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My research talk for CCA promotion
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Dan Foreman-Mackey
February 03, 2022
Science
1
770
My research talk for CCA promotion
A summary of what I've been up to for the past few years and where my research program is going.
Dan Foreman-Mackey
February 03, 2022
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Transcript
BUILDING THE SOFTWARE INFRASTRUCTURE FOR ASTROPHYSICS by Dan Foreman-Mackey
who am I? / / what’ve I been up to?
1
7 [1] solving Hard™ data analysis problems [2] enabling and
empowering astrophysicists
implementation.
data = > physics
open source software for astrophysics 2
why?
credit: Adrian Price-Whelan / / data: SAO/NASA ADS
my open source contributions 3
None
gaussian processes 4
p(data|physics)
data ~ N(model; noise)
°0.6 °0.3 0.0 0.3 0.6 raw [ppt] 0 5 10
15 20 25 time [days] °0.30 °0.15 0.00 de-trended [ppt] N = 1000 reference: DFM+ (2017)
°0.6 °0.3 0.0 0.3 0.6 raw [ppt] 0 5 10
15 20 25 time [days] °0.30 °0.15 0.00 de-trended [ppt] N = 1000 reference: DFM+ (2017)
data ~ N(model; noise)
data ~ N(model; noise)
so. why not?
data ~ N(model; noise)
None
reference: Ambikasaran, DFM+ (2015)
None
reference: Ambikasaran, DFM+ (2015)
reference: DFM, Agol, Ambikasaran, Angus (2017); DFM (2018); DFM, Luger,
et al. (2021)
None
reference: Gordon, Agol, DFM (2020)
what’s next?
None
None
None
credit: Quang Tran
reference: Luger, DFM, Hedges (2021)
probabilistic inference 5
p(data|physics)
have: physics = > data
want: data = > physics
integral of the form f(physics) p(physics|data) dphysics
None
number of parameters patience required a few tenish not outrageously
many reference: DFM (priv. comm.)
number of parameters patience required emcee a few tenish not
outrageously many reference: DFM (priv. comm.)
number of parameters patience required emcee a few tenish not
outrageously many how things should be reference: DFM (priv. comm.)
None
None
None
None
gradients!
dp(data|physics) / dphysics
automatic differentiation aka “backpropagation”
your model is just code
apply the chain rule
apply the chain rule over and over again . .
.
sounds silly?
it's not! (mostly)
None
None
what’s next?
None
jax.readthedocs.io
my approach to open source 6
None
[1] don’t underestimate users [2] build libraries, not (just) scripts
[3] teach by example
None
None
None
bringing open source practices to research more generally
None
None
None
None
what’s next? 7
7 [1] inference with stochastic or intractable models [2] what
can we do to better support open source in astrophysics
7
7 credit: Adrian Price-Whelan
many fundamental software packages have a shockingly small number of
maintainers.
a selection of some* CCA-supported software: * my apologies for
neglecting your favorites!
None
BUILDING THE SOFTWARE INFRASTRUCTURE FOR ASTROPHYSICS @ CCA