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Rising Stars
2025

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Rising Stars

D-Index
40
Citations
9229
World Ranking
642
National Ranking
95

Engineering and Technology

D-Index
36
Citations
8584
World Ranking
8569
National Ranking
2366

Yuanzhi Li publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Yuanzhi Li sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 134 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 117 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 59 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 93 publications — 6th percentile

6% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Yuanzhi Li D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Yuanzhi Li sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 128 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 349 scientists 41 D-Index: 362 scientists 42 D-Index: 425 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 94 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 24 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 36 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Yuanzhi Li is affiliated with Carnegie Mellon University in the United States and has a research focus primarily within the field of Computer Science, with a particular emphasis on Artificial Intelligence. Their work spans several subfields including Computer Vision and Pattern Recognition, Statistics and Probability, Statistical and Nonlinear Physics, and Information Systems.

Their research topics cover a range of areas in machine learning and AI techniques, featuring:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Stochastic Gradient Optimization Techniques
  • Domain Adaptation and Few-Shot Learning
  • Generative Adversarial Networks and Image Synthesis
  • Neural Networks and Applications
  • Machine Learning and Algorithms

Li has contributed to a variety of recent research papers including:

  • Textbooks Are All You Need II: phi-1.5 technical report, 2023, arXiv (Cornell University)
  • Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone, 2024, arXiv (Cornell University)
  • Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning, 2020, arXiv (Cornell University)
  • Learning and generalization in overparameterized neural networks, going beyond two layers, 2025, arXiv (Cornell University)
  • LoRA: Low-Rank Adaptation of Large Language Models, 2021, arXiv (Cornell University)

The venues where Yuanzhi Li publishes reflect a strong presence in open-access platforms, predominantly the arXiv repository affiliated with Cornell University, where they have over sixty publications. Other publication venues include the SSRN Electronic Journal, Electronic Journal of Statistics, Proceedings of the 44th International Conference on Software Engineering, and Mathematical Programming.

Collaborative efforts feature several frequent coauthors, including:

  • Zeyuan Allen-Zhu
  • Dhruv Malik
  • Quanquan Gu
  • Samy Jelassi
  • Sébastien Bubeck

Yuanzhi Li's research output highlights extensive engagement with topics in deep learning and neural network architectures, participating in advancing understanding and technical developments in ensemble methods, knowledge distillation, and adaptation of large language models.

Best Publications

  • LoRA: Low-Rank Adaptation of Large Language Models.

    Edward J. Hu;Yelong Shen;Phillip Wallis;Zeyuan Allen-Zhu

  • A Convergence Theory for Deep Learning via Over-Parameterization

    Zeyuan Allen-Zhu;Yuanzhi Li;Zhao Song

  • Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers

    Zeyuan Allen-Zhu;Yuanzhi Li;Yingyu Liang

  • Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data

    Yuanzhi Li;Yingyu Liang

  • Convergence Analysis of Two-layer Neural Networks with ReLU Activation

    Yuanzhi Li;Yang Yuan

  • A Latent Variable Model Approach to PMI-based Word Embeddings

    Sanjeev Arora;Yuanzhi Li;Yingyu Liang;Tengyu Ma

  • Linear Algebraic Structure of Word Senses, with Applications to Polysemy

    Sanjeev Arora;Yuanzhi Li;Yingyu Liang;Tengyu Ma

  • An Alternative View: When Does SGD Escape Local Minima?

    Robert Kleinberg;Yuanzhi Li;Yang Yuan

  • Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations

    Yuanzhi Li;Tengyu Ma;Hongyang Zhang

  • Towards Understanding Ensemble, Knowledge Distillation and Self-Distillation in Deep Learning.

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks

    Yuanzhi Li;Colin Wei;Tengyu Ma

  • Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees.

    Yuping Luo;Huazhe Xu;Yuanzhi Li;Yuandong Tian

  • On the Convergence Rate of Training Recurrent Neural Networks

    Zeyuan Allen-Zhu;Yuanzhi Li;Zhao Song

  • What Can ResNet Learn Efficiently, Going Beyond Kernels?

    Zeyuan Allen-Zhu;Yuanzhi Li

  • NEON2: Finding Local Minima via First-Order Oracles

    Zeyuan Allen-Zhu;Yuanzhi Li

  • LazySVD: Even Faster SVD Decomposition Yet Without Agonizing Pain

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Feature Purification: How Adversarial Training Performs Robust Deep Learning

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Much Faster Algorithms for Matrix Scaling

    Zeyuan Allen-Zhu;Yuanzhi Li;Rafael Oliveira;Avi Wigderson

  • First Efficient Convergence for Streaming k-PCA: A Global, Gap-Free, and Near-Optimal Rate

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Physics of Language Models: Part 3.1, Knowledge Storage and Extraction

    Unknown

  • Backward Feature Correction: How Deep Learning Performs Deep Learning

    Zeyuan Allen-Zhu;Yuanzhi Li

  • Even Faster SVD Decomposition Yet Without Agonizing Pain

    Zeyuan Allen Zhu;Yuanzhi Li

Frequent Co-Authors

Zeyuan Allen-Zhu
Zeyuan Allen-Zhu Meta Platforms, Inc.
Sébastien Bubeck
Sébastien Bubeck Microsoft (United States)
Yingyu Liang
Yingyu Liang University of Wisconsin–Madison
Tengyu Ma
Tengyu Ma Stanford University
Yin Tat Lee
Yin Tat Lee Microsoft (United States)
Sanjeev Arora
Sanjeev Arora Princeton University
Elad Hazan
Elad Hazan Princeton University
Aaron Sidford
Aaron Sidford Stanford University
Avi Wigderson
Avi Wigderson Institute for Advanced Study
Zhao Song
Zhao Song Adobe Systems (United States)

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