Felix Petersen

My primary research interest is machine learning with differentiable algorithms. For example, I have made a general framework for making algorithms differentiable, and have also focussed on differentiable sorting and differentiable rendering. While making algorithms differentiable by propagating distributions through them is a lot of fun, I also like propagating distributions through neural networks, which improves uncertainty estimation, robustness, and fairness.

I am a postdoctoral researcher at Stanford University in Stefano Ermon's group and in collaboration with Christian Borgelt, Hilde Kuehne, Mikhail Yurochkin, Yuekai Sun, Oliver Deussen, among others.

I have been working, i.a., at the University of Konstanz, at TAU, DESY, PSI, and CERN.

News

Oct 2022 Our papers "Deep Differentiable Logic Gate Networks" and "Domain Adaptation meets Individual Fairness. And they get along" were accepted to NeurIPS!

Jun 2022 I submitted my thesis on "Learning with Differentiable Algorithms"!

Jun 2022 Our paper "Differentiable Top-k Classification Learning" was accepted to ICML!

Mar 2022 Our paper "GenDR: A Generalized Differentiable Renderer" was accepted to CVPR!

Feb 2022 Our paper "Monotonic Differentiable Sorting Networks" was accepted to ICLR!

Oct 2021 Our papers "Learning with Algorithmic Supervision via Continuous Relaxations" and "Post-processing for Individual Fairness" were accepted to NeurIPS!

Oct 2021 Our paper "Style Agnostic 3D Reconstruction via Adversarial Style Transfer" was accepted to WACV.

May 2021 Our paper "Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision" was accepted to ICML.

Apr 2020 Presenting my research @ Brown University (Apr 20)

Mar 2020 Research stay @ University of Rhode Island

Feb 2020 Presenting my research @ FAIR and Google Research in Paris

Dec 2019 Presenting @ NeurIPS: AlgoNet: C^∞ Smooth Algorithmic Neural Networks for Solving Inverse Problems

Nov 2019 Science Slams in Ulm (Nov 26) and in Reutlingen (Nov 27)

Nov 2019 I am honored to receive the Konrad-Zuse-Jugendpreis

Research

My main research interests are algorithmic supervision, differentiable algorithms, and analytical propagation of distributions through neural networks.

	GenDR: A Generalized Differentiable Renderer Felix Petersen, Christian Borgelt, Bastian Goldluecke, Oliver Deussen in Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR 2022) YouTube Code
	Differentiable Top-k Classification Learning Felix Petersen, Hilde Kuehne, Christian Borgelt, Oliver Deussen in Proceedings of the 39th International Conference on Machine Learning (ICML 2022)
	Monotonic Differentiable Sorting Networks Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen in Proceedings of the International Conference on Learning Representations (ICLR 2022) YouTube Code / diffsort library
	Learning with Algorithmic Supervision via Continuous Relaxations Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen in Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS 2021) YouTube Code / AlgoVision library
	Post-processing for Individual Fairness Felix Petersen, Debarghya Mukherjee, Yuekai Sun, Mikhail Yurochkin in Proceedings of the 35th International Conference on Neural Information Processing Systems (NeurIPS 2021) YouTube Code
	Style Agnostic 3D Reconstruction via Adversarial Style Transfer Felix Petersen, Hilde Kuehne, Bastian Goldluecke, Oliver Deussen in Proceedings of the IEEE Winter Conf. on Applications of Computer Vision (WACV 2022) YouTube
	Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision Felix Petersen, Christian Borgelt, Hilde Kuehne, Oliver Deussen in Proceedings of the 38th International Conference on Machine Learning (ICML 2021) YouTube
	AlgoNet: C^∞ Smooth Algorithmic Neural Networks Felix Petersen, Christian Borgelt, Oliver Deussen
	Pix2Vex: Image-to-Geometry Reconstruction using a Smooth Differentiable Renderer Felix Petersen, Amit H. Bermano, Oliver Deussen, Daniel Cohen-Or
	Towards Formula Translation using Recursive Neural Networks Felix Petersen, Moritz Schubotz, Bela Gipp in Proceedings of the 11th Conference on Intelligent Computer Mathematics (CICM), 2018
	LaTeXEqChecker - A framework for checking mathematical semantics in LaTeX documents Felix Petersen Presented in the Special Session of the 11th Conference on Intelligent Computer Mathematics (CICM), 2018 Slides

Media Coverage

	campus.kn article about my Schülerstudium (studying while in school): English: “At university, knowledge counts more than age” – “Im Gespräch” interviews Felix Petersen, who completed a Schülerstudium German: „An der Uni ist die Expertise wichtiger als das Alter“ – Im Gespräch mit Schülerstudium-Absolvent Felix Petersen
	Südkurier article about me as a 19 years old PhD student: Dieser junge Mann ist 19 Jahre alt – und schreibt gerade seine Doktorarbeit. Über ein hochbegabtes Talent der Konstanzer Uni
	German TV: Short interview about my participation at Jugend forscht: „Jugend forscht“: Hamburger Schüler stellen Forschungsprojekte vor
	Südkurier article about my work at DESY: Streng geheimes Forschungsprojekt: 17-jähriger Informatik-Student entwickelt neuartigen Röntgenlaser

Teaching

	Maths Workshop + Individualized Maths Introduction since WS 2019/2020
	Tutor: Analysis and Linear Algebra SS 2019 + 2020 + 2021
	Seminar: Current Trends in Computer Graphics (+ Neural Networks, and Mathematical Language Processing) WS 2019/2020
	Tutor: Discrete Mathematics SS 2017 and SS 2018
	Tutor: Programming Course 1 (Java) WS 2016/2017 and WS 2017/2018

Based on ✩.