Allen Tran

I'm a Data Scientist at Netflix where I work on problems in content programming and valuation using ML, causal inference, NLP, etc.

I have interests in using deep learning and RL for sequence learning and NLP, as well as applying machine learning and causal models to non traditional areas such as law.

• inferring the long-term causal effects of long-term treatments from short-term experiments (draft)
• beyond customer lifetime valuation: measuring the value of acquisition and retention for subscription services (WebConf 2022)
• deep learning for driving detection on mobile phones (KDD 2016)
• causal infererence on observational studies @ Hulu
• fed translator - predict paths of fedfunds rate from fed statements/minutes
• graph2vec - learn vectors for nodes in graphs
• use all the data in forecasting - probabilistic PCA with missing data
• traffic speeds by zipcode
• uncovering the structure of the US economy via production/consumption networks
• propagating recommendations via the social graph

Slides on various papers (other authors):

• Memory Networks (Facebook AI Research)
• Deep Neural Decision Forests
• AlphaGo (DeepMind)

Fun proofs (I wish they were my proofs, they are just my notes!)

• Dinitz / Graph Coloring Proof from "The Book"
• 6 proofs of the Infinitude of Primes from "The Book"

You can also find my research from my past life as an Econ PhD at UCLA.