• Presented our paper titled "Deepsteal: Advanced model extractions leveraging efficient weight stealing in memories" at IEEE Security and Privacy Oakland 2022.​​
  • Presented our paper titled ”Deep-Dup: An Adversarial Weight Duplication Attack Framework to Crush Deep Neural Network in Multi-Tenant FPGA” in USENIX Security Symposium 2021. ​
  • Presented our paper titled ”Tbt: Targeted neural network attack with bit trojan” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition in 2020.
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  • Presented my internship work "Towards Universal Adversarial Examples and Defenses" at Information Theory Workshop, October 2021. 
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  • Presented our paper titled” Robust Sparse Regularization: Defending Adversarial Attacks Via Regularized Sparse Network” in GLSVLSI 2020: Proceedings of the 2020 on Great Lakes Symposium on VLSI.