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.
Evil from Within: Machine Learning Backdoors Through Dormant Hardware Trojans.
Proc. of the 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.
Manipulating Feature Visualizations with Gradient Slingshots.
Technical report, arXiv:2401.06122, 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.
Proc. of the 30th Network and Distributed System Security Symposium (NDSS), 2023.
Dos and Don'ts of Machine Learning in Computer Security.
Proc. of the 31st USENIX Security Symposium, 2022.
Distinguished Paper Award
Explaining Graph Neural Networks for Vulnerability Discovery.
Proc. of the 14th ACM Workshop on Artificial Intelligence and Security (AISEC), 2021.
Best Paper Award
TagVet: Vetting Malware Tags using Explainable Machine Learning.
Proc. of the 14th ACM European Workshop on Systems Security (EuroSec), 2021.
Evaluating Explanation Methods for Deep Learning in Security.
Proc. of the 5th IEEE European Symposium on Security and Privacy (EuroS&P), 2020.