Bo Peng is a Senior AI Research Scientist with eight years of experience bridging academic research and production at top tech companies, currently at Meta after roles at Google and internships across Meta and AWS. He completed a Ph.D. in Computer Science at Ohio State focused on sequence modeling and language models applied to recommender systems and health informatics, and co-advised research that balances theory with real-world impact. He champions efficient model families (notably RWKV) and has contributed practical front-end work for AI-assisted Chinese novel generation, demonstrating both machine learning depth and product-minded implementation. Based in Mountain View, he combines rigorous research credentials with hands-on engineering across the ML stack, often translating novel sequence modeling ideas into usable tools and interfaces.
8 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at The Ohio State University
AI 写小说,生成玄幻和言情网文等等。中文预训练生成模型。采用我的 RWKV 模型,类似 GPT-2 。AI写作。RWKV for Chinese novel generation.
Role in this project:
Front-end Developer
Contributions:7 releases, 153 commits, 1 PR in 1 year 5 months
Contributions summary:Bo primarily contributed to the development and maintenance of the user interface for the AI-powered writing tool. Their work included updating the `index.html` file, which likely involved adding features, fixing bugs, and improving the overall user experience. They also implemented changes to the text area and log area, including adding options to control the length and randomness of generated text. The user demonstrated an understanding of HTML, CSS, and JavaScript in the context of an AI-powered writing tool.
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
Contributions:5 releases, 235 commits, 14 PRs in 1 year 5 months
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