# Zheng Zhang

**AWS Shanghai AI Lab**
Email: [[e-mail]](mailto\:[e-mail]), [[e-mail]](mailto\:[e-mail]), [[e-mail]](mailto\:[e-mail])
Website: [https://zzhang-cn.github.io/](https://zzhang-cn.github.io/)
Phone: [phone]

## Education

- **1984-1987**: B.S., Electronical Engineering, Fudan University, Shanghai, P.R.C.
- **1990-1992**: M.S., Electrical and Computer Engineering, University of Texas at Dallas
- **1993-1996**: Ph.D., Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

## Research Interests
- Forward-looking fundamental research on Large Language Models, focusing on their design, computational properties, applications in real-world contexts, and transformative potential in scientific discovery.
- Development of practical and innovative frameworks to detect, diagnose, and mitigate hallucinations and reasoning limitations in LLMs, applicable across diverse modalities.
- Addressing educational challenges in an AI-driven future by fostering Renaissance-like thinking, achieving exceptional performance with AI tools, and strengthening core abilities without tool dependence—ultimately empowering individuals to surpass pre-AI capabilities. Developed [**LLM4LLM** Project](https://github.com/zzhang-cn/LLM4LLM/), an innovative curriculum using LLMs to explain their own mechanisms through interactive learning. [Recent talk](https://news.qq.com/rain/a/20250125A03RV300?uid%5B0%5D=100130518884&uid%5B1%5D=100130518884&suid=&media_id=)

## Professional Experience

### AWS Shanghai AI Lab (2018-Present)
- **Senior Principal Scientist and Lab Director** (Founding Director), built the lab from ground up with focus on machine learning/AI systems, algorithms and applications
- Led the lab to strong research and industry impact:
- Built and mentored research teams achieving \~100 publications in top-tier venues
- Established research directions leading to significant technology transfers to AWS services
- Founded and led development of [DGL](https://www.dgl.ai/) (Deep Graph Library): Leading open-source graph neural network platform, widely adopted in academia and industry
- Built robust research pipeline through internship program (~200 interns to date, with strong placement record)
- Established innovative industry-academia bridge through Consulting Professor Program
- Successfully incubated and built two additional high-performing AWS teams

### NYU Shanghai (2014-2018)

- Tenured **Global Network Professor** of Computer Science
- Founder and advisor to the open-source project [MXNet](https://mxnet.apache.org/versions/1.9.1/), later adopted by Amazon as its official ML platform
- Pioneered cross-disciplinary research: Initiated collaboration between neuroscience and machine learning for natural language understanding
- Redesigned core computer science curriculum: Developed "Introduction to Computer Science" from ground up, integrating history, core algorithms, machine learning basics, and hands-on system building in one course.

### Microsoft Research Asia (2002-2014)

- **Principal Researcher**. Founded and led System Research Group, growing it into a leading systems research organization in Asia
- Member of Senior Leadership Team, shaping research strategy and direction of MSRA
- Built research communities that bridged Asia and global systems research:
- Founded SIGOPS APSys Workshop, now a premier systems venue in Asia-Pacific
- Established ChinaSys community, connecting systems researchers across institutions
- Recognized with multiple Microsoft Gold Star and Achievement Awards for research excellence and impact

## Publications

[Google Scholar Profile](https://scholar.google.com.hk/citations?user=k0KiE4wAAAAJ\&hl=en) | Citations: 19,209 | h-index: 64 | i10-index: 146 (Since 2020: Citations: 10,375 h-index: 37, i10-index: 75)

### Selected Works

#### Open-source frameworks for scalable machine learning and graph machine learning.

Initiated popular scalable machine learning and graph neural network framework through robust, open-source platforms (\~2.9K and \~1.9K citations, for MXNet and DGL, respectively).

- Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, Zheng Zhang. **Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks**. arXiv:1909.01315. Sep 2019
- Chen T, Li M, Li Y, Lin M, Wang N, Wang M, Xiao T, Xu B, Zhang C, Zhang Z. **Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems**. arXiv:1512.01274. Dec 2015.

#### Large Language Models

Rethinking LLMs as computing devices and addressing their key limitations; mechanisms and benchmarks to perform hallucination detection.

- Zhang Z. **Comprehension Without Competence: Architectural Limits of LLMs in Symbolic Computation and Reasoning**. TMLR, Nov 2025.
- Ru D, Qiu L, Hu X, Zhang T, Shi P, Chang S, Jiayang C, Wang C, Sun S, Li H, Zhang Z. **Ragchecker: A fine-grained framework for diagnosing retrieval-augmented generation**. *NeurIPS 2024 D&B Track*. 2024 Aug 15.
- Hu X, Ru D, Qiu L, Guo Q, Zhang T, Xu Y, Luo Y, Liu P, Zhang Y, Zhang Z. **RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models**. *EMNLP 2024*.

#### Graph Neural Networks, Algorithms and Applications

GNN algorithms and system co-design, application and benchmarking of GNN for relational data, and applications in text and multi-agent trajectory prediction.

- Huang K, Jiang H, Wang M, Xiao G, Wipf D, Song X, Gan Q, Huang Z, Zhai J, Zhang Z. **FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training**. VLDB Endowment. 2024 Feb 1;17(6):1473-86.
- Wang M, Gan Q, Wipf D, Cai Z, Li N, Tang J, Zhang Z, et al. **4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs**. *arXiv:2404.18209*. 28 Apr 2024.
- Guo Q, Jin Z, Wang Z, Qiu X, Zhang W, Zhu J, Zhang Z, Wipf D. **Fork or fail: Cycle-consistent training with many-to-one mappings**. *AISTATS*. 2021 Mar; pp. 1828-36.
- Guo Q, Jin Z, Qiu X, Zhang W, Wipf D, Zhang Z. **Cyclegt: Unsupervised graph-to-text and text-to-graph generation via cycle training**. *arXiv:2006.04702*. 2020 Jun 8.
- Li L, Yao J, Wenliang L, et al. **Grin: Generative relation and intention network for multi-agent trajectory prediction**. *NeurIPS*. 2021 Dec;34:27107-18.

#### Transformer Architecture Optimizations

Early solutions for addressing long-range context challenges, influencing subsequent innovations in attention mechanisms.
- Guo Q, Qiu X, Liu P, Shao Y, Xue X, Zhang Z. **Star-transformer**. *NeurIPS 2019*. 2019 Feb 25.
- Ye Z, Guo Q, Gan Q, Qiu X, Zhang Z. **Bp-transformer: Modelling long-range context via binary partitioning**. *ICLR 2020*. 2019 Nov 11.

#### Core Computer Vision Problems

Research key challenges in computer vision, object-centric learning and sequential image attention.

- Seitzer M, Horn M, Zadaianchuk A, et al. **Bridging the gap to real-world object-centric learning**. *ICLR* 2023.
- Welleck S, Mao J, Cho K, Zhang Z. **Saliency-based sequential image attention with multiset prediction**. *NeurIPS*. 2017
- Xiao T, Zhang J, Yang K, Peng Y, Zhang Z. **Error-driven incremental learning in deep convolutional neural network for large-scale image classification**. *ACM Multimedia*. 2014 Nov; pp. 177-86.
- Xiao T, Xu Y, Yang K, Zhang J, Peng Y, Zhang Z. **The application of two-level attention models in deep convolutional neural network for fine-grained image classification**. *CVPR*. 2015; pp. 842-50.

## Grants and Research Support

- **Major Program Grant (2015-2019)**: Science and Technology Commission Shanghai Municipal (15JC140014), Joint research of brain mechanism of language processing and computational modeling in artificial intelligence, 5M RMB, PI, project initiator and coordinator.
- **Research Grant (2015-2018)** Science and Technology Commission Shanghai Municipal (15XD1503000), cross-disciplinary research of cognitive process in bio-system and artificial intelligence, 400K RMB, PI.

## Awards and Honors

### Major Recognition

- China's National "Thousand Talent Program" recipient (2016)

### Best Paper Awards

- Best Paper, Eurosys (2012)
- Best Student Paper, Eurosys (2008)
- Best Paper, USENIX LISA (2003)

### Industry Achievement Awards

- Excellence in Collaboration, Cloud and China Enterprise, Microsoft (2014)
- Microsoft 10 Year's Achievement Award (2012)
- Microsoft Gold Star Award (2005, 2007)
- HP Diamond Award (2001)
- HP Star Award (1997)

## Professional Service

### Leadership in Research Communities

- **Founder and Steering Committee Member**, SIGOPS APSys (2009-2014)
- **Founder**, ChinaSys (cross-institute systems research community in China)
- **General Co-Chair**, ACM SIGOPS APSys Workshop 2011
- **PC Co-Chair**, ACM SIGOPS APSys Workshop 2013

### Program Committee Service

- ACM Eurosys 2017
- ACM OSDI 2016
- ACM/USENIX Middleware 2014
- USENIX FAST 2014

### Conference Organization

- **Organizer**, AI-Brain Workshop, NYU Shanghai (2016)

### Journal and Grant Reviews

- **Invited Expert Reviewer**, National Science Foundation of China (NSFC) (2004-2005)

## Patents
46 US Patent awards total