Steven S. Lyubomirsky
sslyu (at) cs.washington.edu
I am a doctoral candidate at the University of Washington's Programming Languages and Software Engineering group. My adviser is Zachary Tatlock. I am broadly interested in compilers and tools related to compilers, such as in producing tools to enable new kinds of programming, including tools built on proof assistants and SMT solvers to develop programs with proven correctness properties and compilers for domain-specific languages that better encode expert knowledge to achieve greater performance and expressiveness.
Presently I am part of SAMPL, which is an interdisciplinary research group holistically exploring problems spanning the machine learning stack. I am specifically working on projects related to Relay, whose purpose is to enable new abstractions and optimizations in machine-learning frameworks by providing an expressive intermediate representation and new compilation pipeline for TVM.
I have previously worked on the Bagpipe project, for verifying BGP configurations. An offshoot of the Bagpipe project is the SpaceSearch library, allowing for reasoning about the reduction of a problem to SMT in Coq and then extracting the proof of correctness to a solver-aided tool in Rosette.
I am not a member of the PLSE running club, Race Condition Running. I am, however, the only member of the Mutual Exclusion Walking Association. Its membership is very exclusive. In fact, no one can join because the waiting list is deadlocked.
Marisa Kirisame*, Steven Lyubomirsky*, Altan Haan*, Jennifer Brennan, Mike He, Jared Roesch, Tianqi Chen, and Zachary Tatlock. 2021. Dynamic Tensor Rematerialization. ICLR 2021 (spotlight). ArXiv link. Recorded talk. Conference page. (*Equal contribution.)
Konstantin Weitz, Steven Lyubomirsky, Stefan Heule, Emina Torlak, Michael D. Ernst, and Zachary Tatlock. 2017. SpaceSearch: a library for building and verifying solver-aided tools. Proc. ACM Program. Lang. 1, ICFP, Article 25 (August 2017), 28 pages. DOI: https://doi.org/10.1145/3110269
Gus Henry Smith, Andrew Liu, Steven Lyubomirsky, Scott Davidson, Joseph McMahan, Michael Taylor, Luis Ceze, Zachary Tatlock. 2021. Pure, Low-Level Tensor Program Rewriting via Access Patterns (Representation Pearl). To appear at MAPS '21 (at PLDI). ArXiv link.
Bo-Yuan Huang*, Steven Lyubomirsky*, Thierry Tambe*, Yi Li, Mike He, Gus Smith, Gu-Yeon Wei, Aarti Gupta, Sharad Malik, and Zachary Tatlock. 2021. From DSLs to Accelerator-Rich Platform Implementations: Addressing the Mapping Gap. LATTE '21 (ASPLOS Workshop). https://capra.cs.cornell.edu/latte21/paper/30.pdf. Recorded talk. (*Equal contribution.)
Jared Roesch, Steven Lyubomirsky, Logan Weber, Josh Pollock, Marisa Kirisame, Tianqi Chen, and Zachary Tatlock. 2018. Relay: a new IR for machine learning frameworks. In Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages (MAPL 2018). ACM, New York, NY, USA, 58-68. DOI: https://dl.acm.org/citation.cfm?doid=3211346.3211348
Jared Roesch, Steven Lyubomirsky, Marisa Kirisame, Logan Weber, Josh Pollock, Luis Vega, Ziheng Jiang, Tianqi Chen, Thierry Moreau, and Zachary Tatlock. 2019. Relay: A High-Level Compiler for Deep Learning. ArXiv:1904.08368 [Cs, Stat], arXiv.org, http://arxiv.org/abs/1904.08368.