Scholar

Boyao Wang

Google Scholar ID: eqoNDrkAAAAJ

Carnegie Mellon University

Efficient AI

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Publications

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2025

Cited

Resume (English only)

Academic Achievements

1. Evaluated performance on LLaMA-3.1-8B, Qwen2.5-7B, and Gemma-2-9B, demonstrated 5% improvement in common sense benchmark scores over state-of-the-art methods including SliceGPT at 50% sparsity
2. Compressed LLaMA-3.2-3B to 1.3B and post-trained on 0.05B tokens, outperforming TinyLlama-1.1 pretrained on 10B tokens, demonstrating structured pruning efficiently generates smaller models with far less computational resources
3. Developed an end-to-end approach to compress CNNs using structured pruning and Q-learning, achieving 81% parameter reduction on VGG-19 (CIFAR-100) within 1% performance drop

Research Experience

1. Adapt-Pruner: Adaptive and Structured Pruning for Efficient LLMs (Co-First Author), Preprint, Supervisor: Tong Zhang
2. RL-Pruner: Structured Pruning Using Reinforcement Learning for CNNs (First Author), Preprint, Source Code, Supervisor: Volodymyr Kindratenko

Education

1. Carnegie Mellon University, Dec. 2026, Master of Science in Machine Learning, Incoming Student
2. University of Illinois Urbana-Champaign, Jun. 2025, Bachelor of Science in Computer Engineering, Highest Honors, GPA: 3.94/4.00
3. Zhejiang University, Jun. 2025, Bachelor of Engineering in Electronic and Computer Engineering, GPA: 3.97/4.00
4. Stanford University, Jun. 2024 - Aug. 2024, Non-Degree, Undergraduate Summer Visitor, GPA: 4.075/4.30

Background

Currently pursuing a Master's degree in Machine Learning at Carnegie Mellon University, seeking Machine Learning Engineer and Applied Research Internship opportunities for Summer 2026. Earned a Bachelor of Science in Computer Engineering from the University of Illinois Urbana-Champaign (Highest Honors) and a Bachelor of Engineering in Electronic and Computer Engineering from Zhejiang University (Outstanding Graduate). Conducted research under the guidance of Professor Volodymyr Kindratenko and worked in Professor Tong Zhang’s lab, focusing on compressing LLMs and their post-training.

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