Qixin Wang Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
unknown
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Qixin Wang's research focuses on the development and application of artificial intelligence, particularly large language models (LLMs) and large vision models (LVMS), for enhancing user interaction and system performance. Recent work includes "AdaptJobRec," an LLM-powered agentic system designed to improve conversational career recommendations. Wang also contributed to a hybrid LVMS-powered GUI agent for the Walmart Myassistant application, aiming to streamline user experience. Further research explores "SplitE," a method for enhancing knowledge graph embedding precision through entity splitting and contextualization, and a practical universal designated verifier transitive signature proof scheme for graph-based data systems. Wang has published six papers and has an h-index of 1, with total citations of 7.
Metrics
- h-index: 1
- Publications: 6
- Citations: 8
Selected Publications
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AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System (2026)
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SplitE: Enhancing Knowledge Graph Embedding Precision with Entity Split and Contextualization (2025)
Collaboration Network
Top Collaborators
- SplitE: Enhancing Knowledge Graph Embedding Precision with Entity Split and Contextualization
- AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System
- A Hybrid Large Vision Model Powered GUI Agent for Walmart Myassistant Application
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System
- A Hybrid Large Vision Model Powered GUI Agent for Walmart Myassistant Application
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- SplitE: Enhancing Knowledge Graph Embedding Precision with Entity Split and Contextualization
- A Hybrid Large Vision Model Powered GUI Agent for Walmart Myassistant Application
- SplitE: Enhancing Knowledge Graph Embedding Precision with Entity Split and Contextualization
- AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System
- AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- A Hybrid Large Vision Model Powered GUI Agent for Walmart Myassistant Application
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- A Hybrid Large Vision Model Powered GUI Agent for Walmart Myassistant Application
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- A Hybrid Large Vision Model Powered GUI Agent for Walmart Myassistant Application
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- A Hybrid Large Vision Model Powered GUI Agent for Walmart Myassistant Application
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- A Hybrid Large Vision Model Powered GUI Agent for Walmart Myassistant Application
- AdaptJobRec: Enhancing Conversational Career Recommendation Through an LLM-Powered Agentic System
- SplitE: Enhancing Knowledge Graph Embedding Precision with Entity Split and Contextualization
- SplitE: Enhancing Knowledge Graph Embedding Precision with Entity Split and Contextualization
- SplitE: Enhancing Knowledge Graph Embedding Precision with Entity Split and Contextualization
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