RAIDR: Retention-aware intelligent DRAM refresh. - Jamie Liu; Ben Jaiyen; Richard Veras; Onur Mutlu (ISCA, 2012)

A stencil compiler for short-vector SIMD architectures. - Thomas Henretty; Richard Veras; Franz Franchetti; Louis-Noël Pouchet; J. Ramanujam; P. Sadayappan (ICS, 2013)

When polyhedral transformations meet SIMD code generation. - Martin Kong; Richard Veras; Kevin Stock; Franz Franchetti; Louis-Noël Pouchet; P. Sadayappan (PLDI, 2013)

Capturing the Expert: Generating Fast Matrix-Multiply Kernels with Spiral. - Richard Veras; Franz Franchetti (VECPAR, 2014)

A scale-free structure for power-law graphs. - Richard Veras; Tze Meng Low; Franz Franchetti (HPEC, 2016)

Compilers, hands-off my hands-on optimizations. - Richard Veras; Doru-Thom Popovici; Tze Meng Low; Franz Franchetti (WPMVP@PPoPP, 2016)

Automating the Last-Mile for High Performance Dense Linear Algebra. - Richard Michael Veras; Tze Meng Low; Tyler Michael Smith; Robert A. van de Geijn; Franz Franchetti (CoRR, 2016)

A scale-free structure for real world networks. - Richard Michael Veras; Franz Franchetti (HPEC, 2017)

A Systematic Approach for Obtaining Performance on Matrix-Like Operations. - Richard Veras (2017)

SPIRAL: Extreme Performance Portability. - Franz Franchetti; Tze Meng Low; Doru-Thom Popovici; Richard Michael Veras; Daniele G. Spampinato; Jeremy R. Johnson; Markus Püschel; James C. Hoe; José M. F. Moura (Proc. IEEE, 2018)

Analytical cache modeling and tilesize optimization for tensor contractions. - Rui Li 033; Aravind Sukumaran-Rajam; Richard Veras; Tze Meng Low; Fabrice Rastello; Atanas Rountev; P. Sadayappan (SC, 2019)

On Large-Scale Matrix-Matrix Multiplication On Compressed Structures. - Sudhindra Gopal Krishna; Aditya Narasimhan; Sridhar Radhakrishnan; Richard Veras (IEEE BigData, 2021)

Using q-learning to select the best among functionally equivalent implementations. - Meggie van den Oever; Lauren E. Grimley; Richard Michael Veras (ARRAY@PLDI, 2022)

A Framework for Analyzing the Robustness of Graph Models. - Khaled Abdelaal; Richard Veras (HPEC, 2023)

Observe Locally, Classify Globally: Using GNNs to Identify Sparse Matrix Structure. - Khaled Abdelaal; Richard Veras (IWANN (1), 2023)

A Framework for Analyzing the Performance of Sparse Matrix and Graph Operations. - Khaled Abdelaal; Richard Veras (HPEC, 2024)

Reinforcement Learning for Graph Coloring: Understanding the Power and Limits of Non-Label Invariant Representations. - Chase Cummins; Richard Veras (CoRR, 2024)