Zhenduo Wang is an AI Engineer with a PhD in Computer Science from the University of Utah and eight years of experience applying NLP and information retrieval techniques to real-world problems. He has moved fluidly between academia and industry—publishing work on zero-shot clarifying question generation at WWW’23, aligning LLMs for physical-safety-aware conversational agents during a postdoc at Georgia Tech, and shipping applied ML features in internships at Microsoft, Roku, and American Family Insurance. Now at Growth Protocol he focuses on neuro-symbolic reasoning to help enterprises make strategic decisions, blending research rigor with product impact. His background in computational and applied mathematics and early experience in game development give him a pragmatic, systems-oriented approach to model design and evaluation. Colleagues describe him as someone who pairs deep technical fluency in SFT/PPO and retrieval with a knack for turning datasets into robust alignment outcomes. Based in Salt Lake City, he brings both research-first thinking and hands-on engineering to build safer, more useful conversational AI.
8 years of coding experience
2 years of employment as a software developer
The University of Utah
Master's degree Computational and Applied Mathematics, Master's degree Computational and Applied Mathematics at University of Minnesota Duluth
Senior High, Senior High at Dalian No.8 Senior High School
Bachelor of Engineering (BE) Computer Software Engineering, Bachelor of Engineering (BE) Computer Software Engineering at Dalian University of Technology
A conversational QA system consisting of a pipeline of models.
Contributions:1 review, 149 commits, 3 PRs in 2 years 7 months
nlppipelinebertdeep-learningconversational
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