Tianqi Chen
Associate Professor
Builds machine learning systems and compilers for deploying models efficiently across hardware.
Tianqi Chen's lab at CMU works on accelerators, chips & architecture and efficient training & inference. 45 papers since 2023, 43 at top venues, mostly NeurIPS and ICLR. 24 PhD students and 24 master's students. 31 alumni. Applications close .
At a glance
- papers, 2023–2026
- 12/ year
- at T1 venues
- 96%
- PhD students
- 24
- MS students
- 24
- alumni listed
- 31
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 10 papers2025 · T1 10
- 27 papers2024 · T1 25
- 7 papers2023 · T1 7
T1 venuesOtherVenues: NeurIPS, ICLR, ASPLOS, ICML, MLSys
Research focuspaper titles that use each topic's words
- Efficient training & inference23 papers
- Accelerators, chips & architecture0 papers
- efficiently0 papers
- builds machine0 papers
- compilers0 papers
Paper list
From the lab homepage.
20261 paper
- EuroSys (T1)AdaServe: Accelerating Multi-SLO LLM Serving with SLO-Customized Speculative Decoding
202510 papers
- ICLR (T1)APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding
- MLSys (T1)FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
- ASPLOS (T1)GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
- ASPLOS (T1)Helix: Serving Large Language Models over Heterogeneous GPUs and Network via Max-Flow
- ICLR (T1)MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding
- ICLR (T1)MagicPIG: LSH Sampling for Efficient LLM Generation
- ICLR (T1)Memory Mosaics
- ASPLOS (T1)Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
- MLSys (T1)XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models
- ICLR (T1)Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity
202427 papers
- NeurIPS (T1)(Spotlight)
- MLSys (T1)ACROBAT: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time
- MLSys (T1)Atom: Low-bit Quantization for Efficient and Accurate LLM Serving
- ICML (T1)Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt
- ICLR (T1)Efficient Streaming Language Models with Attention Sinks
- NeurIPS (T1)Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding
- ICML (T1)Galore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
- ICML (T1)Get More with LESS: Synthesizing Recurrence with KV Cache Compression for Efficient LLM Inference
- ICML (T1)HexGen: Generative Inference of Large Language Model over Heterogeneous Environment
- ICLR (T1)Joma: Demystifying Multilayer Transformers via Joint Dynamics of MLP and Attention
- ICML (T1)KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache
- NeurIPS (T1)Laughing Hyena Distillery: Extracting Compact Recurrences from Convolutions
20237 papers
- OSDI (T1)EinNet: Optimizing Tensor Programs with Derivation-Based Transformations
- COLT (T1)Fast Algorithms for a New Relaxation of Optimal Transport
- NeurIPS (T1)H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
- NeurIPS (T1)Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer
- VLDB (T1)SDPipe: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel Training
- ASPLOS (T1)SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning
- ASPLOS (T1)TensorIR: An Abstraction for Automatic Tensorized Program Optimization
Where alumni went31 listed, 0% to industry
Industry 0Academia 4
Also to
- MIT
- UC Berkeley
- University of Helsinki
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
Similar labsnearest by research summary
- CNSJTU
Minyi Guo
- Efficient training & inference
- USUSC
Priyadarshini Panda
- Accelerators, chips & architecture
- USUT Austin
Diana Marculescu
- Accelerators, chips & architecture
- USUCSD
Bill Lin
- Accelerators, chips & architecture
- USGeorgia Tech
Yingyan Celine Lin
- Accelerators, chips & architecture
- SGNUS
Yao Lu
- Distributed, cloud & edge systems
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
CMUcloses