Characterizing and Optimizing End-to-End Systems for Private Inference
Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen
ASPLOS, 2023
[arXiv]
[code]
[video]
[poster]
Links
[Google Scholar]
[GitHub]
[Resume]
kvgarimella AT nyu DOT edu
Bio
I'm an ECE PhD student at New York University advised by Brandon Reagen. I also work with Siddharth Garg. I'm broadly interested in machine learning, systems, and security. Currently, my research focuses on fully homomorphic encryption, multi-party computation, and machine learning security & privacy. In the summer of 2024, I interned at NVIDIA Research where I worked on accelerating FHE (CKKS) on GPUs.
Previously, I received an MS in Computer Engineering from Washington University in St. Louis where I was advised by Xuan Zhang working on adversarial machine learning in autonomous vehicles. I hold a BA in Physics from Hendrix College.
Outside of research, I enjoy playing tennis, cooking, reading, and biking around NYC.
Publications
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale
Karthik Garimella, Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen
arxiv pre-print, 2021
[arXiv]
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
Karthik Garimella, Nandan Kumar Jha, Brandon Reagen
Privacy Preserving Machine Learning Workshop @ ACM CCS, 2021
[arXiv]
[code]
[poster]
Attacking Vision-based Perception in End-to-End Autonomous Driving Models
Adith Boloor, Karthik Garimella, Xin He, Christopher Gill, Yevgeniy Vorobeychik, Xuan Zhang
Journal of Systems Architecture, 2020
[arXiv]
[code]
CARLA Autonomous Driving Challenge 2019
Adith Boloor, Karthik Garimella, Jinghan Yang, Christopher Gill, Yevgeniy Vorobeychik, Ayan Chakrabarti, Xuan Zhang
Invited talk at CVPR, 2019
[CVPR workshop]
[code]
Teaching
Head Graduate Teaching Assistant - Deep Learning Spring 2023 @ NYU ECE
Head Graduate Teaching Assistant - Computer Architecture Fall 2023 @ NYU ECE