Scholar
Ali Jannesari
Google Scholar ID: YhWnhQEAAAAJ
Associate Professor, Iowa State University
high-performance computing
machine learning
parallel computing
software analytics
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Citations & Impact
All-time
Citations
1,531
H-index
20
i10-index
51
Publications
20
Co-authors
13
list available
Contact
Email
jannesari@iastate.edu
Publications
29 items
Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
2026
Cited
0
A Lock-Free Work-Stealing Algorithm for Bulk Operations
2026
Cited
0
Rudder: Steering Prefetching in Distributed GNN Training using LLM Agents
2026
Cited
0
OptiML: An End-to-End Framework for Program Synthesis and CUDA Kernel Optimization
2026
Cited
0
Split-on-Share: Mixture of Sparse Experts for Task-Agnostic Continual Learning
2026
Cited
0
Dynamic Detection of Inefficient Data Mapping Patterns in Heterogeneous OpenMP Applications
ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming · 2026
Cited
0
AgenticPruner: MAC-Constrained Neural Network Compression via LLM-Driven Strategy Search
2026
Cited
0
ZeroDVFS: Zero-Shot LLM-Guided Core and Frequency Allocation for Embedded Platforms
2026
Cited
2
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Resume (English only)
Academic Achievements
Paper accepted by ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC 2025)
Paper accepted by ACM International Conference on Supercomputing (ICS 2025)
Paper accepted by Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2025)
Paper accepted by Conference on Neural Information Processing Systems (NeurIPS 2024)
Two papers accepted at International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2024)
Background
Associate Professor in the Department of Computer Science at Iowa State University
Director of the Software Analytics and Pervasive Parallelism (SwAPP) Lab
Research focuses on the intersection of High-Performance Computing (HPC) and Artificial Intelligence (AI)
Aims to build reliable and efficient software using AI, parallel computing, and HPC
Helps developers utilize modern heterogeneous parallel computing platforms for complex software in data science and HPC
Co-authors
13 total
Felix Wolf
Professor of Computer Science, TU Darmstadt
Co-author 2
Nesreen K. Ahmed
Senior Principal Scientist, Cisco AI Research, Intel Labs, Purdue University
Co-author 4
Co-author 5
Co-author 6
Nathan R. Tallent
Pacific Northwest National Laboratory
Co-author 8
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