what is a computational biologist doing at the New York Times? - - PowerPoint PPT Presentation

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what is a computational biologist doing at the new york
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what is a computational biologist doing at the New York Times? - - PowerPoint PPT Presentation

Oct 09, 2022 231 likes •907 views

what is a computational biologist doing at the New York Times? (and what can academia do for a 163-year old company?) chris.wiggins@columbia.edu chris.wiggins@nytimes.com chris.wiggins@hackNY.org @chrishwiggins context/background

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what is a computational biologist doing at the New York Times?

  • (and what can academia do for a

163-year old company?)

chris.wiggins@columbia.edu chris.wiggins@nytimes.com chris.wiggins@hackNY.org @chrishwiggins

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context/background

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context/background

(before ‘the talk’)

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biology: 1892 vs. 1995 biology changed for good.

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genetics: 1837 vs. 2012 from “segments” to algorithms

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genetics: 1837 vs. 2012 from intuition to prediction

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data science: web scale

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example: 163 yr old

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bit.ly/nyt-interactive-2013

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R+D: nytlabs.com

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developer.nytimes.com: 2008

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example: millions of views per hour

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from “segments” to algorithms

insert figure here

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from intuition to prediction

insert figure here

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data science: the web

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data science: the web is your “online presence”

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data science: the web is a microscope

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data science: the web is an experimental tool

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data science: the web is an optimization tool

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</header>

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</header>

i.e., <body>

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common requirements in data science:

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common requirements in data science:

  • 1. practices
  • 2. skills
  • 3. culture
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common requirements in data science:

  • 1. practices
  • 2. skills
  • 3. culture
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common requirements in data science:

  • 1. practices
  • 2. skills
  • 3. culture
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data science: practice

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data science: practice

  • reframe domain questions

as machine learning tasks

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data science: practice

  • better wrong than "nice"
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data science: practice

  • be relevant
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data science: practice

  • be relevant
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data science: practice

  • be relevant
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data science: practice

  • hypotheses are not data jeopardy
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data science: practice

  • befriend experimentalists
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data science: practice

  • befriend experimentalists
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data science: practice

  • befriend experimentalists
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data science: skills

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data science: skills

  • find quantifiables
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data science: skills

  • find quantifiables (choose carefully)
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data science: skills

  • straw man first
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data science: skills

  • straw man first
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data science: skills

  • small wins before feature engineering
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data science: skills

  • data engineering before data science
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data science: culture

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data science: culture

  • be communicative
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data science: culture

  • be communicative

(promote rhetorical literacy)

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data science: culture

  • be communicative

(promote rhetorical literacy)

  • related: strive to build models

which are both predictive and interpretable

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data science: culture

  • be skeptical

(promote critical literacy)

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data science: culture

  • be empowering
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data science: culture

  • be transparent
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data science: culture

  • promote literacy:

functional critical rhetorical

  • (cf. Selber, Multiliteracies for a Digital Age. 2004)
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data science: culture

  • promote literacies:
  • 1. functional
  • 2. critical
  • 3. rhetorical
  • (cf. Selber, Multiliteracies for a Digital Age. 2004)
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data science: culture

  • promote literacies:
  • 1. functional
  • 2. critical
  • 3. rhetorical
  • (cf. Selber, Multiliteracies for a Digital Age. 2004)
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data science: culture

  • promote literacies:
  • 1. functional
  • 2. critical
  • 3. rhetorical
  • (cf. Selber, Multiliteracies for a Digital Age. 2004)
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data science: culture

  • promote literacies:
  • 1. functional
  • 2. critical
  • 3. rhetorical
  • (cf. Selber, Multiliteracies for a Digital Age. 2004)
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</body>

i.e., <footer>

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summary:

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summary: pay attention to:

  • 1. practices
  • 2. skills
  • 3. culture
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practices:

  • 1. reframe questions as ML
  • 2. better wrong than "nice"
  • 3. be relevant
  • 4. aim for hypothesis vs data jeapordy
  • 5. befriend experimentalists
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skills:

  • 1. find quantifiables
  • 2. straw man first
  • 3. small wins before feature engineering
  • 4. data engineering before data science
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culture:

  • 1. be communicative
  • 2. be skeptical
  • 3. be empowering
  • 4. be transparent
  • 5. promote literacies
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find out more!

  • 1. postdoc/student opportunities:

chris.wiggins@columbia.edu

  • 2. always hiring:

chris.wiggins@nytimes.com

  • 3. let’s talk:
  • @chrishwiggins
  • gist.github.com/chrishwiggins/
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what is a computational biologist doing at the New York Times?

  • (and what can academia do for a

163-year old company?)

chris.wiggins@columbia.edu chris.wiggins@nytimes.com chris.wiggins@hackNY.org @chrishwiggins