We introduce two natural notions of fairness (subgroup and instantaneous) which could establish the study of fairness in forecasting and learning of linear dynamical systems.
Jakub received his bachelor and masters degrees from Masaryk University, in Brno, the Czech Republic, and his Ph.D. degree from the University of Nottingham, Nottingham, U.K., in 2006, 2009, and 2012, respectively. He has worked in two start-ups, at ARM Ltd., at the University of Edinburgh, at the University of Toronto, at IBM Research -- Ireland, and at the University of California, Los Angeles. He is currently a faculty member at the Czech Technical University in Prague, the Czech Republic. He designs and analyses algorithms for optimisation and control problems across a range of application domains, including power systems, quantum computing, and robust statistics.
Supervisor to the following students
Mentor to the following post-docs
Close collaborators at CTU
Previously, I have worked with a number of excellent post-doctoral fellows:
- Libor Caha, whose PhD was from the Slovak Academy of Sciences (2021). Subsequent position: Post-doc at Technical University of Munich.
- Philipp Haehnel, whose PhD was from the Trinity College Dublin (2018). Subsequent position: Post-doc at Harvard University.
Previously, I have also worked with a number of PhD students while at IBM Research.
- Olivier Massicot at the University of Illinois Urbana-Champaign (2019-2020). Continues his PhD studies at UIUC.
- Anna de Rosier at the University of Gdansk (2019–2020): Subsampling in Testing Non-Locality. Subsequent position: Post-doc at Maynooth University.
- Cunlu Zhou at the University of Notre Dame (2018): Alternative Methods in Semidefinite Programming; Subsequent position: Post-doc at the University of Toronto
- Jie Liu at the Lehigh University (2017–2018): Hybrid Methods for Polynomial Optimisation; Subsequent position: Research Scientist at Innopeak Technology
- Jing Xu at the University of Pennsylvania (2017): Robust Parameter Estimation in Gaussian Mixture Models; Subsequent position: ML Engineer at Facebook (FAIR)
- Alan Liddel at the University of Notre Dame (2016): Hybrid Methods for Polynomial Optimisation; Subsequent position: Software Engineering Manager at Path Robotics
- Martin Takac at the University of Edinburgh (2014): First-Order Methods for Semidefinite Programming; Subsequent position: Tenured faculty associate professor at Lehigh University
- Wann-Jiun Ma at the University of Notre Dame (2014): First-Order Methods for Semidefinite Programming; Subsequent position: Post-doc at Duke University
- Tim McCoy at the University of Notre Dame (2013): Homotopy Methods in Power Systems; Subsequent position: Software engineer at Google
The manner in which agents contribute to a social-computing platform (e.g., social sensing, social media) is often governed by distributed algorithms. We explore the guarantees available in situations, where fairness among the agents contributing to the platform is needed.
Many optimization problems in binary decision variables are hard to solve. In this work, we demonstrate how to leverage decades of research in classical optimization algorithms to warm-start quantum optimization algorithms. This allows the quantum algorithm to inherit the performance guarantees from the classical algorithm used in the warm-start.
Our Ph.D. student and a two-time poster session winner Maria Rigaki shares her secret tips.
We are coordinating a major Horizon Europe project in fair artificial intelligence. Ethical aspects of AI systems lie at the center of the project which aims to develop explainable and transparent AI algorithms.
Apple AirTags and other location-tracking devices are a useful tool when it comes to locating misplaced items or even missing people. But in the hands of stalkers or criminals, these can be weaponized. How can we maintain privacy standards and guarantee fairness? Our AI algorithms offer solutions.