Biography

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

My research interests focus on efficient and secure multi-modality AI acceleration algorithms and hardwares.

🔥 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

News

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
Seven Papers Accepted by DAC'2026
Last updated on Feb 28, 2026
Zizhuo Won ACM/SIGDA SRC First Place
Last updated on Nov 5, 2025
See all

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

Experience:

  • 2018.09 - 2020.01 Research Scientist
  • 2020.01 - 2021.06 Senior Research Scientist
  • 2021.06 - 2022.07 Staff Research Scientist

Responsibilities include:

  • Tech Lead, On-Device AI, Meta Reality Lab
  • Efficient NN for AR Glasses
  • Efficient NN/HW Co-Design/Co-Optimization
 
 
 
 
 
Research Intern
arm
May 2017 – Aug 2017 California
Private AI
 
 
 
 
 
Research Intern
arm
May 2016 – Aug 2016 California
Hardware Security
 
 
 
 
 
Research & Design Intern
Cadence Design System
May 2014 – Aug 2014 California
Static timing analysis acceleration

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
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.
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.

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.

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.

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.

Tong Xie, Yijiahao Qi, Jinqi Wen, Zishen Wan, Yanchi Dong, Zihao Wang, Shaofei Cai, Yitao Liang, Tianyu Jia, Yuan Wang, Runsheng Wang, Meng Li (2026). CREATE: Cross-Layer Resilience Characterization and Optimization for Efficient yet Reliable Embodied AI Systems. In International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2026.

Jinqi Wen, Tong Xie, Runsheng Wang, Meng Li (2026). DRIFT: Harnessing Inherent Fault Tolerance for Efficient and Reliable Diffusion Model Inference. In Design Automation Conference (DAC) 2026.

Zebin Yang, Yijiahao Qi, Tong Xie, Bo Yu, Shaoshan Liu, Meng Li (2026). DySL-VLA: Efficient Vision-Language-Action Model Inference via Dynamic-Static Layer-Skipping for Robot Manipulation. In Design Automation Conference (DAC) 2026.

Xincheng Feng, Wenyong Zhou, Taiqiang Wu, Zhengwu Liu, Meng Li, Ngai Wong (2026). EdgeSC: Universal Stochastic Computing Architecture for Efficient Edge Detection. In Design Automation Conference (DAC) 2026.

Zebin Yang, Tong Xie, Baotong Lu, Shaoshan Liu, Bo Yu, Meng Li (2026). KEEP: A KV-Cache-Centric Memory Management System for Efficient Embodied Planning. In Design Automation Conference (DAC) 2026.

Weikai Xu, Meng Li, Shuzhang Zhong, Tianyang Luo, Dongxue Zhao, Ling Liang, Zongwei Wang, Qianqian Huang, Yimao Cai, Ru Huang (2026). NASiC: 3D NAND-based CAM-Selected Multibit CIM Architecture for Efficient On-Device Mixture-of-Experts LLM Inference. In Design Automation Conference (DAC) 2026.

See all publications

Projects

*
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.
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.
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.
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.
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.
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.
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.
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.
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.

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

Contact