Agentic Product ML
Building practical assistant experiences that connect user intent, business context, and reliable actions.
Agentic AI / Probabilistic ML
Vincent Zheng
Applied ML Scientist at OpenTable working on Concierge agent experiences.
I build applied AI systems for dining discovery and assistance. My research connects LLMs, retrieval, ranking, probabilistic forecasting, and time-series foundation models, with a focus on using context reliably.
Focus
Current Themes
Retrieval and Ranking
Using search, embeddings, ranking, and evaluation to improve decision support in real products.
Probabilistic ML
Research background in uncertainty, structured prediction, and time-series foundation models.
Selected Work
Publications
Overcoming the Modality Gap in Context-Aided Forecasting
Introduces CAF-7M and DoubleCast for context-aided probabilistic forecasting with textual information.
Probabilistic Traffic Forecasting with Dynamic Regression
Extends error-correlation modeling to spatiotemporal forecasting with a matrix-variate autoregressive process and non-isotropic training loss.
Multivariate Probabilistic Time Series Forecasting with Correlated Errors
Introduces an efficient parameterization of cross-covariance matrices for multivariate probabilistic forecasting.
Projects
Applied AI and Research
OpenTable Concierge Agent
Applied ML for concierge-style dining assistance, connecting user intent, context, ranking, and reliable product actions.
DoubleCast
Context-aware prediction with CAF-7M, Chronos, Qwen3-14B, and DualT5 cross-attention.
Dynamic Regression
Structured spatiotemporal prediction with dynamic error-correlation modeling.