knowledge/profile.md

# Prof. Liang Zhao — Profile

## Brief description (at a glance)
Dr. Liang Zhao is an award-winning AI researcher at Emory University who builds
**scalable and trustworthy machine learning for structured, spatial, and scientific
problems**. Over more than a decade his work has spanned a remarkable arc — from the
deployed **EMBERS** civil-unrest forecasting system, through foundational work on
**graph neural networks and deep graph generation**, to today's frontier of
**retrieval-augmented and agentic large language models**, **AI for science**
(molecules, epidemiology, power grids), and **LLM efficiency**. He is among
**Stanford's "Top 2%" most-cited scientists**, with **~12,200 citations, an h-index of
52, and an i10-index of 213**, and his research is funded by **NSF (including a CAREER
Award), NIH**, and industry partners such as **Amazon, Meta, and NVIDIA**. His guiding
philosophy: *"Whatever science domain I work with, I abstract the core problem into
mathematical language."*

## Who he is
Dr. Liang Zhao is a **Winship Distinguished Research Professor** and **Associate
Professor** in the **Department of Computer Science at Emory University**, with a
joint appointment in the **Cell & Molecular Biology Division of the Winship Cancer
Institute**. He earned his **Ph.D. from Virginia Tech in 2017**. He has authored
**200+ peer-reviewed papers**, with roughly **12,000 citations, an h-index of 52,
and an i10-index of 212** (Google Scholar). His work is supported by **NSF, NIH**,
and industry partners including **Amazon, Meta, and NVIDIA**.

Homepage: https://cs.emory.edu/~lzhao41/
Google Scholar: https://scholar.google.com/citations?user=qnvyqtwAAAAJ&hl=en

## Research mission
He builds **scalable and trustworthy machine learning for structured, spatial, and
scientific domains**. A recurring theme in his own words: "Whatever science domain
I work with, I abstract the core problem into mathematical language."

### Core research areas
- **Graph neural networks & graph learning** — foundations, deep graph generation,
  graph transformation, text-attributed graphs, distributed/efficient GNN training,
  spectral–spatial unification, GNN explainability.
- **Geospatial & spatio-temporal reasoning** — spatial event forecasting, trajectory
  generation, polyhedra/3D geometry representation learning, spatial RAG and
  spatial agents.
- **AI for science / scientific ML** — AI-guided molecule and disinfectant design,
  neurobiology simulation, wildfire-smoke modeling, tumor detection, MRI-based brain
  health, healthcare knowledge graphs.
- **Large language models** — retrieval-augmented generation (RAG), graph & citation
  RAG, agentic systems, domain specialization, uncertainty quantification, federated
  pruning, resource-efficient LLMs.
- **Trustworthy & efficient AI** — explainability, robustness, optimization for
  infrastructure and energy efficiency, LLM cost reduction.

## Research impact & reach
- **Metrics (Google Scholar):** ~12,200 total citations, h-index 52, i10-index 213;
  most citations are recent (since 2021), reflecting an accelerating research program.
- **Highly cited works** include the *Graph Neural Networks: Foundations, Frontiers,
  and Applications* book (1400+ citations), the *Domain Specialization of LLMs* survey,
  the *EMBERS* civil-unrest forecasting system, and *GRAG: Graph Retrieval-Augmented
  Generation*.
- **Frequent collaborators** span top institutions and labs: Chang-Tien Lu and Naren
  Ramakrishnan (Virginia Tech), Lingfei Wu, Feng Chen (UT Dallas), Jian Pei (Duke),
  Jieping Ye (Alibaba), Charu Aggarwal (IBM), Hui Xiong, and Lise Getoor (UC Santa
  Cruz), among others.
- **Newest directions (2026):** "world models" for science and decision-making —
  e.g. *Toward World Models for Epidemiology*, *MolWorld* for molecular optimization,
  and *LUMINA*, a grid foundation model for power-system optimization — plus
  self-maintaining **LLM-agent skill libraries** (*SkillOps*).

## Selected awards & recognition
- **2025**: KDD Test of Time Award; CIKM Best Paper Runner-up.
- **2024**: Stanford "Top 2% Scientists" ranking.
- **2023**: IEEE Middle-Career Award.
- **2022**: ICDM Best Paper Award; Meta Research Award.
- **2020**: NSF CAREER Award; Amazon Research Award.

## Current Ph.D. students and their topics
- **Dazhou Yu** (2021–): 3D geometry, spatial reasoning, polyhedra representation learning (PolyhedronNet; Spatial-RAG).
- **Yifei Zhang** (2022–): visual explanations, saliency analysis, active learning (Saliency-Bench).
- **Bo Pan** (2022–): graph neural networks, text-attributed graphs, GNN explanations (GraphNarrator).
- **Mengdan Zhu** (2023–): latent representation explanation, generative models (LatentExplainer).
- **Yuntong Hu** (2023–): RAG systems, citation networks, memory-augmented LLMs (GRAG, CG-RAG, RAG-without-Forgetting).
- **Zeeshan Memon** (2024–): information propagation, diffusion networks (propagation-tree identification).
- **Yang Qiao** (2024–).
- **Ziyuan Qin** (2024–, co-advised): biomedical informatics applications.
- **Xinyuan Song** (2024–).
- **Riyang Bao** (2025–): spatial reasoning, agentic systems (Spatial-Agent).
- **Yuzhu Mao** (2025–).
- **Ziyang Yu** (2025–).
- **Yuxiang Lai** (2026–, co-advised): biomedical applications.
- **Ken Su** (2026–).