My research interests lie in Computer Security, Machine Learning and Cryptography. In my current work, I study the worst-case behavior of Deep Learning systems from an adversarial perspective, to understand and mitigate long-term threats to the safety and privacy of users.
Does Adversarial Machine Learning Research Matter? (AdvML 2021)
Measuring and Enhancing the Security of Machine Learning (my "job talk")
Adversarial Examples (Machine Learning Street Talk)
Remote Side-Channel Attacks on Anonymous Cryptocurrencies (USENIX Security)
On Adaptive Attacks to Adversarial Examples Defenses (USENIX ScAINet)
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware (ICLR)