Nandan Kumar Jha

Nandan Kumar Jha

PhD student at NYU CCS

New York University

About me

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 primary research focuses on the design and optimization of neural networks for efficient processing of encrypted inputs in Private Inference.

In the first half of my PhD, I worked on 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.

Currently, my research focuses on the privacy and security of large language models (LLMs). Specifically, I am investigating the role of nonlinearities (self-attention, GELU, and LayerNorm) in GPT models, 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, ICLR'24, CVPR'24, and ICML'24. If you are interested in collaborating, please feel free to email me!

  • Privacy-preserving Machine Learning (PPML)
  • Efficient Design of LLMs for Privacy and Security
  • Cryptographic Methods for Secure Neural Network Computation
  • 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

Recent Publications

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(2023). Characterizing and Optimizing End-to-End Systems for Private Inference. In ASPLOS 2023.

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(2021). CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale. In Arxiv Preprint.

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(2021). Circa: Stochastic ReLUs for Private Deep Learning. In NeurIPS 2021.

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(2020). DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs. In ACM JETC 2020.



Seagate Technology
Electrical Design Engineer
Sep 2015 – Jul 2017 Bangalore, INDIA

Responsibilities include:

  • Designing power delivery circuit for M.2 Solid State Drives.
  • Electrical characterization of DRAM and NAND modules
  • Signal Intergrity verification of DRAM/NAND datapath
IIT Bombay
Project Research Assistant
Nov 2014 – Jun 2015 Mumbai, INDIA
Worked on the deployment of wireless broadband in rural areas using the TV white Space (unused licensed band in UHF band)