I am a PhD student at Technische Universität Berlin. I am part of the Chair of Machine Learning and Security within the Berlin Institute for the Foundations of Learning and Data (BIFOLD). My research revolves around applications of Machine Learning for Computer Security aswell as privacy and security of modern Machine Learning Models.
Manipulating Feature Visualizations with Gradient Slingshots.
Advances in Neural Information Processing Systems 39 (NeurIPS), 2025. (to appear)
Evil from Within: Machine Learning Backdoors Through Dormant Hardware Trojans.
40th Annual Computer Security Applications Conference (ACSAC), 2024.
Pitfalls in Machine Learning for Computer Security.
Communications of the ACM, 67, (11), 2024.
Security Viewpoints on Explainable Machine Learning.
PhD thesis, Technische Universität Berlin, 2024.
Lessons Learned on Machine Learning for Computer Security.
IEEE Security & Privacy, 21, (4), 2023.
Evil from Within: Machine Learning Backdoor through Hardware Trojans.
Technical report, arXiv:2304.08411, 2023.
Machine Unlearning of Features and Labels.
30th Network and Distributed System Security Symposium (NDSS), 2023.
Dos and Don'ts of Machine Learning in Computer Security.
31st USENIX Security Symposium, 2022.
Distinguished Paper Award
Explaining Graph Neural Networks for Vulnerability Discovery.
14th ACM Workshop on Artificial Intelligence and Security (AISEC), 2021.
Best Paper Award
TagVet: Vetting Malware Tags using Explainable Machine Learning.
14th ACM European Workshop on Systems Security (EuroSec), 2021.
Evaluating Explanation Methods for Deep Learning in Security.
5th IEEE European Symposium on Security and Privacy (EuroS&P), 2020.