Charith Mendis
Assistant Professor
Studies compilers and machine learning for program optimization, including learned cost models and compilers for ML workloads.
Charith Mendis's lab at UIUC works on compilers & programming languages, learning theory & optimization and data management & big data. 29 papers since 2023, 22 at top venues, mostly ASPLOS and PACMPL. 9 PhD students and 4 master's students. 9 alumni. Applications close .
At a glance
- papers, 2023–2026
- 8/ year
- at T1 venues
- 76%
- PhD students
- 9
- MS students
- 4
- alumni listed
- 9
Papers by yearfrom the lab homepage, 2023–2026
- 6 papers2026 · T1 2 · collecting
- 8 papers2025 · T1 8
- 8 papers2024 · T1 7
- 7 papers2023 · T1 5
T1 venuesOtherVenues: ASPLOS, PACMPL, MLSys, NeurIPS, SIGMOD
Research focuspaper titles that use each topic's words
- Learning theory & optimization6 papers
- Compilers & programming languages1 paper
- Data management & big data1 paper
- compilers0 papers
- workloads0 papers
Paper list
From the lab homepage.
20266 papers
- PACMPL(OOPSLA)Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions
- CGOGRANII : Selection and Ordering of Primitives in GRAph Neural Networks using Input Inspection (opens in a new tab)
- Conference on Language Modeling (COLM)Learning Reusable Program Transformations via LLM-Guided Rule Synthesis
- ISPASSMINISA: Minimal Instruction Set Architecture for Next-gen Reconfigurable Inference Accelerator
- EuroSys (T1)SAS: Sparse Attention Synthesizer for Efficient Language Model Inference (opens in a new tab)
- OSDI (T1)VTC: DNN Compilation with Virtual Tensors for Data Movement Elimination (opens in a new tab)
20258 papers
- PACMPL (T1)Automated Verification of Soundness of DNN Certifiers (opens in a new tab)
- ICML (T1)COGNATE: Acceleration of Sparse Tensor Programs on Emerging Hardware using Transfer Learning
- PACMPL (T1)GALA: A High Performance Graph Neural Network Acceleration LAnguage and Compiler (opens in a new tab)
- PLDI (T1)MISAAL: Synthesis-Based Automatic Generation of Efficient and Retargetable Semantics-Driven Optimizations (opens in a new tab)
- SIGMOD (T1)PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees (opens in a new tab)
- PACMPL (T1)SPLAT: A framework for optimised GPU code-generation for SParse reguLar ATtention (opens in a new tab)
- MICRO (T1)TAIDL: Tensor Accelerator ISA Definition Language with Auto-generation of Scalable Test Oracles (opens in a new tab)
- POPL (T1)TensorRight: Automated Verification of Tensor Graph Rewrites (opens in a new tab)
20248 papers
- MLSys (T1)COMET: X86 Cost Model Explanation Framework (opens in a new tab)
- Static Analysis SymposiumConstraintFlow: A DSL for Specification and Verification of Neural Network Analyses
- SIGMOD (T1)Dias: Dynamic Rewriting of Pandas Code (opens in a new tab)
- ASPLOS (T1)Hydride: A Retargetable and Extensible Synthesis-based Compiler for Modern Hardware Architectures (opens in a new tab)
- ASPLOS (T1)TGLite: A Lightweight Programming Framework for Continuous-Time Temporal Graph Neural Networks (opens in a new tab)
- SIGIR (T1)TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-Learning (opens in a new tab)
- CIKM (T1)Towards Efficient Temporal Graph Learning: Algorithms, Frameworks, and Tools (opens in a new tab)
- ASPLOS (T1)Two-Face: Combining Collective and One-Sided Accesses for Efficient Distributed SpMM (opens in a new tab)
20237 papers
- MetaComChallenges in Metaverse Research: An Internet of Things Perspective
- NeurIPS (T1)Learning Large Graph Property Prediction via Graph Segment Training (opens in a new tab)
- ISCA (T1)SPADE: A Flexible and Scalable Accelerator for SpMM and SDDMM (opens in a new tab)
- PPoPPTGOpt: Redundancy-Aware Optimizations for Temporal Graph Attention Networks (opens in a new tab)
- NeurIPS (T1)TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs (opens in a new tab)
- MLSys (T1)Unified Convolution Framework: A compiler-based approach to support sparse convolutions (opens in a new tab)
- ASPLOS (T1)WACO: Learning Workload-Aware Co-optimization of the Format and Schedule of a Sparse Tensor Program (opens in a new tab)
Where alumni went9 listed
Apply to UIUCFall 2027
UIUC Siebel Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL iBT / IELTS
- Fee
- USD 90
Not yet confirmed for this intake.
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