The secret life of an IAIFI Fellow
florpi
Building a space for the intersection of Physics and AI
https://florpi.github.io/
IAIFI Fellow
Carol Cuesta-Lazaro
Undergrad in Physics
Madrid (Spain)
Heidelberg (Germany)
2017: first ML project as an assistant researcher
First time can combine all my interests!
PhD in Data Intensive Science
Durham (UK)
Working on cosmology (theory)
+ Industry placements
IAIFI Committees
How I thought it would look like
How it looks like
Industry Partnership
Helped organise industry lunches
Early Career and Equity
Moderated panel on Industry x Academia collaborations (IAIFI workshop 2024)
Yearly gathering feedback from ECRs
Pushed for fellows involvement in felowship hiring
(Marisa and Thomas thinking about all the work this will really take that we are totally overlooking)
IAIFI Activities
Outreach
AstroxML Hackathon
Teaching
AstroML Hackathon 2024
LatamSummerSchool 2023
HerWILL 2024
IAIFISummerSchool2024
CambridgeScienceFestival2022
Ethics&ML2023
My research interests
AI4Science
Equivariance & Symmetries
Anomaly detection
Out-of-Distribution
Interpretability
Quantifying Uncertainties
Partial Observations
PDEs
Multimodal
Simulation-based-Inference
Foundation Models
Hierarchical
IAIFI Coffee
Hanging out at the penthouse
IAIFI Seminars
Hackathons
["A point cloud approach to generative modeling for galaxy surveys at the field level"
Cuesta-Lazaro and Mishra-Sharma
arXiv:2311.17141]
Base Distribution
Target Distribution
- Sample
- Evaluate
Siddharth Mishra-Sharma
IAIFI Fellow
Modelling Galaxy Point Clouds
ICML AstroML Spotlight 2023
Fixed Initial Conditions / Varying Cosmology
p(\theta|x) = \frac{p(x|\theta)p(\theta)}{p(x)}
Diffusion model
CNN
Diffusion
Increasing Noise
p(\sigma_8|\delta_m)
p(\sigma_8|\delta_m + 0.01 \epsilon)
p(\sigma_8|\delta_m + 0.02 \epsilon)
["Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo"
Mudur, Cuesta-Lazaro and Finkbeiner]
Nayantara Mudur
CNN
Diffusion
Douglas Finkbeiner
IAIFI JI
IAIFI SI
NeurIPs ML for the Physical Sciences 2023
All learnable functions
All learnable functions constrained by your data
All Equivariant functions
More data efficient!
Incorporating symmetries in ML architectures
Non-equivariant
Julia Balla
Siddharth Mishra-Sharma
> x2 in constraining power
From Angstroms to Gigaparsecs
Tess Smidt
IAIFI JI
IAIFI Fellow
IAIFI SI
p_\phi(\rho_\mathrm{DM}|\rho_\mathrm{Galaxies})
1 to Many:
["Debiasing with Diffusion: Probabilistic reconstruction of Dark Matter fields from galaxies"
Ono et al arXiv:2403.10648]
Victoria Ono
Core Park
Truth DM
Inferred DM
Observed
Nayantara Mudur
Probabilistic dark matter mapping
NeurIPs Physical Sciences 2023
IAIFI JI
Harvard students
["3D Reconstruction of Dark Matter Fields with Diffusion Models: Towards Application to Galaxy Surveys" Park, Mudur, Cuesta-Lazaro et al (in-prep)]
Posterior Sample
Posterior Mean
Debiasing Cosmic Flows
ICML AI4Science 2024
Rewinding the Universe
Stochastic Interpolants
NF
p(\delta_\mathrm{ICs}, \theta|\delta_\mathrm{Final}) =
p(\delta_\mathrm{ICs}|\delta_\mathrm{Final})
p(\theta|\delta_\mathrm{ICs},\delta_\mathrm{Final})
Michael Albergo
IAIFI Fellow
Daniel Eisenstein
IAIFI SI
Simulating what you need
(and sometimes what you want)
Compressing cosmological simulations
Brandon Y. Feng
(in Bill Freeman's group)
IAIFI SI
Ge Yang talking about NERFs at IAIFI colloquium
~ 10 trillion particles per snapshot stored
x Discrete snapshots
Can we learn compressed continuous representations with Neural Fields?
IAIFI Coffee
Learning priors from simulations
Mikhail Ivanov
IAIFI Affiliate
Why IAIFI works
Bring postdocs excited about AI+Physics
+ get them great junior collaborators
+ gpus
=
New ideas, best practices and state of the art ML
+ resources
New community at the intersection
summer schools, hackathons, workshops
deck
By carol cuesta
