| 2025 | EMNLP | Spectral Scaling Laws in Language Models: emphHow Effectively Do Feed-Forward Networks Use Their Latent Space? | Nandan Kumar Jha, Brandon Reagen |
| 2025 | ICCAD | Network and Compiler Optimizations for Efficient Linear Algebra Kernels in Private Transformer Inference (Invited Paper). | Karthik Garimella, Negar Neda, Austin Ebel, Nandan Kumar Jha, Brandon Reagen |
| 2023 | ASPLOS | Characterizing and Optimizing End-to-End Systems for Private Inference. | Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha, Siddharth Garg, Brandon Reagen |
| 2021 | HCI | 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 |
| 2021 | ICML | DeepReDuce: ReLU Reduction for Fast Private Inference. | Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, Brandon Reagen |
| 2020 | WACV | ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks. | Rajat Saini, Nandan Kumar Jha, Bedanta Das, Sparsh Mittal, C. Krishna Mohan |
| 2020 | VLSID | E2GC: Energy-efficient Group Convolution in Deep Neural Networks. | Nandan Kumar Jha, Rajat Saini, Subhrajit Nag, Sparsh Mittal |
| 2019 | VLSID | The Ramifications of Making Deep Neural Networks Compact. | Nandan Kumar Jha, Sparsh Mittal, Govardhan Mattela |