GPUs as an opportunity for offloading garbage collection. - Martin Maas 001; Philip Reames; Jeffrey Morlan; Krste Asanovic; Anthony D. Joseph; John Kubiatowicz (ISMM, 2012)

PHANTOM: practical oblivious computation in a secure processor. - Martin Maas 001; Eric Love; Emil Stefanov; Mohit Tiwari; Elaine Shi; Krste Asanovic; John Kubiatowicz; Dawn Song (CCS, 2013)

Callisto: co-scheduling parallel runtime systems. - Tim Harris 001; Martin Maas 001; Virendra J. Marathe (EuroSys, 2014)

GhostRider: A Hardware-Software System for Memory Trace Oblivious Computation. - Chang Liu 021; Austin Harris 001; Martin Maas 001; Michael W. Hicks; Mohit Tiwari; Elaine Shi (ASPLOS, 2015)

Trash Day: Coordinating Garbage Collection in Distributed Systems. - Martin Maas 001; Tim Harris 001; Krste Asanovic; John Kubiatowicz (HotOS, 2015)

Taurus: A Holistic Language Runtime System for Coordinating Distributed Managed-Language Applications. - Martin Maas 001; Krste Asanovic; Tim Harris 001; John Kubiatowicz (ASPLOS, 2016)

Return of the Runtimes: Rethinking the Language Runtime System for the Cloud 3.0 Era. - Martin Maas 001; Krste Asanovic; John Kubiatowicz (HotOS, 2017)

Hardware and Software Support for Managed-Language Workloads in Data Centers. - Martin Maas 001 (2017)

A Hardware Accelerator for Tracing Garbage Collection. - Martin Maas 001; Krste Asanovic; John Kubiatowicz (IEEE Micro, 2019)

Learning-based Memory Allocation for C++ Server Workloads. - Martin Maas 001; David G. Andersen; Michael Isard; Mohammad Mahdi Javanmard; Kathryn S. McKinley; Colin Raffel (ASPLOS, 2020)

Hardware support for managed languages: an old idea whose time has finally come? (keynote). - Martin Maas 001 (MPLR, 2020)

A Taxonomy of ML for Systems Problems. - Martin Maas 001 (IEEE Micro, 2020)

Adaptive huge-page subrelease for non-moving memory allocators in warehouse-scale computers. - Martin Maas 001; Chris Kennelly; Khanh Nguyen 001; Darryl Gove; Kathryn S. McKinley; Paul Turner (ISMM, 2021)

Learning on Distributed Traces for Data Center Storage Systems. - Giulio Zhou; Martin Maas 001 (MLSys, 2021)

Analyzing a Caching Model. - Leon Sixt; Evan Zheran Liu; Marie Pellat; James Wexler; Milad Hashemi; Been Kim; Martin Maas 001 (CoRR, 2021)

Learning to Design Accurate Deep Learning Accelerators with Inaccurate Multipliers. - Paras Jain 001; Safeen Huda; Martin Maas 001; Joseph E. Gonzalez; Ion Stoica; Azalia Mirhoseini (DATE, 2022)

Distilling the Real Cost of Production Garbage Collectors. - Zixian Cai; Stephen M. Blackburn; Michael D. Bond; Martin Maas 001 (ISPASS, 2022)

Learning to Improve Code Efficiency. - Binghong Chen; Daniel Tarlow; Kevin Swersky; Martin Maas 001; Pablo Ariel Heiber; Ashish Naik; Milad Hashemi; Parthasarathy Ranganathan (CoRR, 2022)

Rethinking Storage Management for Data Processing Pipelines in Cloud Data Centers. - Ubaid Ullah Hafeez; Martin Maas 001; Mustafa Uysal; Richard McDougall (CoRR, 2022)

TelaMalloc: Efficient On-Chip Memory Allocation for Production Machine Learning Accelerators. - Martin Maas 001; Ulysse Beaugnon; Arun Chauhan 001; Berkin Ilbeyi (ASPLOS (1), 2023)

Predicting Dynamic Properties of Heap Allocations using Neural Networks Trained on Static Code: An Intellectual Abstract. - Christian Navasca; Martin Maas 001; Petros Maniatis; Hyeontaek Lim; Guoqing Harry Xu (ISMM, 2023)

Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples. - Phitchaya Mangpo Phothilimthana; Saurabh Kadekodi; Soroush Ghodrati; Selene Moon; Martin Maas 001 (ASPLOS (3), 2024)

LAVA: Lifetime-Aware VM Allocation with Learned Distributions and Adaptation to Mispredictions. - Jianheng Ling; Pratik Worah; Yawen Wang; Yunchuan Kong; Chunlei Wang; Clifford Stein 003; Diwakar Gupta; Jason Behmer; Logan A. Bush; Prakash Ramanan; Rajesh Kumar; Thomas Chestna; et al. (CoRR, 2024)

Combining Machine Learning and Lifetime-Based Resource Management for Memory Allocation and Beyond. - Martin Maas 001; David G. Andersen; Michael Isard; Mohammad Mahdi Javanmard; Kathryn S. McKinley; Colin Raffel (Commun. ACM, 2024)

A Practical Cross-Layer Approach for ML-Driven Storage Placement in Warehouse-Scale Computers. - Chenxi Yang; Yan Li; Martin Maas 001; Mustafa Uysal; Ubaid Ullah Hafeez; Arif Merchant; Richard McDougall (CoRR, 2025)

ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers. - Hannah Lin; Martin Maas 001; Maximilian Roquemore; Arman Hasanzadeh; Fred Lewis; Yusuf Simonson; Tzu-Wei Yang; Amir Yazdanbakhsh; Deniz Altinbüken; Florin Papa; Maggie Nolan Edmonds; Aditya Patil; et al. (CoRR, 2025)