Luiz Chamon
© 2020–2023. All rights reserved.
© 2020–2023. All rights reserved.
My name is Luiz Chamon. I am an ELLIS–SimTech independent research group leader at the University of Stuttgart, Germany. I head the Information Systems Group. We develop tools to enable inteligent systems to extract, process, and act on information.
Oct 5th, 2023 – I am giving an invited talk on "Learning under requirements" at SimTech 2023 in Stuttgart.
Sep 25th, 2023 – I am giving a talk on "Learning under robustness constraints" at EUCCO 2023 in Heidelberg.
Sep 15th, 2023 – I am giving a tutorial on "Robust learning" at the IMPRS-IS BootCamp.
Aug 7th, 2023 – "Transferability Properties of Graph Neural Networks" was accepted for publication on the IEEE TSP (paper).
Jul 11th, 2023 – I am giving an invited talk at the Data Science and Dependence 2023 Conference in Heidelberg.
Jun 21st, 2023 – I am giving a seminar at the SimTech ML sessions, University of Stuttgart.
Jun 20th, 2023 – I am giving a talk at the Kolloquium Technische Kybernetik, University of Stuttgart.
May 16th, 2023 – "Distributed Universal Adaptive Networks" was accepted for publication on the IEEE TSP (paper).
Apr 24th, 2023 – Two papers accepted at ICML 2023:
Apr 22nd, 2023 – I am participating of the Science Cypher which happens in the context of the "SHIFT: KI und eine zukünftige Gemeinschaft" exhibition of the Stuttgart Museum of Art (Kunstmuseum Stuttgart).
Feb 2nd, 2023 – I am giving a (hybrid) talk at EPFL (see here for more details and registration).
Jan 11th, 2023 – I have joined the faculty of the International Max Planck Research School for Intelligent Systems (IMPRS-IS).
Oct 25th, 2022 – I have become a member of ELLIS and its Stuttgart unit.
Oct 5th, 2022 – "Constrained learning with non-convex losses" was accepted for publication on the IEEE TIT (paper).
May 30th, 2022 – I will be moving to the University of Stuttgart in October 2022.
May 15th, 2022 – "Probabilistically Robust Learning: Balancing Average- and Worst-case Performance" was accepted at ICML 2022 (arXiv).
April 27th, 2022 – I gave a talk on Learning under requirements at the ELLIS unit of the University of Stuttgart [youtube].
Feb 4th, 2022 – I was invited to participate of Caltech's 2022 Young Investigators Lecture series. My talk is scheduled for April 7th.
Feb 3rd, 2022 – "Safe policies for reinforcement learning via primal-dual methods" was accepted for publication on the IEEE TAC (paper).
December 10th, 2021 – New preprint: "Transferability properties of graph neural networks" (arXiv).
September 29th, 2021 – "Adversarial Robustness with Semi-Infinite Constrained Learning" was accepted at NeurIPS 2021 (arXiv).
August 24th, 2021 – "Graphon signal processing" was published on the IEEE TSP (paper).
July 1st, 2021 – I am joining UC Berkeley as a postdoc of the Collaboration on the Theoretical Foundations of Deep Learning.
April 1st, 2021 – I will be giving an invited seminar at Microsoft Research.
March 23rd, 2021 – "Approximately supermodular scheduling subject to matroid constraints" was accepted for publication in IEEE TAC (arXiv).
March 10th, 2021 – New preprint: "Constrained learning with non-convex losses" (arXiv).
February 26th, 2021 – New papers accepted at ACC 2021:
February 25th, 2021 – New preprint: "State augmented constrained reinforcement learning: Overcoming the limitations of learning with rewards" (arXiv).
February 1st, 2021 – I will be giving an invited seminar at the Massachusetts Institute of Technology (EECS).
January 18th, 2021 – Check out our paper this week at EUSIPCO 2020: "Graphon filters: Signal processing in very large graphs" (Wednesday 9:10am, EST) [paper]
January 8th, 2021 – I will be giving an invited seminar at the Johns Hopkins Mathematical Institute for Data Science (MINDS).
January 6th, 2021 – I will be giving an invited seminar at the Toyota Technological Institute at Chicago (TTIC).
December 30th, 2020 – "Approximate supermodularity of Kalman filter sensor selection" published on IEEE TAC (paper, IEEEXplore).
December 15th, 2020 – Check out my papers at IEEE CDC this week:
December 5th, 2020 – Check out my papers at NeurIPS next week:
December 3rd, 2020 – I've released the first version of csl, a python package for constrained learning. You can get the package on GitHub and check out applications in fairness and robustness in the documentations.
October 13th, 2020 – "Probably Approximately Correct Constrained Learning" (arXiv) and "Transferability of Graphon Neural Networks" (arXiv) accepted at NeurIPS 2020.
September 5th, 2020 – "Resilient control: Compromising to adapt" (arXiv) and "Risk-constrained linear-quadratic regulators" (arXiv) accepted at IEEE CDC 2020.
May 5th, 2020 – I received the best student paper and best paper awards at ICASSP 2020. Check out papers [1, 2] and video [1].
April 1st, 2020 – New preprint: "Graphon signal processing" submitted to IEEE TSP (arXiv).
March 20th, 2020 – "Functional nonlinear sparse models" was accepted for publication on IEEE TSP (arXiv).
February 7th, 2020 – Patent "Sparse cascaded-integrator-comb filters" with Analog Devices is out.
© 2020–2023. All rights reserved.