Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
My research talk for CCA promotion
Search
Dan Foreman-Mackey
February 03, 2022
Science
1
780
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
Tweet
Share
More Decks by Dan Foreman-Mackey
See All by Dan Foreman-Mackey
Open software for Astronomical Data Analysis
dfm
0
150
Open Software for Astrophysics, AAS241
dfm
2
540
Astronomical software
dfm
1
730
emcee-odi
dfm
1
670
Exoplanet population inference: a tutorial
dfm
3
460
Data-driven discovery in the astronomical time domain
dfm
6
720
TensorFlow for astronomers
dfm
6
810
How to find a transiting exoplanets
dfm
1
470
Long-period transiting exoplanets
dfm
1
320
Other Decks in Science
See All in Science
データベース11: 正規化(1/2) - 望ましくない関係スキーマ
trycycle
PRO
0
930
テンソル分解による糖尿病の組織特異的遺伝子発現の統合解析を用いた関連疾患の予測
tagtag
2
220
A Guide to Academic Writing Using Generative AI - A Workshop
ks91
PRO
0
130
LayerXにおける業務の完全自動運転化に向けたAI技術活用事例 / layerx-ai-jsai2025
shimacos
2
1.5k
データベース15: ビッグデータ時代のデータベース
trycycle
PRO
0
330
Cross-Media Information Spaces and Architectures (CISA)
signer
PRO
3
31k
システム数理と応用分野の未来を切り拓くロードマップ・エンターテインメント(スポーツ)への応用 / Applied mathematics for sports entertainment
konakalab
1
380
データマイニング - グラフ構造の諸指標
trycycle
PRO
0
160
NASの容量不足のお悩み解決!災害対策も兼ねた「Wasabi Cloud NAS」はここがスゴイ
climbteam
0
110
研究って何だっけ / What is Research?
ks91
PRO
1
110
Celebrate UTIG: Staff and Student Awards 2025
utig
0
130
実力評価性能を考慮した弓道高校生全国大会の大会制度設計の提案 / (konakalab presentation at MSS 2025.03)
konakalab
2
190
Featured
See All Featured
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
34
6k
A Modern Web Designer's Workflow
chriscoyier
695
190k
Navigating Team Friction
lara
189
15k
[RailsConf 2023] Rails as a piece of cake
palkan
56
5.8k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
36
2.5k
Practical Orchestrator
shlominoach
190
11k
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.4k
Code Reviewing Like a Champion
maltzj
525
40k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
231
53k
Building a Scalable Design System with Sketch
lauravandoore
462
33k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
656
61k
Six Lessons from altMBA
skipperchong
28
4k
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