Martin Nocker, MSc

Project Assistant martin.nocker@mci.edu

Professional work experience

02/2021 - present
Project Assistant - Management Center Innsbruck
02/2019 - 01/2021
Software Engineer Automation - D. Swarovski KG, Wattens
05/2018 - 12/2018
Master's Student - BMW AG

Education

10/2021 - present
University of Rostock
PhD Student
10/2016 - 12/2018
MSc - Technical University of Munich
Electrical and Computer Engineering
10/2013 - 03/2016
BSc - Leopold-Franzens University Innsbruck
Computer Science

Teaching (faculty internships etc.)

09/2024 - present
MCI - The Entrepreneurial School
Programming I
02/2024 - present
MCI - The Entrepreneurial School
Smart Systems & Machine Learning

Practice related research and developmental project

03/2024 - present
Research Assistant - MCI - The Entrepreneurial School
Josef Ressel Center for Security Analysis of IoT-Devices
04/2021 - 09/2023
Wissenschaftlicher Mitarbeiter - FFG - Die Österreichische Forschungsförderungsgesellschaft
SMiLE - Secure Machine Learning Applications with Homomorphically Encrypted Data

Peer reviewed journal article

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

Peer reviewed academic/professional meeting proceedings

Russold, M., Nocker, M., & Schöttle, P. (2024). Incremental Whole Plate ALPR Under Data Availability Constraints. Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM, 131-140. https://doi.org/10.5220/0012566400003654
Schmidt, J., Pietsch, V., Nocker, M., Rader, M., & Montuoro, A. (2024). Navigating the Trade-Off Between Explainability and Privacy. Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISIGRAPP, 726-733. https://doi.org/10.5220/0012472200003660
Merkle, F., Sirbu, M. R., Nocker, M., & Schöttle, P. (2024). Generating Invariance-Based Adversarial Examples: Bringing Humans Back into the Loop. In G. L. Foresti, A. Fusiello, & E. Hancock (Hrsg.), Image Analysis and Processing—ICIAP 2023 Workshops (S. 15-27). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-51023-6_2
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).

Supervised bachelor theses

Oberhofer Samuel (2024): Invariance-Based Adversarial Examples: Algorithmic Creation and Human Evaluation for Image Classification
Aster Pirmin (2024): Maschinelle Fehlererkennung im technischen Keramik-3D-Druck
Meusburger Matthias (2024): Analysis of Air Quality Data Using Homomorphic Encryption
Koudelka Paul (2023): Explainable Machine Learning Algorithms While Using Homomorphic Encryption