Curriculum Vitae
Machine Learning Engineer at Adobe building LLM-based agent orchestration systems for Adobe Experience Platform. Ph.D. in Computer Science with publications at top peer-reviewed venues (NAACL, ACL, EMNLP, COLING, CIKM, WSDM).
Work Experience
Machine Learning Engineer
Adobe Experience Platform, Agent Orchestrator
2025 – now
- Designed and built the Agent Executor, the core multi-agent orchestration engine serving tens of thousands of enterprise users, routing queries to 15+ specialized AI agents.
- Owned Response Synthesis end-to-end: design doc, LLM-based evaluation pipeline with human-LLM alignment study, production launch.
- Leading Context-Aware Agent Selection: analyzed production traffic revealing 49.2% misrouting, built ranking module covering 80.8% of interactions.
- Built Scalable Attachment Summarization and Multi-layer OOS Defense; migrated to Response API; implemented MLflow tracing with OpenTelemetry.
Machine Learning Engineer Intern
Adobe Experience Platform
2024
- Built MuRAR, a multi-modal question-answering assistant for Adobe Experience Platform.
- Won Best Demonstration Paper at COLING 2025; filed patent (D54114).
Lead Web Development Engineer
Department of CSE, University of Texas at Arlington
2020 – 2024
- Led a team building department web applications for Ph.D. management and faculty review; managed 100K+ database records.
Education
Ph.D., Computer Science
University of Texas at Arlington
2020 – 2025
Thesis: Understanding Misinformation on Social Media Through Truthfulness Stance.
Advisor: Dr. Chengkai Li
M.S., Computer Science
University of Texas at Arlington
2018 – 2020
B.S., Computer Science
Tianjin University of Finance and Economics
2014 – 2018
Publications
26
X. Shi, Z. Zhu, Z. Zhang, C. Li.
Hallucination Mitigation in Natural Language Generation from Large-Scale Open-Domain Knowledge Graphs. EMNLP, 2023.
15 Z. Zhu, D. Lee, H. Zhang, S.S. Harsha, L. Feujio, A. Maharaj, Y. Li.
MuRAR: A Simple and Effective Multimodal Retrieval and Answer Refinement Framework for Multimodal Question Answering. COLING, 2025.
11 Z. Zhu, K. Meng, J. Caraballo, I. Jaradat, X. Shi, Z. Zhang, F. Akrami, H. Liao, et al.
A Dashboard for Mitigating the COVID-19 Misinfodemic. EACL, 2021.
8 Z. Zhu, Z. Zhang, H. Zhang, C. Li.
RATSD: Retrieval Augmented Truthfulness Stance Detection from Social Media Posts toward Factual Claims. Findings of NAACL, 2025.
6
H. Zhang, Z. Zhu, Z. Zhang, J. Devasier, C. Li.
Granular Analysis of Social Media Users' Truthfulness Stances toward Climate Change Factual Claims. NLP Meets Climate Change (ClimateNLP), 2024.
5
H. Zhang, Z. Zhu, Z. Zhang, C. Li.
LLMTaxo: Leveraging Large Language Models for Constructing Taxonomy of Factual Claims from Social Media. Findings of ACL, 2025.
5 Z. Zhu, Z. Zhang, F. Patel, C. Li.
Detecting Stance of Tweets Toward Truthfulness of Factual Claims. Computation+Journalism Symposium, 2022.
3
Z. Zhang, Z. Zhu, H. Zhang, F. Patel, J. Caraballo, P. Hennecke, C. Li.
Wildfire: A Twitter Social Sensing Platform for Layperson. WSDM, 2024.
2 Z. Zhu, H. Zhang, Z. Zhang, C. Li.
TSD-CT: A Benchmark Dataset for Truthfulness Stance Detection. CIKM, 2025.
2
Z. Zhang, Z. Zhu, H. Zhang, C. Li.
Exploring Behavioral Tendencies on Social Media: A Perspective Through Claim Check-Worthiness. ASONAM, 2024.
1 Z. Zhu, H. Zhang, Z. Zhang, C. Li.
TrustMap: Mapping Truthfulness Stance of Social Media Posts on Factual Claims for Geographical Analysis. CIKM, 2025.
1 Z. Zhu.
Understanding Misinformation on Social Media through Truthfulness Stance. Ph.D. Dissertation, 2025.
Z. Zhu, Z. Zhang, H. Zhang, C. Li.
Detecting Stance of Social Media Posts Toward Truthfulness of Factual Claims for Social Goods. Preprint, 2024.
A.K.R. Madhusudhan, H. Zhang, Z. Zhu, Z. Zhang, J. Devasier, C. Li.
Evaluating Open Source LLMs for Claim Detection in Fact-Checking. Preprint, 2025.
Technical Skills
Languages Python, JavaScript/TypeScript, SQL, PHP, HTML/CSS, Cypher
ML/AI PyTorch, TensorFlow, HuggingFace, OpenAI API, LangChain, RAG, BERT, GPT
Systems Docker, Kubernetes, Azure, MLflow, OpenTelemetry, Neo4j, Git, FastAPI
Awards & Activities
2025 Best Demonstration Paper Award, COLING
2025 Featured in Adobe official blog: "Show, Don't Tell: Multimodal Answers in AI Assistant"
2025 Best Presentation & Demo Award, Student Computing Research Festival
2025 Invited speaker: Caltech Election Integrity Conference
2024 Patent filed: D54114 "Multimodal Retrieval and Answer Refinement Framework" (Adobe)
2024 Invited speaker: TACCSTER
2021–25 Reviewer: SIGMOD, NAACL, ACL, KDD, SIGIR, VLDB, WSDM, ICDE
NSF Contributor on grants #2346261 ($299K) and #2333834 ($1.5M)
