Unraveling Hawking radiation
Santi, Natali Soler Matubaro de, & Santarelli, Raphael. 2019. https://doi.org/10.1590/1806-9126-rbef-2018-0312
CV
Education
- Ph.D in Physics, University of São Paulo, 2019/2024
- M.S. degree in Physics, University Federal of São Carlos, 2016/2018
- B.S. degree in Physics, University of São Paulo, 2012/2015
Research experience
- 2025 - present: Postdoctoral Scholar
- UC Berkeley
- Supervisor: PhD Uros Seljak
- 2025 - present: Researcher Affiliate
- Berkeley Lab
- Supervisor: PhD Uros Seljak
- 2024 - 2024: Visiting Scholar Consultant
- Simons Foundation
- Supervisor: PhD Francisco Villaescusa-Navarro
- 2019 - 2024: Doctor degree in Physics
- University of São Paulo
- Supervisor: PhD Luis Raul Weber Abramo
- 2022 - 2023: Guest researcher/Research Intern Abroad/Predoc at CCA
- Simons Foundation/Flatiron Institute/CMB Analysis and Simulation group
- Supervisor: PhD Francisco Villaescusa-Navarro
- 2016 - 2018: Master degree in Physics
- University Federal of São Carlos
- Supervisor: PhD Raphael Santarelli
- 2017: Participation of event organizing committee
- University Federal of São Carlos
- XIII Semana de Física (XIII SeFís)
- 2014 - 2015: Undergrad Research
- University of São Paulo
- Project title: Introduction to particle physics with emphasis on quantum fluctuations
- Supervisor: Professor Attilio Cucchieri
- 2012: Undergrad Research
- University of São Paulo
- Project title: Synthesis and characterization of BaTi0.9Zr0.1O3 powders
- Supervisor: Professor Antonio Carlos Hernandes
- 2009 - 2011: Highschool Research
- University of São Paulo
- Project title: Study of superconductivity and development of a superconductor material
- Supervisor: Professor Antonio Carlos Hernandes
Honors and Awards
- Honorable Mention – 2025 ICTP-SAIFR PhD Prize in Classical Gravity and Applications – SBF, 2025
- Honorable Mention – José Leite Lopes Prize for Best Doctoral Thesis 2025 – SBF, 2025
- SBF Award for Best PhD Thesis in the Area of Particle and Fields Physics – SBF, 2025
- Outstanding PhD thesis – Postgraduate Award, IF, USP, 2025
- Honorable mention for an outstanding scientific article - Postgraduate Award, IF, USP, 2024
- First best poster award in Master’s degree - XIII Physics Week (XIII SeFís), 2017
- Certificate of merit - Best student of the 3rd year of high school, 2011
- Certificate of merit - Best student of the 8th grade elementary school II, 2008
- Silver medal - X Brazilian Astronomy and Astronautics Olympiad (X OBA), 2007
- Honorable mention - II Brazilian Olympiad of Mathematics of Public schools (II OBMEP), 2006
- Silver medal - VIII Brazilian Astronomy and Astronautics Olympiad (VIII OBA), 2005
Publications
Mass Evolution of Schwarzschild Black Holes
de Santi, N.S.M., Santarelli, R., 2019. https://doi.org/10.1007/s13538-019-00708-y
A machine learning suite to halo-galaxy connection
de Santi, N. S. M. and et al., 2023
Mimicking the halo-galaxy connection using machine learning
Natalí S. M. de Santi, et al. 2022, https://doi.org/10.1093/mnras/stac1469
Improving cosmological covariance matrices with machine learning
de Santi, N. S. M. and Abramo, L. R. 2022, DOI:10.1088/1475-7516/2022/09/013
Primeiros passos na obtenção de parâmetros cosmológicos utilizando matrizes de covariância cosmológicas sem ruído
de Santi, N. S. M. and Abramo, L. R. 2022, DOI: 10.5151/astrocientistas2021-11
Robust field-level likelihood-free inference with galaxies
Natalí S. M. de Santi et al 2023 ApJ 952 69
High-fidelity reproduction of central galaxy joint distributions with Neural Networks
Natália V N Rodrigues, Natalí S M de Santi, et al., 2023, https://doi.org/10.1093/mnras/stad1186
A Hierarchy of Normalizing Flows for Modelling the Galaxy-Halo Relationship
Lovell, C. C., Hassan, S., Anglés-Alcázar, D., et al. 2023, arXiv:2307.06967. doi:10.48550/arXiv.2307.06967
Obtaining cosmological covariance matrices with machine learning
de Santi, N. S. M. and Abramo, L. R. 2023
A universal equation to predict Ωm from halo and galaxy catalogues
Shao, H., de Santi, N. S. M., Villaescusa-Navarro, F., et al. 2023, ApJ, 956, 2, 149. doi:10.3847/1538-4357/acee6f
The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites
Ni, Y., Genel, S., Anglés-Alcázar, D., et al. 2023, ApJ, 959, 2, 136. doi:10.3847/1538-4357/ad022a
Field-level simulation-based inference with galaxy catalogs: the impact of systematic effects
de Santi, N. S. M., Villaescusa-Navarro, F., Raul Abramo, L., et al. 2025, JCAP. doi:10.1088/1475-7516/2025/01/082
Exploring the halo-galaxy connection with probabilistic approaches
Rodrigues, N. V. N., de Santi, N. S. M., Abramo, R., et al. 2025, AAP. doi:10.1051/0004-6361/202453284
Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models
Natalí S. M. de Santi et al 2025 arXiv
Talks
Development of a magnetic levitation system
Poster presentation at XIX Congresso de Iniciação Científica da UFSCar (XIX CIC), Federal University of São Carlos, São Carlos, SP, Brazil
Synthesis and characterization of BaTi0.9Zr0.1O3 powders
Poster presentation at 20 Simpósio Internacional de Iniciação Científica (XX SIICUSP), University of São Paulo, São Paulo, SP, Brazil
Numerical studies on the classical and quantum harmonic oscillator
Poster presentation at IV Semana Integrada do Instituto de Física de São Carlos (SIFSC 4), University of São Paulo, São Carlos, SP, Brazil
Regularization techniques in the Casimir effect
Poster presentation at V Semana Integrada do Instituto de Física de São Carlos (SIFSC 5), University of São Paulo, São Carlos, SP, Brazil
Observer dependence in the particle concept and the Unruh Effect
Poster presentation at XIII Semana de Física (XIII SeFís), Federal University of São Carlos, São Carlos, SP, Brazil
Improving covariance matrices using machine learning
Oral poster presentation at Latin American Workshop on Observational Cosmology, International Centre for Theoretical Physics (ICTP) & South American Institute for Fundamental Research (SAIFR), São Paulo, SP, Brazil
Improving Covariance Matrices using Machine Learning
Meeting - Poster presentation at Latin American Workshop on Observational Cosmology, Virtual meeting
First steps in the obtation of the cosmological parameters using denoised cosmological covariance matrices
Oral communication at Astrocientistas I: Encontro Brasileiro de Meninas e Mulheres da Astrofísica, Gravitação e Cosmologia, Virtual meeting
Using machine learning to mimic the halo-galaxy connection
Talk at Group Meeting: Prof. Francesco Shankar, University of Southampton, Southampton, UK
A machine learning suite to mimic the halo-galaxy connection
International Conference - Poster presentation at International Conference on Machine Learning for Astrophysics - ML4Astro, Virtual participation - Catania, Italy
Improving cosmological covariance matrices using machine learning
Talk at Journal Club: Astronomical Observatory of Trieste, I.N.A.F., Trieste, Italy
Improving cosmological covariance matrices with machine learning
Talk at AI in Astronomy, IAG, USP, São Paulo, SP, Brazil
Obtaining cosmological covariance matrices with machine learning
Oral presentation at XLV Reunião Anual da Sociedade Astronômica Brasileira, Virtual participation
Graph neural networks for robust parameter inference in cosmology: the first steps before real data
Oral presentation at XLVI Reunião Anual da Sociedade Astronômica Brasileira, Planetário Rio, Rio de Janeiro, RJ, Brazil
Inferring cosmological parameters using galaxies
Oral presentation at CAMELS Workshop, Simons Foundation, New York, NY, US
Halo-galaxy connection and machine learning: an aerial view
Oral presentation at II mini-Workshop on the halo-galaxy connection for the South American community, Universidad Técnica Federico Santa María, Santiago, Chile
Obtaining cosmological information through machine learning
Talk at Gothamfest 2023, Simons Foundation, New York, NY, US
Constraining Ωm using galaxies
Talk at Galaxy Formation Group Meeting, Flatiron Institute, New York, NY, US
Can we use galaxies to constrain 𝛀m?
Talk at Journal club: Berkeley Cosmology Group, UC Berkeley, Berkeley, CA, US
Constraining Ωm with phase-space information of galaxies
Oral presentation at KITP Conference: Galaxy Formation and Evolution in the Data Science Era, UC Santa Barbara, Santa Barbara, CA, US
Using graph neural networks to constrain Ωm - A robust model
Talk at Cosmology Lunch Talks, Institute of Advanced Studies (IAS), Princeton, NJ, US
Simulation-based inference: constraining Ωm with galaxies phase-space information
Talkn at Tri-state Cosmology X Data Science Meeting, Flatiron Institute, New York, NY, US
Doing Cosmology with galaxies
Talk at Pizza Lunch Talks, Columbia University, New York, NY, US
Implicit likelihood inference: a robust model to constrain 𝛀m
Talk at Group Seminars - Prof. Bhuvnesh Jain, UPenn, Philadelphia, PA, US
A robust model to constrain 𝛀m with implicit likelihood free-inference
Talk at Open Group Seminars - Prof. Cora Dvorkin, Harvard University, Cambridge, MA, US
Implicit likelihood inference using graph neural networks: a robust model to constrain 𝛀m
Talk at Data-Science X Astro seminar, Yale University
Field-level likelihood-free inference with galaxies: a robust model
Talk at Cosmology Seminar KIPAC, Stanford University, Santa Clara County, CA, US
Unraveling Cosmology from field-level likelihood-free inference - A robust model
Talk at Renoir Science Seminar, Recherche Energie NOIRe (RENOIR), Marseille, France
Cosmology from galaxy phase-space information and a bit of halo-galaxy connection
Talk at Grav Seminars, Gravitational Geometry and Dynamics research group, Aveiro, Portugal - Online
Field-level simulation-based inference: using GNNs to get a robust model
Talk at CITA cosmology discussion, University of Toronto, Toronto, Canada
Cosmology from field-level likelihood-free inference: The first steps towards real data
Oral presentation at Pre-doc symposium , Flatiron Institute, New York, NY, US
How to use Machine Learning (to try) to understand the Universe
Oral presentation at III mini-Workshop on the halo-galaxy connection for the South American community, Universidad de Atacama, Copiapó, Chile
Before real data: pressing graph neural networks to do field-level simulation-based inference with galaxies
Oral communication at ML-IAF/CCA-2023: Debating the potential of machine learning in astronomical surveys, CCA, New York, NY, US & IAP, Paris, France
The Influence of Systematics on Galaxy Catalogs - looking for a robust model to do field-level likelihood-free inference
Talk at Seminários do CBPF/COTEC, CBPF, Rio de Janeiro, Brazil
A proper probabilistic approach for halo-galaxy connection and likelihood-free parameter inference
Oral presentation at AI driven discovery in Physics and Astrophysics - CD3, UTokyo, UTokyo, Japan
Measuring the impact of systematics on the predictions of 𝛀m on simulated galaxy catalogs using graph neural networks
Talk at Yale Astronomy Data Science Seminar, Yale University, New Haven, CT, US
Measuring the impact of systematics on the predictions of 𝛀m on simulated galaxy catalogs using graph neural networks
Talk at ML Session of DoA, Tsinghua University, Beijing, China
Graph neural networks for Cosmology
Talk at First Learning on Graphs (LOG) conference in South America, ICMC
Cosmology with graph neural networks
Talk at SP Research Group meetings in Astro & Cosmo, Principia Institute
The use of machine learning and artificial intelligence in astrophysics, cosmology, and gravitation researches
Talk at As astrocientistas: III Encontro Brasileiro de Meninas e Mulheres da Astrofísica, Gravitação e Cosmologia, Astrocientistas
How to use graph neural networks to understand the Universe?
Talk at AstroGainz group meeting, Chile
Learning Cosmology with Graph Neural Networks
Talk at BCCP Seminars, UC Berkeley, Berkeley, US
The Universe from the perspective of machine learning and data science
Talk at Public talks, UPEI, Cairo, Egypt
Teaching and other activities
Skills
- Computer Languages
- Bash
- C
- Cython
- Fortran
- LateX
- Python
- Slurm
- Machine Learning
- Anomaly Detection (AD)
- Computer Vision (CV)
- Convolutional Neural Networks (CNN)
- Decision Trees (DT)
- Generative Artificial Intelligence (GenAI)
- Graph Neural Networks (GNN)
- Logistic Regression (LR)
- Moment Neural Networks (MNN)
- Natural Language Processing (NLP)
- Neural Networks (NN)
- Normalizing Flows (NF)
- Random Forest (RF)
- Recommendation Systems (RS)
- Regression Data Augmentation (SMOGN)
- Symbolic Regression (SR)
- Support Vector Machines (SVM)
- Stacked Models (SM)
- Tools
- Emacs
- Git (github)
- Jupyter Notebooks
- Linux
- Mathematica
- Docker
- APIs
- AWS
- Languages
- Native speaker in Portuguese
- Proeficient in English
- Beginner in Spanish
Refereeing
- New Astronomy (New Astron.) - 2024
- Monthly Notices of the Royal Astronomical Society (MNRAS) - 2022/2023/2025
- The Astrophysical Journal (ApJ) - 2023
- Journal of Cosmology and Astroparticle Physics (JCAP) - 2025
Orientations
- Veer Mehta. Undergrad research. UC Berkeley. Inference of halo properties from galaxy simulation data using machine learning.
- Tirthankar De. Undergrad research. Indian Institute of Technology Roorkee. Statistical and machine learning approaches for cosmological parameter inference.
Member of thesis and dissertation examination committees
- Jara, C. B. R.; MONTERO-DORTA, ANTONIO D; Amigo, P.; de Santi, N. S. M.; RODRIGUES, NATÁLIA V N. Member of undergraduate thesis of Catalina Belén Riveros Jara. The halo-galaxy connection through the lens of a machine: predicting galaxy clustering. 2025. Undergraduate thesis in Astrophysics - Universidad Tecnica Federico Santa Maria - Chile.