Dr. Ozan Özdenizci
Short Bio
Ozan Özdenizci is a research group leader at the Chair of Cyber-Physical-Systems at the Montanuniversität Leoben in Austria, since April 2024. Prior to joining CPS, he was a postdoctoral researcher at the Institute of Theoretical Computer Science at Graz University of Technology. He received his PhD in electrical engineering from Northeastern University (Boston, MA, USA) in 2020, and his BSc and MSc degrees from Sabancı University (Istanbul, Turkey). His research is focused in the domain of robust, secure and efficient deep learning algorithms for reliable artificial intelligence systems, and statistical signal processing with biomedical applications.
Research Interests
Machine learning, security and privacy in deep learning, adversarial machine learning, resource-efficient learning algorithms, brain-inspired neural computation, generative artificial intelligence, statistical signal processing, biomedical and neural data analysis, neuroinformatics.
Contact
Dr. Ozan Özdenizci
Research Group Leader at the Chair of Cyber-Physical-Systems
Montanuniversität Leoben
Franz-Josef-Straße 18,
8700 Leoben, Austria
Phone: +43 3842 402 – 1903
Email: ozan.oezdenizci@unileoben.ac.at
Chat: WEBEX
Selected Publications
[1] O. Özdenizci, R. Legenstein, “Adversarially robust spiking neural networks through conversion”, Transactions on Machine Learning Research, 2024.
[2] O. Özdenizci, R. Legenstein, “Restoring vision in adverse weather conditions with patch-based denoising diffusion models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.
[3] O. Özdenizci, R. Legenstein, “Improving robustness against stealthy weight bit-flip attacks by output code matching”, CVPR 2022.
[4] O. Özdenizci, R. Legenstein, “Training adversarially robust sparse networks via Bayesian connectivity sampling”, ICML 2021.
[5] O. Özdenizci, Y. Wang, T. Koike-Akino, D. Erdogmus, “Learning invariant representations from EEG via adversarial inference”, IEEE Access, 2020.
[6] O. Özdenizci, D. Erdogmus, “Information theoretic feature transformation learning for brain interfaces”, IEEE Transactions on Biomedical Engineering, 2019.
[7] O. Özdenizci, M. Yalcin, A. Erdogan, V. Patoglu, M. Grosse-Wentrup, M. Cetin, “Electroencephalographic identifiers of motor adaptation learning”, Journal of Neural Engineering, 2017.
A complete list of publications can be found on Google Scholar.