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Sophia Xing

Sophia Xing

Founder Plot Party, formerly Head of Product at Inworld AI

sophia-xing
San Francisco, California
Joined March 2025
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Summary

Sophia Xing is a serial innovator and founder, currently leading Plot Party, an AI-driven platform for visual storytelling. She aims to simplify the creation of cinematic content, leveraging AI to help users translate their imagination into film. nitter+1
With a strong background in AI product management, Sophia has held leadership roles at companies like Inworld AI and Forethought. She specialized in developing AI chatbot software for customer service, focusing on machine learning applications to enhance operational efficiency and user experience through NLP and computer vision. globeeawards+1
Sophia's academic foundation in economics, statistics, and machine learning from Princeton University (BA '15) and an MBA from MIT Sloan School of Management, where she graduated in the top 5%, underscores her deep analytical and technical capabilities. Her Princeton thesis explored sentiment analysis for stock market prediction. globeeawards+3
Her experience spans quantitative finance at Goldman Sachs, where she researched ML for investment strategies, to her work at Google. At MIT, she contributed to the Covid Analytics Task Force and developed an award-winning AR project, TalkAI, to aid public speaking. hackernoon
As an angel investor, Sophia Xing actively supports new ventures, demonstrating her commitment to fostering innovation within the startup ecosystem. rocketreach
She is an advocate for thoughtful AI development, emphasizing human-AI collaboration to optimize workflows and decision-making, rather than solely focusing on automation. She also openly discusses the unique challenges faced by female founders. hackernoon+1

Work

Education

Projects

Writing

SENTIMENT ANALYSIS OF NEWS ARTICLES: IMPLICATIONS FOR STOCK RETURNS PREDICTION AND MARKET TURBULENCE FORECAST

January 1, 2015

Models sentiment on both an individual stock level and a general market level using sentiment analysis methods like Latent Dirichlet Allocation and Gibbs sampling approximation. News article summaries are used to generate sentiment indices, which show statistically significant correlations with excess stock returns and market sentiment indices for predicting financial turbulence.

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