The Ramifications of Making Deep Neural Networks Compact. - Nandan Kumar Jha; Sparsh Mittal; Govardhan Mattela (VLSID, 2019)

DRACO: Co-Optimizing Hardware Utilization, and Performance of DNNs on Systolic Accelerator. - Nandan Kumar Jha; Shreyas Ravishankar; Sparsh Mittal; Arvind Kaushik; Dipan Mandal; Mahesh Chandra (ISVLSI, 2020)

E2GC: Energy-efficient Group Convolution in Deep Neural Networks. - Nandan Kumar Jha; Rajat Saini; Subhrajit Nag; Sparsh Mittal (VLSID, 2020)

ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks. - Rajat Saini; Nandan Kumar Jha; Bedanta Das; Sparsh Mittal; C. Krishna Mohan (WACV, 2020)

On the Demystification of Knowledge Distillation: A Residual Network Perspective. - Nandan Kumar Jha; Rajat Saini; Sparsh Mittal (CoRR, 2020)

DeepPeep: Exploiting Design Ramifications to Decipher the Architecture of Compact DNNs. - Nandan Kumar Jha; Sparsh Mittal; Binod Kumar 001; Govardhan Mattela (ACM J. Emerg. Technol. Comput. Syst., 2020)

Digital Storytelling: The Integration of Intangible and Tangible Heritage in the City of Surat, India. - Chika Udeaja; Lukman E. Mansuri; Busisiwe Chikomborero Ncube Makore; Kwasi Gyau Baffour Awuah; Dilip A. Patel; Claudia Trillo; Nandan Kumar Jha (HCI (33), 2021)

DeepReDuce: ReLU Reduction for Fast Private Inference. - Nandan Kumar Jha; Zahra Ghodsi; Siddharth Garg; Brandon Reagen (ICML, 2021)

Circa: Stochastic ReLUs for Private Deep Learning. - Zahra Ghodsi; Nandan Kumar Jha; Brandon Reagen; Siddharth Garg (NeurIPS, 2021)

Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning. - Karthik Garimella; Nandan Kumar Jha; Brandon Reagen (CoRR, 2021)

CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale. - Karthik Garimella; Nandan Kumar Jha; Zahra Ghodsi; Siddharth Garg; Brandon Reagen (CoRR, 2021)

Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance. - Nandan Kumar Jha; Sparsh Mittal (IEEE Trans. Computers, 2021)

Characterizing and Optimizing End-to-End Systems for Private Inference. - Karthik Garimella; Zahra Ghodsi; Nandan Kumar Jha; Siddharth Garg; Brandon Reagen (ASPLOS (3), 2023)

ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models. - Nandan Kumar Jha; Brandon Reagen (CoRR, 2024)

AERO: Softmax-Only LLMs for Efficient Private Inference. - Nandan Kumar Jha; Brandon Reagen (CoRR, 2024)

TruncFormer: Private LLM Inference Using Only Truncations. - Patrick Yubeaton; Jianqiao Mo; Karthik Garimella; Nandan Kumar Jha; Brandon Reagen; Chinmay Hegde; Siddharth Garg (CoRR, 2024)

DeepReShape: Redesigning Neural Networks for Efficient Private Inference. - Nandan Kumar Jha; Brandon Reagen (Trans. Mach. Learn. Res., 2024)

Entropy-Guided Attention for Private LLMs. - Nandan Kumar Jha; Brandon Reagen (CoRR, 2025)

A Random Matrix Theory Perspective on the Learning Dynamics of Multi-head Latent Attention. - Nandan Kumar Jha; Brandon Reagen (CoRR, 2025)