Photo by Ola Sierant
Castle Square, Warsaw

Dr Anna Dawid

Quantum physics & machine learning scientist,
theatre and games enthusiast.

Hi! I’m a research fellow at the Center of Computational Quantum Physics of the Flatiron Institute in New York, happily playing with interpretable machine learning for science and ultracold molecules for quantum simulations.
I defended my joint Ph.D. degree in physics and photonics in September 2022 under the supervision of Prof.  Michał Tomza (Faculty of Physics, University of Warsaw, Poland) and Prof.  Maciej Lewenstein (ICFO – The Institute of Photonic Sciences, Spain).  Before, I did my MSc in quantum chemistry and BSc in Biotechnology at the University of Warsaw.

  • github
  • twitter
  • scholar
  • orcid-id

Research directions

Understanding machine learning

Why overparametrized models generalize so well? Is generalization related to the flatness of the training loss minimum? What is the reason for double descent? What data features are learned by machines? 

Machine learning for sciences

How to boost quantum experiments with deep learning? How to automatically detect local and global order parameters of quantum phase transitions? Can we learn new physics from trained neural networks?

Ultracold molecules

What happens when we have both magnetic and electric excitations which are additionally coupled? Can we go beyond diatomic molecules with diagonal Franck-Condon factors with laser cooling?

Quantum simulations

What novel phases of matter can we design? How to understand high-temperature superconductivity with quantum simulators?

Featured publications

A.  Dawid et al. Modern applications of machine learning in quantum sciences. arXiv:2204.04198 (2022).

See the summary on Twitter!


A.  Dawid,  P.  Huembeli,  M.  Tomza,  M.  Lewenstein  &   A.  Dauphin. 
Hessian-based toolbox for reliable and interpretable machine learning in physics
Mach. Learn.: Sci. Technol. 3, 015002 (2022).
See the summary on Twitter!

Käming*,  A.  Dawid*,  K.  Kottmann*,  M.  Lewenstein,  K.  Sengstock,  A.  Dauphin  &  C.  Weitenberg. 
Unsupervised machine learning of topological phase transitions from experimental data
Mach. Learn.: Sci. Technol. 2, 035037 (2021).


Oct 3, 2022

A new adventure as a Flatiron Research Fellow unveils!

Sept 20, 2022

I defended with honours my PhD thesis titled “Quantum many-body physics with ultracold atoms and molecules: exact dynamics and machine learning”. It was a crazy ride, and I loved every minute of it (weeell, a large majority!

May 11, 2022

I am extremely happy and honoured to announce that I was awarded the START fellowship of the Foundation for Polish Science for the best scientists under 30!