Milad Hashemi

Google (United States) — United States

Computer Science · Physical Sciences

12h-index792citations34works2.72yr avg
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Research Topics

Parallel Computing and Optimization Techniques(16), Advanced Data Storage Technologies(11), Interconnection Networks and Systems(5), Machine Learning and Data Classification(5), Advanced Neural Network Applications(4)

Publications34 total

Automated High-Level Code Optimization for Warehouse Performance
IEEE Micro·2025
ECO: An LLM-Driven Efficient Code Optimizer for Warehouse Scale Computers
arXiv (Cornell University)·2025Open Access
Limoncello: Prefetchers for Scale
2024· 8 cited
CUF: Continuous Upsampling Filters
2023· 12 cited
Learning Performance-Improving Code Edits
arXiv (Cornell University)·2023· 23 citedOpen Access
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
2022 IEEE International Conference on Data Mining (ICDM)·2022· 144 cited
Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks
arXiv (Cornell University)·2022· 4 citedOpen Access
CUF: Continuous Upsampling Filters
arXiv (Cornell University)·2022Open Access
Learning to Improve Code Efficiency
arXiv (Cornell University)·2022· 4 citedOpen Access
Data-Driven Offline Optimization For Architecting Hardware Accelerators
arXiv (Cornell University)·2021· 2 citedOpen Access
A hierarchical neural model of data prefetching
2021· 78 cited
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
arXiv (Cornell University)·2021· 25 citedOpen Access
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
arXiv (Cornell University)·2021· 8 citedOpen Access
Apollo: Transferable Architecture Exploration
arXiv (Cornell University)·2021· 13 citedOpen Access
No MCMC for me: Amortized sampling for fast and stable training of energy-based models
arXiv (Cornell University)·2020· 7 citedOpen Access
Learned Hardware/Software Co-Design of Neural Accelerators
arXiv (Cornell University)·2020· 8 citedOpen Access
Machine Learning for Systems
IEEE Micro·2020· 5 cited
An Imitation Learning Approach for Cache Replacement
arXiv (Cornell University)·2020· 5 citedOpen Access
Neural Execution Engines: Learning to Execute Subroutines
arXiv (Cornell University)·2020· 7 citedOpen Access
Neural Execution Engines: Learning to Execute Subroutines
Neural Information Processing Systems·2020· 8 cited
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Frequent Co-authors

Kevin Swersky(19), Parthasarathy Ranganathan(13), Yale N. Patt(6), Yujun Yan(5), Danai Koutra(5), Amir Yazdanbakhsh(5), Khubaib Khubaib(3), Onur Mutlu(3), Heiner Litz(3), Yaoqing Yang(2), Zhan Shi(2), Akanksha Jain(2), Calvin Lin(2), Eiman Ebrahimi(2), Jamie Smith(2), Grant Ayers(2), Jichuan Chang(2), Christos Kozyrakis(2), Aman Madaan(2), Alexander Shypula(2)