Martin Nocker, MSc

2021 - heute
Projektmitarbeiter - Management Center Innsbruck

2019 - 2021
Software Engineer Automation - D. Swarovski KG, Wattens

Masterand - BMW AG, München

2021 - heute
PhD Student - Universität Rostock

2016 - 2018
Elektrotechnik und Informationstechnik - Technische Universität München (MSc)

2013 - 2016
Informatik - Leopold-Franzens Universität Innsbruck (BSc)

  • Klocker, F., Bernsteiner, R., Ploder, C., & Nocker, M. (2023). A Machine Learning Approach for Automated Cost Estimation of Plastic Injection Molding Parts. Cloud Computing and Data Science, 4(2), 87-111. https://doi.org/10.37256/ccds.4220232277

  • Martin, Nocker, David, Drexel, Michael Rader, Alessio Montuoro, and Pascal Schöttle. "HE-MAN - Homomorphically Encrypted MAchine learning with oNnx models", In The 8th International Conference on Machine Learning Technologies (ICMLT), 2023. https://doi.org/10.1145/3589883.3589889
  • Roland Rauter, Martin Nocker, Florian Merkle, and Pascal Schöttle. "On the Effect of Adversarial Training Against Invariance-based Adversarial Examples", In The 8th International Conference on Machine Learning Technologies (ICMLT), 2023. https://doi.org/10.1145/3589883.3589891
  • Widmann, T., Merkle, F., Nocker, M., & Schöttle, P. (2023). Pruning for Power: Optimizing Energy Efficiency in IoT with Neural Network Pruning. In L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, & E. Pimenidis (Hrsg.), Engineering Applications of Neural Networks (S. 251-263). Springer Nature Switzerland. doi: 10.1007/978-3-031-34204-2_22
  • Mrowca, A., Nocker, M., Steinhorst, S., & Günnemann, S. (2019). Learning temporal specifications from imperfect traces using bayesian inference. In Proceedings of the 56th Annual Design Automation Conference 2019 (pp. 1-6).

2021 - 2023
SMiLE - Secure Machine Learning Applications with Homomorphically Encrypted Data - FFG - Die Österreichische Forschungsförderungsgesellschaft

Koudelka Paul (2023): Explainable Machine Learning Algorithms While Using Homomorphic Encryption