Biography

I am currently a tenure-track assistant professor jointly affiliated with the Institute for Artificial Intelligence and the School of Integrated Circuits at Peking University. Before joining Peking University, I was a staff research scientist and tech lead on Meta’s On-Device AI team, where I researched and productized efficient AI algorithms and hardware for next-generation AR/VR devices. I received my Ph.D. in Computer Engineering from the University of Texas at Austin under the supervision of Prof. David Z. Pan and my bachelor’s degree from Peking University under the supervision of Prof. Ru Huang and Prof. Runsheng Wang.

Research Focus

I develop efficient and privacy-preserving AI systems through algorithm–hardware co-design, with applications in large language models, embodied AI, and multimodal intelligence.

🔥 We are actively recruiting!

🔥 My group has multiple Postdoctoral positions available with a focus on Efficient and Secure AI Acceleration. We also welcome creative and self-motivated interns (undergraduate and Master’s students).

🔥 My group often has several PhD and master positions each year. We always prioritize students interning in the group. Please contact early.

🔥 If you are interested, please send me an email with subject “Prospective Student from [Your Institute]” along with your CV and transcripts.

Download my resumé.

Interests
  • Efficient and Secure Multi-Modality Artificial Intelligence
  • Algorithm/Hardware Co-Design/Co-Optimization
Education

  • PhD in Computer Engineering, 2018

    University of Texas at Austin, Austin, Tx, USA

  • MS in Computer Engineering, 2015

    University of Texas at Austin, Austin, Tx, USA

  • BS in Microelectronics, 2013

    Peking University, Beijing, China

Experience

 
 
 
 
 
Tenure-Track Assistant Professor
Peking University
Jul 2022 – Present Beijing
Institute for Artificial Intelligence & School of Integrated Circuits
 
 
 
 
 
Staff Research Scientist
Meta
Sep 2018 – Jul 2022 California
  • Progressed from Research Scientist to Senior and Staff Research Scientist
  • Tech Lead for on-device AI at Meta Reality Labs, focusing on efficient neural networks and hardware co-design for AR glasses
 
 
 
 
 
Research Intern (Summer 2016 & 2017)
Arm
May 2016 – Aug 2017 California
Hardware security and privacy-preserving AI
 
 
 
 
 
Research & Design Intern
Cadence Design System
May 2014 – Aug 2014 California
Static timing analysis acceleration

News

One Paper Accepted by CCS'2026
Last updated on Jul 19, 2026
Two Papers Accepted by MICRO 2026 and One Paper Accepted by ICCAD 2026
Last updated on Jul 12, 2026
Three Papers Accepted in ICML 2026
Last updated on May 3, 2026
Dr. Meng Li received ACM SIGDA Outstanding Young Faculty Award
Last updated on Apr 24, 2026
One Paper Accepted by OSDI'2026 and Two Collaboration Papers Accepted by ISCA'2026
Last updated on Mar 31, 2026
See all

Recent Talks

Efficient Private Transformer Inference through Network/Protocol Co-optimization
Discuss the recent NeurIPs'2024 paper from our lab on joint network/protocol optimization to enable efficient private Transformer inference.
Oct 19, 2024 11:00 AM — 12:00 PM
Video
Efficient Private Inference through Network/Protocol Co-optimization
Discuss the recent works from our lab on joint network/protocol optimization to enable more efficient privacy-preserving AI.
Jan 24, 2024 11:00 AM — 12:00 PM
Neural Acceleration with Full Stack Optimization
Discuss the recent works from our lab on full stack optimization to enable accurate yet efficient AI acceleration for multi-modal applications.
Jan 20, 2024 11:00 AM — 12:00 PM
Efficient Multi-Modal AI Acceleration
Discuss the recent works from our lab on full stack optimization to enable accurate yet efficient AI acceleration for multi-modal applications.
Jun 7, 2023 11:00 AM — 12:00 PM
Efficient Audio-Visual Understanding on AR Devices
Discuss full stack techniques from training, optimization, hardware, etc perspectives to enable efficient audio-visual understanding on AR devices
Nov 5, 2021 11:00 AM — 12:00 PM
See all events

Recent Publications

More detailed publication lists available through Google Scholar

Quickly discover relevant content by filtering publications.
Renjie Wei, Haochen Huang, Chuyu Qiu, Jinqi Wen, Dehao Xu, Meng Li (2026). MatMoE: Matryoshka Mixture-of-Experts with Dynamic Mixed-Precision Quantization for Efficient Inference. In International Conference on Computer-Aided Design (ICCAD) 2026.

Yi Chen, Ziyu Tang, Chao Yang, Guang Fan, Mingzhe Zhang, Meng Li (2026). Helios: Melting Kernel Boundaries for GPU-Accelerated HE via Graph Rewriting and Microarchitecture-Aware Mapping. In MICRO 2026.

Jiangrui Yu, Ye Yu, Si Chen, Chenqi Lin, Wenxuan Zeng, Junfeng Fan, Mingyu Gao, Meng Li (2026). OptiPrime: Optimizing Private Inference through protocol-hardware codesign. In MICRO 2026.

Tong Xie, Zuodong Zhang, Chao Yang, Yuan Wang, Runsheng Wang, Meng Li (2026). Aging Aware Adaptive Voltage Scaling for Reliable and Efficient AI Accelerators. In International Symposium of Electronic Design Automation (ISEDA) 2026.

Paper

Boyi Fu, Weikai Xu, Tong Xie, Jin Luo, Yaoyu Tao, Meng Li (2026). NICE: 3D-NAND-based In-Memory-Computing with In-Situ ECC-Protection for Fault-Tolerant and Efficient LLM Inference. In International Symposium of Electronic Design Automation (ISEDA) 2026.

Zizhuo Fu, Yifan Zhou, Zhaoxin Lu, Guangyu Sun, Runsheng Wang, Meng Li, Yibo Lin (2026). RePart: Efficient Hypergraph Partitioning with Logic Replication Optimization for Multi-FPGA System. In International Symposium of Electronic Design Automation (ISEDA) 2026.

Paper

Zizhuo Fu, Wenxuan Zeng, Runsheng Wang, Meng Li (2026). Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse. In International Conference on Machine Learning (ICML) 2026.

Paper

Shengxuan Qiu, Haochen Huang, Shuzhang Zhong, Pengfei Zuo, Meng Li (2026). HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction. In International Conference on Machine Learning (ICML) 2026.

Paper

Linye Wei, Zixiang Luo, Pingzhi Tang, Meng Li (2026). TEAM: Temporal–Spatial Consistency Guided Expert Activation for MoE Diffusion Language Model Acceleration. In International Conference on Machine Learning (ICML) 2026.

Paper

Shuzhang Zhong, Haochen Huang, Shengxuan Qiu, Pengfei Zuo, Runsheng Wang, Meng Li (2026). Breaking the Reward Barrier: Accelerating Tree-of-Thought Reasoning via Speculative Exploration. In USENIX Symposium on Operating Systems Design and Implementation (OSDI) 2026.

Paper

See all publications

Projects

More opensource projects available through github

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All Deep Learning Security Other
CryptoMoE: Privacy-Preserving and Scalable Mixture of Experts Inference via Balanced Expert Routing
Based on the Secretflow project developed by Ant Group, develop the first model/protocol co-optimization framework for private MoE LLM inference.
Code
MPCache: MPC-Friendly KV Cache Eviction for Efficient Private LLM Inference
Based on the Secretflow project developed by Ant Group, develop the first model/protocol co-optimization framework for static-dynamic sparse attention in LLM.
Paper Code
Lightmamba: Efficient mamba acceleration on fpga with quantization and hardware co-design
We developed and opensourced an end-to-end FPGA implementation for efficient Mamba LLM inference based on high level synthesis. Compared to GPU, LightMamba can achieve significant latency and power reduction.
Paper Code
HybriMoE: Hybrid CPU-GPU Scheduling and Cache Management for Efficient MoE Inference
Based on the KTransformers project, develop an adaptive scheduling framework that leverage the heterogeneous computation capability of CPU and GPU for efficient mixture-of-expert LLM inference.
Paper Code
AdapMoE: Adaptive Sensitivity-based Expert Gating and Management for Efficient MoE Inference
Develop an adaptive scheduling framework for efficient mixture-of-expert LLM inference on edge devices.
Paper Code
MPCViT: Searching for Accurate and Efficient MPC-friendly Vision Transformer with Heterogeneous Attention
Develop MPCViT that leverages neural architecture search to search MPC-friendly vision transformers.
Paper Code
NASViT: Neural Architecture Search for Efficient Vision Transformer with Gradient Conflict-Aware Supernet Training
Propose gradient conflict-aware training to improve supernet-based NAS and develop a family of optimized hybrid CNN/ViT networks that achieve state-of-the-art performance Pareto.
Paper Code
AlphaNet: Improved Training of Supernet with Alpha-Divergence
Develop AlphaNet to improve the supernet-based NAS with a more generalized alpha-divergence-based knowledge distillation and achieve state-of-the-art performance Pareto.
PDF Code
AttentiveNAS: Improving Neural Architecture Search via Attentive Sampling
Develop AttentiveNAS that focuses on improving the sampling strategy for supernet-based NAS to achieve state-of-the-art performance Pareto.
PDF Code

Accomplish­ments

  • ACM SIGDA Outstanding Young Faculty Award — ACM SIGDA, Apr 2026
  • Ant Group InTech Future Award — Ant Group, Sep 2025
  • On-Device Multi-modal Generative AI for Science Contest 1st Place — ACM SIGDA, Jun 2025
  • AICAS Grand Challenge on LLM Hardware System Design 1st Place — IEEE Circuit & System Society, Apr 2025
  • Young Teachers’ Teaching Skills Competition 1st Place Prize — Peking University, Dec 2024
  • CCF-Ant Group Research Award on Hardware/Software Co-Design — China Computer Federation (CCF), Aug 2024
  • CCF Integrated Circuits Early Career Award — China Computer Federation (CCF), Jul 2024
  • Secretflow Outstanding Industry-Academic Cooperation Contribution Award — Ant Group, May 2024
  • CCF-Ant Group Research Award on Privacy Computing — China Computer Federation (CCF), Aug 2023
  • Margarida Jacome Outstanding Dissertation Prize — University of Texas at Austin, May 2019
  • Outstanding Dissertations Award — European Design and Automation Association (EDAA), May 2019
  • Nominee of ACM Doctoral Dissertation Award — University of Texas at Austin, Mar 2019
  • First Place, Student Research Competition Grand Final (Graduate Category) — Aossication of Computing Machinery (ACM), Jun 2018
  • Best Paper Award — ACM Great Lake Symposium on VLSI (GLSVLSI), Mar 2018
  • Best Poster (Presentation) Award — ASPDAC Student Research Forum, Aossication of Computing Machinery (ACM) SIGDA, Feb 2018
  • Gold Medal — ICCAD Student Research Competition, Aossication of Computing Machinery (ACM) SIGDA, Nov 2017
  • Best Paper Award — IEEE International Symposium on Hardware Oriented Security and Trust (HOST), Jul 2017
  • Cockrell School Graduate Student Fellowship — University of Texas at Austin, Sep 2013
  • Yang Fuqing and Wang Yangyuan Academician Scholarship — Peking University, Sep 2011
  • Li Yanhong Baidu Scholarship — Peking University, Sep 2010

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