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Designed and implemented a collaborative-filtering–based user embedding framework with streaming updates to mitigate cold start, enabling effective use in large-scale recommendation and advertising models.
2021-2022Designed and implemented a channel recommendation model to identify the most relevant YouTube channels for a brand based on campaign keywords and URL-derived context. The approach leveraged shared embedding spaces and novel clustering techniques to account for multimodal channel content, paired with a two-stage ranking system optimized for real-time querying at scale. This system reduced channel selection time by ~90%, reproduced expert decisions with >99% precision, and was patented.
2018-2021Go Space is a program / model that attempts to embed similar Tseumego close to each other. The ultimate goal is to use this as a study tool: As the user solves problems, we can explore / exploit to build a map for that user to identify which parts of the go space the user has difficulty. We can then serve problems from difficult regions until the user gets better. Today, the UI has not yet been built, but a V1 model has been built, and there is some evidence that it works well.
2022Link