Publications and Related Works
Published Work
Canopy: Property-Driven Learning for Congestion Control
Chenxi Yang, Divyanshu Saxena, Rohit Dwivedula, Kshiteej Mahajan, Swarat Chaudhuri, Aditya Akella
EuroSys, April 2026
A Joint Learning Approach to Hardware Caching and Prefetching
Samuel Yuan, Divyanshu Saxena, Jiayi Chen, Nihal Sharma, Aditya Akella
MLSys@Neurips, December 2025
Man-Made Heuristics Are Dead. Long Live Code Generators!
Rohit Dwivedula, Divyanshu Saxena, Aditya Akella, Swarat Chaudhuri, Daehyeok Kim
HotNets, November 2025
Large Language Models as Realistic Microservice Trace Generators
Donghyun Kim, Sriram Ravula, Taemin Ha, Alexandros G. Dimakis, Daehyeok Kim, Aditya Akella
EMNLP, November 2025
Quilt: Resource-aware Merging of Serverless Workflows
Yuxuan Zhang, Sebastian Angel
SOSP, October 2025
OQueue: Observable Communication in Learning Directed Operating Systems
Aditya Tewari, Sujay Yadalam, Arthur Peters, Saurabh Agarwal, Aditya Akella, Michael Swift, Christopher Rossbach
PACMI, October 2025
Striking the Right Chord: Parameter Tuning in Memory Tiering
Konstantinos Kanellis*, Sujay Yadalam*, Shivaram Venkataraman, Michael Swift
DIMES, October 2025
Structural Temporal Logic for Mechanized Program Verification
Eleftherios Ioannidis, Yannick Zakowski, Steve Zdancewic, Sebastian Angel
OOPSLA, October 2025
ConfigBot: Adaptive Resource Allocation for Robot Applications in Dynamic Environments
Rohit Dwivedula, Sadanand Modak, Aditya Akella, Joydeep Biswas, Daehyeok Kim, Christopher Rossbach
IROS, October 2025
Warbler: Speculative Distributed Transactions with Geo-Replication
Weihai Shen, Yang Cui, Siddhartha Sen, Sebastian Angel, Shuai Mu
OSDI 2025, July 2025
How I learned to stop worrying and love learned OS policies
Divyanshu Saxena, Jiayi Chen, Sujay Yadalam, Yeonju Ro, Rohit Dwivedula, Eric Campbell, Aditya Akella, Christopher Rossbach, Michael Swift.
HotOS, May 2025
CONGO: Compressive Online Gradient Optimization
Jeremy Carleton, Prathik Vijaykumar, Divyanshu Saxena, Dheeraj Narasimha, Srinivas Shakkottai, Aditya Akella
The Thirteenth International Conference on Learning Representations, 2025.
Related Publications
Beyond First-Order Tweedie: Solving Inverse Problems using Latent Diffusion.
Litu Rout, Yujia Chen, Abhishek Kumar, Constantine Caramanis, Sanjay Shakkottai, Wen-Sheng Chu.
CVPR, 2024
Deploying and Evaluating LLMs to Program Service Mobile Robots.
Zichao Hu, Francesca Lucchetti, Claire Schlesinger, Yash Saxena, Anders Freeman, Sadanand Modak, Arjun Guha, Joydeep Biswas.
IEEE Robotics and Automation Letters, 2024.
Programming-by-Demonstration for Long-Horizon Robot Tasks.
Noah Patton, Kia Rahmani, Meghana Missula, Joydeep Biswas, Işil Dillig.
Proceedings of the ACM on Programming Languages (POPL), 2024.
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation.
Chenxi Yang, Greg Anderson, and Swarat Chaudhuri.
IEEE Conference on Secure and Trustworthy Machine Learning (SatML), 2024
Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models.
Litu Rout, Negin Raoof, Giannis Daras, Constantine Caramanis, Alexandros G. Dimakis, Sanjay Shakkottai.
NeurIPS 2023
On a Foundation Model for Operating Systems.
Divyanshu Saxena, Nihal Sharma, Donghyun Kim, Rohit Dwivedula, Jiayi Chen, Chenxi Yang, Sriram Ravula, Zichao Hu, Aditya Akella, Sebastian Angel, Joydeep Biswas, Swarat Chaudhuri, Işil Dillig, Alex Dimakis, Brighten P Godfrey, Daehyeok Kim, Christopher Rossbach, and Gang Wang.
MLSys Workshop at NeurIPS 2023
Performance Roulette: How Cloud Weather Affects ML-Based System Optimization.
Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman.
MLSys Workshop at NeurIPS 2023.
Darwin: Flexible Learning-based CDN Caching
Jiayi Chen, Nihal Sharma, Tarannum Khan, Shu Liu, Brian Chang, Aditya Akella, Sanjay Shakkottai, and Ramesh Sitaraman.
SIGCOMM 2023
Murphy: Performance Diagnosis of Distributed Cloud Applications.
Vipul Harsh, Wenxuan Zhou, Sachin Ashok, Radhika Niranjan Mysore, P. Brighten Godfrey, and Sujata Banerjee.
ACM SIGCOMM, September 2023.
Towards a Machine Learning-Assisted Kernel with LAKE.
Henrique Fingler, Isha Tarte, Hangchen Yu, Ariel Szekely, Bodun Hu, Aditya Akella, Christopher Rossbach.
ASPLOS 2023, Vancouver, Canada.
Guiding Safe Exploration with Weakest Preconditions.
Greg Anderson, Swarat Chaudhuri, Işil Dillig.
International Conference on Learning Representations (ICLR), 2023.
Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning.
Yiping Ma, Jess Woods, Sebastian Angel, Antigoni Polychroniadou, and Tal Rabin.
S&P 2023.
CBMM: Financial Advice for Kernel Memory Managers.
Mark Mansi, Bijan Tabatabai and Michael M. Swift.
USENIX ATC ’22, July 2022.
Safe Neurosymbolic Learning with Differentiable Symbolic Execution.
Chenxi Yang and Swarat Chaudhuri.
International Conference on Learning Representations (ICLR), 2022.