ABOUT THE TALK

Hazard / survival modeling is often under-applied given its broad use cases. For example, churn prediction is often posed as a classification problem (did churn or not), when the time component is often given short shrift (when, if ever did the churn happen?)

We hope to argue that hazard modeling is a better fit for these types of problems; spread general awareness of survival modeling, metrics, and data censoring; and describe how Opendoor uses these models to estimate our holding times for homes and mitigate risk, detailing scalability and other technical challenges we had to overcome.

Xinlu Huang

Data Scientist | Opendoor

Xinlu is a data scientists on the pricing and revenue optimization group at Opendoor in San Francisco. The team focuses on developing per-home liquidity estimation and building responsive pricing models.

Before joining Opendoor, Xinlu was a theoretical particle physicist at Stanford. She holds a Master of Music in piano performance from Peabody Conservatory and PhD in physics from Stanford.

Xinlu Huang
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