predictor. Our column shows these methods as applied to Classification and Regression Trees.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Nature Methods Points of Significance column: Ensemble methods: Bagging and random forests. (read)

For example, we can sample the space of values more finely when using bagging with regression trees because each sample has potentially different boundaries at which the tree splits.

Random forests generate a large number of trees by not only generating bootstrap samples but also randomly choosing which predictor variables are considered at each split in the tree.

Krzywinski, M. & Altman, N. (2017) Points of Significance: Ensemble methods: bagging and random forests. Nature Methods 14:933–934.

Background reading

Krzywinski, M. & Altman, N. (2017) Points of Significance: Classification and regression trees. Nature Methods 14:757–758.

...more about the Points of Significance column