Image by Dennis Kummer

Hi, I'm Han

Han Fang 2019.JPG

👨🏻‍💻 📊 🐶 🧬

Han Fang is a Data Scientist Manager at Facebook AI, with a main focus on AI integrity - develop AI technologies to make the online community a safer place. Han's research interests include multi-modal content understanding and natural language processing, and model interpretability and explainability. 

Han holds a PhD in Applied Mathematics and Statistics from Stony Brook University (2017). In his PhD, he developed a set of graphical and machine learning algorithms for large-scale genomics data (cited 1700+ times). He is a recipient of the President’s Award to Distinguished Doctoral Students, the Woo-Jong Kim Dissertation Award, and the Excellence in Research Award. 


Research @ Facebook


Supporting  economy recovery and vaccine distribution across the world

We develop the first micro-estimates of wealth and poverty that cover the populated surface of near 100 low and middle-income countries. Together with UC Berkeley, Facebook’s Data for Good team released the Relative Wealth Index data to support COVID-19 economic recovery and equitable vaccine distribution across the world using AI (paper).

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AI advances to better detect hate speech

We have a responsibility to keep the people on our platforms safe, and dealing with hate speech is one of the most complex and important components of this work. To better protect people, we have AI tools to quickly — and often proactively — detect this content.


Tetris Planner: Optimizing Facebook Data Warehouse Data Placement

Facebook's data warehouse has 9+ data centers around the world, which hosts exabytes of data for analytics and machine learning. Planner was deployed to all Facebook's data centers and successfully rebalancing petabytes of data daily.

Research from PhD

Cell Systems

Scikit-ribo enables accurate estimation and robust modeling of translation dynamics at codon resolution

Scikit-ribo, an open-source analysis package for accurate genome-wide A-site prediction and translation efficiency (TE) estimation from Ribo-seq and RNA sequencing data


Nature Protocols

Indel variant analysis of short-read sequencing data with Scalpel

Scalpel is an open-source software for reliable indel detection based on the microassembly technique

Contact me


Twitter: @han_fang_