CTSTA 2023 Contributors - CTSTA 2023

Sat 17 - Wed 21 June 2023 Orlando, Florida, United States

co-located with PLDI 2023

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Results (16)

A Saman AmarasingheAmarasinghe, Saman Massachusetts Institute of TechnologyUnited States Compiler Support for Structured Data Closing Committee Member in Organizing Committee within the CTSTA-track
B Aart BikBik, Aart Google, Inc.United States Moving the MLIR Sparse Compilation Pipeline into Production
D Maryam Mehri DehnaviDehnavi, Maryam Mehri University of TorontoCanada Accelerating Sparse Matrix Computations with Code Specialization
H Mary HallHall, Mary University of UtahUnited States Integrating Data Layout into Compilers and Code Generators
Olivia HsuHsu, Olivia Stanford UniversityUnited States The Sparse Abstract Machine
K Raghavendra KanakagiriKanakagiri, Raghavendra University of Illinois Urbana-Champaign A General Distributed Framework for Contraction of a Sparse Tensor with a Tensor Network
Gokcen KestorKestor, Gokcen Pacific Northwest National Laboratory Tackling the challenges of high-performance graph analytics at compiler level
Fredrik KjolstadKjolstad, Fredrik Stanford UniversityUnited States Introduction Software and Hardware for Sparse ML Closing Committee Member in Organizing Committee within the CTSTA-track
L Peiming LiuLiu, Peiming Google Inc Moving the MLIR Sparse Compilation Pipeline into Production
Shiwei LiuLiu, Shiwei The University of Texas at Austin, Texas, USA Fantastic Sparse Masks and Where to Find Them
N Nandeeka NayakNayak, Nandeeka University of Illinois at Urbana-ChampaignUnited States TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators
S Amir ShaikhhaShaikhha, Amir University of EdinburghUnited Kingdom Automatic Differentiation for Sparse Tensors
Michelle StroutStrout, Michelle University of ArizonaUnited States Committee Member in Organizing Committee within the CTSTA-track
Kirshanthan SundararajahSundararajah, Kirshanthan Purdue UniversityUnited States Autoscheduling for Sparse Tensor Contraction
W Jaeyeon WonWon, Jaeyeon Massachusetts Institute of Technology Learning workload-aware cost model for sparse tensor program
Y Zihao YeYe, Zihao University of WashingtonUnited States Challenges and Opportunities for Sparse Compilers in LLM