I am a PhD candidate at the Center for Cybersecurity, New York University (NYU), advised by Prof. Brandon Reagen. I’m broadly interested in cryptographically secure privacy-preserving machine learning (PPML) and work at the intersection of deep learning and applied cryptography (homomorphic encryption and multiparty computation) as a part of DPRIVE projects. My research primarily focuses on developing innovative architectures and algorithms to optimize neural network computations on encrypted data.
In my early PhD, I worked on designing nonlinear-efficient CNNs and developed ReLU-optimization techniques (DeepReDuce, ICML'21), and proposed methods for redesigning existing CNNs (DeepReShape, TMLR'24) for end-to-end private inference efficiency.
My current research focuses on the privacy and security of large language models (LLMs). Specifically, I am investigating the role of nonlinearity in GPT models (Our preliminary findings, ATTRIB@NeurIPS'24), aiming to develop innovative methods for designing GPT models with fewer nonlinearities for efficient private inference.
I have also served as an invited reviewer for NeurIPS'23 and ‘24, ICLR'24, CVPR'24, and ICML'24. If you are interested in collaborating, please feel free to email me!
Ph.D. in Privacy-preserving Deep Learning, 2020 - present
New York University
M.Tech. (Research Assistant) in Computer Science and Engineering, 2017 - 2020
Indian Institute of Technology Hyderabad
B.Tech. in Electronics and Communication Engineering, 2009 - 2013
National Institute of Technology Surat
DeepReShape is the first work to conduct a rigorous characterization of desirable neural network attributes for efficient Private Inference (PI). We discovered that distinct network attributes are required for different ReLU counts; in particular, wider networks are beneficial only for higher ReLU counts, whereas networks with a greater proportion of least-critical ReLU are desirable for lower ReLU counts. Further, we introduced a novel network design principle called “ReLU-equalization” to strategically allocate channels within the network to optimize ReLUs and FLOPs efficiency simultaneously. DeepReShape outperforms the current SOTA (SENets, ICLR'23) by achieving a 2.1% increase in accuracy and a 5.2x faster runtime at iso-ReLU counts on CIFAR-100, and an 8.7x faster runtime at iso-accuracy on the TinyImageNet dataset.
DeepReDuce is a set of optimizations for the judicious removal of ReLUs to reduce private inference latency by leveraging the ReLUs heterogeneity in classical networks. DeepReDuce strategically drops ReLUs upto 4.9x (on CIFAR-100) and 5.7x (on TinyImageNet) for ResNet18 with no loss in accuracy. Compared to the state-of-the-art for private inference DeepReDuce improves accuracy and reduces ReLU count by up to 3.5% (iso-ReLU) and 3.5×(iso-accuracy), respectively.
Responsibilities include: