About me

I am a Postdoctoral Researcher at the Department of Electrical Engineering and Computer Science (EECS) at the University of California at Berkeley, working with Michael Jordan. I am very grateful to the Swiss National Science Foundation for supporting my research, through the Early.Postdoc.Mobility (in 2021) and the Postdoc.Mobility (in 2023) fellowships.

Prior to my current position, I was Postdoctoral Research Scientist at the Machine Learning and Optimization (MLO) lab at EPFL, working with Martin Jaggi. While at EPFL, I organized the Smooth Games reading group . In part of my time, I participated in the intelligent Global Health (iGH) sub-group of MLO led by Mary-Anne Hartley, by advising on the machine learning aspect of the ongoing projects. I obtained my Ph.D. from EPFL, and Idiap, supervised by François Fleuret. During my Ph.D. studies I did two internships at: (i) Mila where I was supervised by Yoshua Bengio and Simon Lacoste-Julien, as well as at (ii) DeepMind supervised by Irina Higgins.

I am on the job market ('22/'23). Please reach out if you think I will be a good fit for your department. Thanks!

Research interests

My main interests are at the intersection of game theory and machine learning. My research aims to better understand the training dynamics of multi-player games and develop improved methods for their optimization. In addition, my broader interests include unsupervised learning, generative modeling, generalization, and robustness.


I am very passionate about cultivating a diverse and inclusive ML community, and I actively participate in various activities, most prominently as part of the board of directors and Vice President of the Events committee of WiML. Outside work, I enjoy hiking, (acro)yoga, biking, and playing games. I love traveling to places with distinct nature (and taking photos of such landscapes :) ).

Selected Research Projects

* Equal contributions.

Google Scholar


Supervision & Teaching Assistance

  • Tong Yang, On Interior Point Approach for Solving Variational Inequalities
    2022, UC Berkeley.
  • Gilberto Manunza (MSc thesis), On the Connection between Adversarial Training and Uncertainty Estimation
    seven months MSc project, 2021, EPFL.
  • Apostolov Alexander (CS-498 Semester Project), On the Effect of Variance Reduced Gradient and Momentum for Optimizing Deep Neural Networks
    autumn semester, 2020, EPFL.
  • Co-superivison with Sebastian U. Stich:
    • Oğuz Kaan Yüksel, Normalizing Flows for Generalization and Robustness
      (CS-498 Semester Project) autumn semester, 2020, EPFL.
    • Yehao Liu, On the Drawbacks of Popular Deep Learning Uncertainty Estimation Methods
      spring semester & summer internship, 2021, EPFL.
  • Co-superivison with Mary-Anne Hartley:
    • Deeksha M. Shama, Deep Learning Approaches for Covid-19 Diagnosis via Digital Lung Auscultation
      autumn semester, 2020.
    • Pablo Cañas, On Uncertainty Estimation of Global COVID Policy Simulator
      autumn semester, 2020.
  • Teaching Assistant, Deep Learning Course (EE-559)
    for MSc students, EPFL, 2018 & 2020.
  • Teaching Assistant, An Introduction to Deep Learning
    for MSc students, African Master's in Machine Intelligence, Kigali, Rwanda, 2018.





Reading Group




If you'd like to discuss my work or broadly on doing research in CS, DEI activities, etc., please don't hesitate to reach out at: tatjana.chavdarova[at]berkeley[dot]edu