Simon Mak | Integrating Science into Stats Models
#statistics #science #ai
It’s a common dictum that statisticians need to incorporate domain knowledge into their modeling and the interpretation of their results. But how deeply can scientific principles be embedded into statistical models? Prof. Simon Mak (Duke University) is pushing this idea to the limit by integrating fundamental physics, physiology, and biology into both the models and model inference. This includes Simon’s joint work with Profs. David Dunson and Ruda Zhang (also of Duke University).
Scientific reasoning AND stats. What more could we ask for?
Enjoy!
Watch it on....
YouTube: https://youtu.be/bUbZO7R4z40
Podbean: https://dataandsciencepodcast.podbean.com/e/simon-mak-integrating-science-into-stats-models/
Topics
00:00 - COMING UP….Scientists & Statisticians
02:09 - Introduction - Integrating scientific knowledge into AI/ML
06:08 - How much domain knowledge is sufficient?
09:15 - Choosing which prior knowledge to integrate into a model
14:49 - Black box & gray box optimization
19:50 - Non-physics examples of integrating scientific theory into ML models
22:45 - Scientific principles & modeling at different scales
27:20 - Correlation is one just way of modeling linkage
36:37 - Conditional independence & different-fidelity experiments
39:40 - Innovation vs incorporation of known information in the model
42:52 - Aortic stenosis example
52:49 - Which mathematics can be used to represent scientific knowledge
57:09 - How to acquire scientific domain knowledge
1:02:45 - Complementary approaches to integrating science
1:06:48 - Gaussian process & integrating priors over functions
1:12:48 - A topic for statisticians and scientists to debate:science-based vs data-based learning.
Simon Mak's Webpage: https://sites.google.com/view/simonmak/home