Hi! My name is 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.

I earned my Ph.D. from EPFL, and Idiap, advised by François Fleuret. During my doctoral studies, I completed internships at (i) Mila where I was supervised by Yoshua Bengio and Simon Lacoste-Julien, as well as at (ii) DeepMind mentored by Irina Higgins. Following my Ph.D., I worked as a Postdoctoral Research Scientist at the Machine Learning and Optimization (MLO) lab at EPFL, advised by Martin Jaggi. Later, I joined the Department of Electrical Engineering and Computer Science (EECS) at UC Berkeley as a Postdoctoral Researcher, working with Michael Jordan.

🏅 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.

Research Interests

My research explores the intersection of game theory and machine learning, often referred to as AI. I focus on understanding how AI models, such as neural networks, learn and interact in multi-player game settings, working to develop improved learning methods. I am also engaged in areas like reinforcement learning, generative modeling, generalization, and robustness. Additionally, I am interested in creating new algorithmic approaches to expand AI's scope in modeling complex multi-player structures, with applications in fields such as microeconomics and mechanism design.

News:

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

Selected Research

* Equal contributions.

Google Scholar

Selected Talks

Teaching

  • 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

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]berkeley[dot]edu

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