Smart and Secure Monitoring of Industrial Environments using IoT. - Shruthi Puranik; Jayashree Mohan; K. Chandrasekaran 001 (WCI, 2015)

Optimizing Downloads over Random Duration Links in Mobile Networks. - Amber Bhargava; Spencer Congero; Timothy Ferrell; Alex Jones; Leo Linsky; Jayashree Mohan; Bhaskar Krishnamachari (ICCCN, 2016)

The Dangers and Complexities of SQLite Benchmarking. - Dhathri Purohith; Jayashree Mohan; Vijay Chidambaram (APSys, 2017)

A reinforcement learning approach to optimize downloads over mobile networks. - Jayashree Mohan; Angad Vittal; K. Chandrasekaran 001; Bhaskar Krishnamachari (COMSNETS, 2017)

Storage on Your SmartPhone Uses More Energy Than You Think. - Jayashree Mohan; Dhathri Purohith; Matthew Halpern; Vijay Chidambaram; Vijay Janapa Reddi (HotStorage, 2017)

Analyzing IO Amplification in Linux File Systems. - Jayashree Mohan; Rohan Kadekodi; Vijay Chidambaram (CoRR, 2017)

Finding Crash-Consistency Bugs with Bounded Black-Box Crash Testing. - Jayashree Mohan; Ashlie Martinez; Soujanya Ponnapalli; Pandian Raju; Vijay Chidambaram (OSDI, 2018)

Recipe: converting concurrent DRAM indexes to persistent-memory indexes. - Se Kwon Lee; Jayashree Mohan; Sanidhya Kashyap; Taesoo Kim; Vijay Chidambaram (SOSP, 2019)

Analyzing GDPR Compliance Through the Lens of Privacy Policy. - Jayashree Mohan; Melissa Wasserman; Vijay Chidambaram (Poly/DMAH@VLDB, 2019)

CrashMonkey and ACE: Systematically Testing File-System Crash Consistency. - Jayashree Mohan; Ashlie Martinez; Soujanya Ponnapalli; Pandian Raju; Vijay Chidambaram (ACM Trans. Storage, 2019)

INSTalytics: Cluster Filesystem Co-design for Big-data Analytics. - Muthian Sivathanu; Midhul Vuppalapati; Bhargav S. Gulavani; Kaushik Rajan; Jyoti Leeka; Jayashree Mohan; Piyus Kedia (ACM Trans. Storage, 2020)

CheckFreq: Frequent, Fine-Grained DNN Checkpointing. - Jayashree Mohan; Amar Phanishayee; Vijay Chidambaram (FAST, 2021)

Memory Optimization for Deep Networks. - Aashaka Shah; Chao-Yuan Wu; Jayashree Mohan; Vijay Chidambaram; Philipp Krähenbühl (ICLR, 2021)

Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters. - Jayashree Mohan; Amar Phanishayee; Janardhan Kulkarni; Vijay Chidambaram (CoRR, 2021)

Analyzing and Mitigating Data Stalls in DNN Training. - Jayashree Mohan; Amar Phanishayee; Ashish Raniwala; Vijay Chidambaram (Proc. VLDB Endow., 2021)

Looking Beyond GPUs for DNN Scheduling on Multi-Tenant Clusters. - Jayashree Mohan; Amar Phanishayee; Janardhan Kulkarni; Vijay Chidambaram (OSDI, 2022)

Project Silica: Towards Sustainable Cloud Archival Storage in Glass. - Patrick Anderson 001; Erika Blancada Aranas; Youssef Assaf; Raphael Behrendt; Richard Black; Marco Caballero; Pashmina Cameron; Burcu Canakci; Thales De Carvalho; Andromachi Chatzieleftheriou; Rebekah Storan Clarke; James Clegg; et al. (SOSP, 2023)

SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills. - Amey Agrawal; Ashish Panwar; Jayashree Mohan; Nipun Kwatra; Bhargav S. Gulavani; Ramachandran Ramjee (CoRR, 2023)

VIDUR: A Large-Scale Simulation Framework for LLM Inference. - Amey Agrawal; Nitin Kedia; Jayashree Mohan; Ashish Panwar; Nipun Kwatra; Bhargav S. Gulavani; Ramachandran Ramjee; Alexey Tumanov (MLSys, 2024)

Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve. - Amey Agrawal; Nitin Kedia; Ashish Panwar; Jayashree Mohan; Nipun Kwatra; Bhargav S. Gulavani; Alexey Tumanov; Ramachandran Ramjee (OSDI, 2024)

Metron: Holistic Performance Evaluation Framework for LLM Inference Systems. - Amey Agrawal; Anmol Agarwal; Nitin Kedia; Jayashree Mohan; Souvik Kundu 009; Nipun Kwatra; Ramachandran Ramjee; Alexey Tumanov (CoRR, 2024)

ASTRA: Accurate and Scalable ANNS-based Training of Extreme Classifiers. - Sonu Mehta; Jayashree Mohan; Nagarajan Natarajan; Ramachandran Ramjee; Manik Varma (CoRR, 2024)

POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference. - Aditya K. Kamath; Ramya Prabhu; Jayashree Mohan; Simon Peter 001; Ramachandran Ramjee; Ashish Panwar (ASPLOS (2), 2025)

vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention. - Ramya Prabhu; Ajay Nayak; Jayashree Mohan; Ramachandran Ramjee; Ashish Panwar (ASPLOS (1), 2025)

Towards Efficient Large Multimodal Model Serving. - Haoran Qiu; Anish Biswas; Zihan Zhao; Jayashree Mohan; Alind Khare; Esha Choukse; Íñigo Goiri; Zeyu Zhang 005; Haiying Shen; Chetan Bansal; Ramachandran Ramjee; Rodrigo Fonseca (CoRR, 2025)

Project Silica: Towards Sustainable Cloud Archival Storage in Glass. - Patrick Anderson 001; Erika Blancada Aranas; Youssef Assaf; Raphael Behrendt; Richard Black; Marco Caballero; Pashmina Cameron; Burcu Canakci; Andromachi Chatzieleftheriou; Rebekah Storan Clarke; James Clegg; Daniel Cletheroe; et al. (ACM Trans. Storage, 2025)

Niyama : Breaking the Silos of LLM Inference Serving. - Kanishk Goel; Jayashree Mohan; Nipun Kwatra; Ravi Shreyas Anupindi; Ramachandran Ramjee (CoRR, 2025)

AI Greenferencing: Routing AI Inferencing to Green Modular Data Centers with Heron. - Tella Rajashekhar Reddy; Palak 004; Rohan Gandhi; Anjaly Parayil; Chaojie Zhang; Mike Shepperd; Liangcheng Yu; Jayashree Mohan; Srinivasan Iyengar; Shivkumar Kalyanaraman; Debopam Bhattacherjee (CoRR, 2025)

On Evaluating Performance of LLM Inference Serving Systems. - Amey Agrawal; Nitin Kedia; Anmol Agarwal; Jayashree Mohan; Nipun Kwatra; Souvik Kundu 009; Ramachandran Ramjee; Alexey Tumanov (CoRR, 2025)