Shiang Tang is a Ph.D. mathematician and mathematics intelligence expert with 11 years of experience bridging academic research and AI-driven math evaluation. After earning a Ph.D. in Number Theory from the University of Utah and publishing ten peer-reviewed papers, Shiang taught at UIUC, Purdue, and Dartmouth while transitioning to industry roles focused on creating and curating high-quality, verifiable math problems for model training. Currently improving Grok's mathematical reasoning, they excel at converting proof-based research content into objective, checkable tasks and consistently achieve top task scores in evaluation pipelines. Based in Salt Lake City, Shiang seeks industry roles on the U.S. West side and brings a rare combination of deep theoretical insight, proven teaching experience, and hands-on AI prompt engineering. An avid multitasker outside work, they pair mathematical rigor with creative problem rewrites that expose subtle model reasoning errors.
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