Hi! My name is Tatjana (pronounced /tatiana/) and 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.

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 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.


Jan, 2024: You have two- or multi-player problem with constraints? use our ACVI method -> accepted at ICLR 2024. See you in Vienna!
October 23, 2023: I'll join CISPA as a tenure-track faculty, and will give a course on Games in Machine Learning (winter semester) at Saarland University in Germany -- see the course materials! If you are interested in joining my group use this form to apply!
August, 2023: I'll be at the EUROPT workshop in Budapest; let me know if you'll be there!
mid June, 2023: I will be at Jordan Symposium in France, let me know if you're there!
May 31 - June 3, 2023: I will be at SIAM OP23 in Seattle, and talk about solving VIs with constraints: slides
May 1 - May 5, 2023: I will attend ICLR in person and talk about how to solve games with constraints

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



  • Games in Machine Learning (GML), winter 2023/24 @ Saarland University, advanced lecture.
    The course covers:
    • Game theory & algorithmic game theory basics
    • Variational Inequalities (VIs) — extend classical convex optimization, enabling the formalization of a wide range of problems related to finding equilibria, such as robustified objectives, min-max and actor-critic problems, and multi-agent reinforcement learning, among others.
    • Gradient-based optimization methods to solve VIs and their convergence.
    • Some applications of VIs in machine learning, such as Generative Adversarial Networks.
  • See Course Materials

Group: current members

  • Khaled Alomar, On solving Variational Inequalities with Gradient-based methods: Convergence Analysis
    internship since Nov. 2023, Math Dept. Saarland University.
  • Baraah Adil Mohammed Sidahmed, On Solving Multi-Agent Reinforcement Learning with Optimization Methods for Equilibria
    MSc thesis & intern since Dec. 2023, Data Science and AI Dept., Saarland University.
  • Prashanth Pombala, On improving Variational Inequalities optimization methods using insights from continuous time
    internship since Jan. 2024, Mathematics and Computer Science Dept., Saarland University.
  • Sneha Chetani, On Robust Machine Learning
    internship since Feb. 2024, Data Science and AI Dept., Saarland University.
  • Aniket Sanyal, On improving Variational Inequalities optimization methods using insights from Signal Processing
    internship since Feb. 2024, Computer Science Dept., Saarland University.

Supervision & Teaching Assistance (up to 2023)

  • 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.
  • Oğuz Kaan Yüksel, Normalizing Flows for Generalization and Robustness
    (CS-498 Semester Project) autumn semester, 2020, EPFL. Co-superivison with Sebastian U. Stich
  • Yehao Liu, On the Drawbacks of Popular Deep Learning Uncertainty Estimation Methods
    spring semester & summer internship, 2021, EPFL. Co-superivison with Sebastian U. Stich
  • 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.


Guest Editor


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