Hello! I’m Tatjana (pronounced /tatiana/), currently a Visiting Professor in the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano (Polimi), where I collaborate with Nicola Gatti and Nicolò Cesa-Bianchi.

I hold a Ph.D. from EPFL, and Idiap, advised by François Fleuret. During my doctoral studies, I interned at (i) Mila with Yoshua Bengio and Simon Lacoste-Julien, and at (ii) DeepMind under Irina Jurenka (Higgins). After my Ph.D., I was a Postdoctoral Researcher at EPFL’s MLO Lab with Martin Jaggi, and later at UC Berkeley EECS with Michael Jordan.

» The Games in ML '25 course materials are available here.

Research Interests

I work at the intersection of game theory and machine learning, developing AI systems that learn, strategize, and interact in multi-agent environments. My research spans:

» Outside of research, I’m passionate about the broader implications of games—not just for AI, but for developing human inteligence and society. (Fun fact: I recommend this talk by Kelly Clancy on how games shape our world!)

News:

May, 2025: I will be teaching a "Games in Machine Learning" PhD course at Polimi, starting from late May. Check out Polimi courses website. Course materials are available here.
Sept., 2024: Very excited to be working with Nicola Gatti, Alberto Marchesi, Matteo Castiglioni, and the remaining game theory and reinforcement learning team at Polimi and Milano!
July, 2024: I will be at the ISMP conference and talk about HRDEs for VIs (slides below). See you in Montrèal! PS - if you're attending ICML, don't miss out WiML's events!
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

🏅 Thanks to the Swiss National Science Foundation for supporting my research, through the Early.Postdoc.Mobility (in 2021) and the Postdoc.Mobility (in 2023) fellowships.

Selected Research

* Equal contributions.

Google Scholar

Selected Talks

Teaching

  • Games in Machine Learning (GML), second semester 2024/25 @ Politecnico di Milano, PhD course.
    The course covers:
    • Game theory & optimization basics
    • Game optimization — formalized generaliy through Variational Inequalities.
    • Numerical methods for finding equilibria and their convergence.
    • Example applications of VIs in machine learning.
  • See Course Materials
  • (earlier version) Games in Machine Learning (GML), winter 2023/24 @ Saarland University, advanced lecture.

  • See Course Materials

Games in ML Group

  • 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.
  • 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.
  • Prashanth Pombala, On improving Variational Inequalities optimization methods using insights from continuous time
    internship Jan.-Apr. 2024, Mathematics and Computer Science Dept., Saarland University.
  • * Photo, June '24.

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.

TAing

  • 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.

Activities

Guest Editor

Contact

Email: tatjana.chavdarova[at]polimi[dot]it

Office 20, First floor, Building 21
Via Golgi 39, 20133, Milan
Dipartimento di Elettronica, Informazione e Bioingegneria
Politecnico di Milano