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.
Find Xinlu Huang at
Talk : Liquidity Modeling in Real Estate Using Survival Analysis
Other talks from track AI Meets the Physical World
Tracks
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Deep Learning Applications & Practices
Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.
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Predictive Data Pipelines & Architectures
Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.
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ML in Action
Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud
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Real-world Data Engineering
Showcasing DataEng tech and highlighting the strengths of each in real-world applications.
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Handling Sequential Data Like an Expert / ML Applied to Operations
Discussing the complexities of time (half track) and Machine Learning in the data center (half track). Exploring topics from hyper loglog to predictive auto-scaling in each of two half-day tracks.
Half-day tracks -
AI Meets the Physical World
The track where AI touches the physical world. AI use cases around drones, self-driving cars, ROS, NVidia Jetson, & Amazon Deep Lens.