Kai Zhao, Ph.D.
Research Scientist / MLE, Workday AI
Associate Editor, Electronic Commerce Research (Springer Nature)
Email: kaizhaofrank@gmail.com
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Short bio:
17+ years of research experience in Machine Learning and Artificial Intelligence.
50+ top-tier ML publications (ICML, Neurips, KDD, EMNLP, AAAI, IJCAI, TKDE, TVCG, TCSS, etc.) with over 1200 citations
20+ ML projects as tech leaders with Uber, Walmart, Workday, etc., contributing to multi-million-dollar revenue impact
Working Experience
Research Scientist / MLE, Agentic AI, Workday, CA 2024-
Research Scientist / MLE, Agentic AI, Walmart AI Lab, CA 2024-2025
Assistant Professor, AI/ML, Georgia State University, GA 2017-2024
Post-doc, AI/ML, New York University, NY 2015-2017
Education
Ph.D., Computer Science, University of Helsinki, Finland 2015
BSc., Computer Science, Shandong University, China 2009
Selected Publications 2023-2025 (*: with Industry AI team)
Google Scholar – citations: 1,200 , h-index: 14
Foundation Model:
RecSys 25 Oral (Top 3%)* VL-CLIP: Enhancing Multimodal Recommendations via Visual Grounding and LLM-Augmented CLIP Embeddings
RecSys 25 Oral (Top 3%)* GRACE: Generative Recommendation via Journey-Aware Sparse Attention on Chain-of-Thought Tokenization
Neurips 25* Spatial Reasoning in Foundation Models: Benchmarking Object-Centric Spatial Understanding
ICML 25 Cross-City Latent Space Alignment for Consistency Region Embedding
KDD 25 SILO: Semantic Integration for Location Prediction with Large Language Models
AAAI 24 Urban Region Embedding via Multi-View Contrastive Prediction
IJCAI 24 Exploring Urban Semantics: A Multimodal Model for POI Semantic Annotation with Street View Images and Place Names
IJICAI 24 Learning Hierarchy-Enhanced POI Category Representations Using Disentangled Mobility Sequences
TKDE 23 Beyond The Limits of Predictability in Human Mobility Prediction: Context-Transition Predictability
IJCAI 23 Toward an Integrated View of Semantic Annotation for POIs with Spatial and Textual Information
AI Agents:
ICML 25* CARTS: Collaborative Agents for Recommendation Textual Summarization
Neurips 25* LayoutAgent: A Vision-Language Agent Guided Compositional Diffusion for Layout Planning
Neurips 25* MetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems
Neurips 25* No-Human in the Loop: Agentic Evaluation at Scale for Recommendation
Neurips 25* MetaSynth: Multi-Agent Metadata Generation from Implicit Feedback in Black-Box Systems
SIGIR 25* CAL-RAG: Retrieval-Augmented Multi-Agent Generation for Content-Aware Layout Design
SIGIR 25* ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation
TKDE 24 Human-AI Interaction: Human Behavior Routineness Shapes AI Performance
EMNLP 23 Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning
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