
Using Topological Data Analysis to Solve Complex Problems (2013) [video] - espeed
https://www.youtube.com/watch?v=x3Hl85OBuc0
======
fitzwatermellow
Interesting background on Ayasdi (literally, “to seek,” in Cherokee, perhaps
sparking the newest trend in startup names deriving from indigenous languages)
and its DARPA roots:

[http://bits.blogs.nytimes.com/2013/01/16/ayasdi-a-big-
data-s...](http://bits.blogs.nytimes.com/2013/01/16/ayasdi-a-big-data-start-
up-with-a-long-history/?_r=0)

And a link to founder and Stanford Math Prof. Gunnar Carlsson's seminal AMS
paper on "Topology and Data":

[http://www.ams.org/journals/bull/2009-46-02/S0273-0979-09-01...](http://www.ams.org/journals/bull/2009-46-02/S0273-0979-09-01249-X/)

------
anthony_bak
Hey. That's me. On hackernews :)

Happy to answer questions here regarding TDA but am no longer with Ayasdi so
won't comment on company specific stuff.

The material in the video is dated but still relevant - I now have a better
understanding of how to use "Shape" to improve Machine Learning models in an
operational setting.

~~~
gavazzy
Any new talks, videos, or papers to recommend?

~~~
anthony_bak
Sure. Gunnar's "Topology and Data" paper (already linked to by another poster)
is the first place to go for an overview of TDA (although it's getting dated).
A more updated version (but behind paywall) is "Topological pattern
recognition for point cloud data" (
[http://dx.doi.org/10.1017/S0962492914000051](http://dx.doi.org/10.1017/S0962492914000051)
)

Robert Ghrist has another take on TDA (
[https://www.math.upenn.edu/~ghrist/preprints.html](https://www.math.upenn.edu/~ghrist/preprints.html)
) that is more "engineering" focused and also uses a different mathematical
tool set than the above (eg. Sheaves/Co-Sheaves). I particularly like the
sensor coverage results.

Rob Ghrist and John Harer (
[http://fds.duke.edu/db/aas/math/faculty/john.harer/publicati...](http://fds.duke.edu/db/aas/math/faculty/john.harer/publications.html)
)both have textbooks available if you want to get into the fundamentals in the
field.

Jose Perea has some nice results using ideas from TDA in a variety of
contexts. eg texture classification (
[https://www.math.msu.edu/user_content/docs/KleinBottleTextur...](https://www.math.msu.edu/user_content/docs/KleinBottleTextureAnalysis20150826163949977.pdf)
) and signal processing (
[https://www.math.msu.edu/user_content/docs/Sw1Pes_Theory2015...](https://www.math.msu.edu/user_content/docs/Sw1Pes_Theory20150826163903619.pdf)
)

Here's a talk I gave at ICERM last summer using persistent homology as a
feature generating method for drug discovery:

[https://icerm.brown.edu/video_archive/#/play/726](https://icerm.brown.edu/video_archive/#/play/726)

(slides available for download if you poke around on the icerm web site).

This is my "part two" of the video linked to in the parent article:

[https://www.youtube.com/watch?v=3Z73Wd2T1xE](https://www.youtube.com/watch?v=3Z73Wd2T1xE)

~~~
fitzwatermellow
Thanks for the updated list, Anthony!

On the practical side, what packages are you guys using? I'm familiar with
JavaPlex for Matlab:

[http://appliedtopology.github.io/javaplex/](http://appliedtopology.github.io/javaplex/)

~~~
anthony_bak
For Mapper consider using PythonMapper by Daniel Müllner (
[http://danifold.net/mapper/](http://danifold.net/mapper/) ). The UI is touchy
- I use it mostly via scripts only. Mapper is pretty simple to implement (just
a series of well understood pieces - the magic is in the
interpretation/understanding what you've done). As an example consider the
kepler-mapper project ( [https://github.com/MLWave/kepler-
mapper](https://github.com/MLWave/kepler-mapper) ). More lines of code are
used for calling out to the d3 visualization than implementing the core mapper
algorithm.

For my persistent homology calculations I always use Dionysus (
[http://www.mrzv.org/software/dionysus/](http://www.mrzv.org/software/dionysus/)
). Rumor has it a much improved parallelized version will be released soon.

------
irickt
Papers are available on Ayasdi's site: [http://www.ayasdi.com/approach/data-
scientist/](http://www.ayasdi.com/approach/data-scientist/)

~~~
lsprack
There's also a very nice open-source R implementation of some of the TDA
mapper functionality that I played with a while back:

[https://github.com/paultpearson/TDAmapper/](https://github.com/paultpearson/TDAmapper/)

