If you made any changes in Pure these will be visible here soon.

Personal profile

Academic background

Robert Peharz received his Master degree in Computer Engineering from Graz University of Technology (TU Graz), Austria (2010). From 2010-2015, he pursued his PhD studies at TU Graz, working on probabilistic graphcial models and sum-product networks, with applications to signal processing. From 2015-2017, he was postdoc at the Medical University of Graz, working on interdisciplinary approaches for early recognition of neural maldevelopment via behavioral neuroscience. He was postdoc in the Machine Learning Group (MLG) at the University of Cambridge from 2017-2018. He was Marie-Curie Individual Fellow at MLG Cambridge from 2018-2019. Robert joined Eindhoven University of Technology (TU/e) in November 2019 as an Assistant Professor in the Artificial Intelligence cluster.

Quote

Uncertainty Matters

Research profile

Robert Peharz is an Assistant Professor in the Artificial Intelligence cluster at Eindhoven University of Technology. Robert's work leverages probability as a principled language to represent and process uncertain knowledge. His main research activity is dedicated to develop powerful and expressive machine learning algorithms which are based on probabilistic principles. His particular research targets are probabilistic graphical models, tractable probabilistic models such as probabilistic circuits, and probabilistic deep learning. In his work, Robert aims to unite principled probabilistic modeling with the power of the entire machine learning toolbox.

Fingerprint Dive into the research topics where Robert Peharz is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output

Deep Structured Mixtures of Gaussian Processes

Trapp, M., Peharz, R., Pernkopf, F. & Rasmussen, C. E., 26 Apr 2020, (Accepted/In press) International Conference on Artificial Intelligence and Statistics (AISTATS). Proceedings of Machine Learning Research

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Open Access
  • Automatic Bayesian Density Analysis

    Vergari, A., Molina, A., Peharz, R., Ghahramani, Z., Kersting, K. & Valera, I., 2019, Proceedings of The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19). AAAI Press, p. 5207-5215 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    Open Access

    Bayesian Learning of Sum-Product Networks

    Trapp, M., Peharz, R., Ge, H., Pernkopf, F. & Ghahramani, Z., 26 May 2019, Advances in Neural Information Processing Systems (NeurIPS). 32 ed. Curran Associates, p. 6347-6358

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • Encoding and decoding representations with sum- And max-product networks

    Vergari, A., Peharz, R., Di Mauro, N. & Esposito, F., 1 Jan 2019.

    Research output: Contribution to conferencePaperAcademic

  • Faster attend-infer-repeat with tractable probabilistic models

    Stelzner, K., Peharz, R. & Kersting, K., 1 Jan 2019, 36th International Conference on Machine Learning, ICML 2019. Proceedings of Machine Learning Research, p. 10455-10466 12 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

  • 1 Citation (Scopus)