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
Here we are now at the middle of the fourth large part of this talk.Pepe Deluxeget nowheremore quotes

art: exciting



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


art + science

Bloomberg Businessweek Design Conference — San Francisco, 2013

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Design loves science and science loves design, but doesn't always know it. (Bloomberg Businessweek)

science design

Together with Alberto Cairo, I presented at the Bloomberg Businessweek Design Conference (highlights) on the topic of design and communication in the sciences.

Alberto, as the journalist, motivated why communication should include access to detail through an engaging narrative. He made the distinction between the specialist (heavy on detail) and the communicator (focus on narrative) and emphasized that the distinction is artificial, though often played out (watch video).

I, as the scientist, underscored the importance of clear communication between scientists. As the specialists, they are often very poor communicators. Pick up any science journal and you'll quickly discover that scientists either aren't good at telling stories or are discouraged to do so by the medium. The consequence is the same: papers read like a wall of text. TL;DR anyone? The quality of visual communication in general ranges from muddled to abysmal (watch video).

We need more leaders in the field, such as Nature Publishing Group, to reward and emphasize good visual communication (e.g. Nature Cancer Review 2013 Figure Calendar).

Our presentations concluded with a 15 minute moderated discussion with Sam Grobart, senior Businesssweek writer. Everyone got a little cheeky. Good fun.

presentation video

Watch: my presentation, conversation with Alberto Cairo, moderated by Sam Grobart. (Bloomberg TV), Albert Cairo's presentation.

presentation slides

This was a lightning 7 minute talk. I did more planning about what to say than I usually do, given that there was virtually no opportunity for any kind of backtracking, and include a running narrative below each slide.

Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
Martin Krzywinski - Bloomberg Businessweek Design Conference 2013
1/32

download presentation

My slides are available as PDF, keynote (zipped) or Quicktime. The format is 16:9 HD.

Bloomberg Businessweek Design Issue

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
The reality of redesign is disruptive. How can we pursue new ideas and opportunities without leaving consumers confused or angry? Businessweek puts that question to some of the world's most accomplished designers. (Bloomberg Businessweek Design Issue)

On 28 Jan 2013, Bloomberg Businessweek Design Issue will capture the ideas from the conference and the personalities that generated them.

During the conference, each talk was captured in a series of sketches by Tom Wujec: my talk sketch and moderated discussion sketch.

Martin Krzywinski @MKrzywinski mkweb.bcgsc.ca
Date completed: ongoing — an accurate assessment of the state of the visual communication field in science. (read article)
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...)