Edwin Chen

Founder at Surge AI. We're a team of engineers and researchers from Google, Facebook, Harvard, and MIT building a modern data labeling platform and workforce for NLP.

Need obsessively high-quality human-powered data fast? Reach out! We help top AI companies like OpenAI, Amazon, and Airbnb create powreful human-labeled datasets to train and measure their AI.

Former AI & engineering lead at Google, Facebook, Twitter, Dropbox, and MSR. Pure math, theoretical CS, and linguistics at MIT.

Surge AI
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Recent Posts

A Visual, Layman's Introduction to Language Models in NLP

Surge AI: A New Data Labeling Platform and Workforce for NLP

Exploring LSTMs

Moving Beyond CTR: Better Recommendations Through Human Evaluation

Propensity Modeling, Causal Inference, and Discovering Drivers of Growth

Product Insights for Airbnb

Improving Twitter Search with Real-Time Human Computation

Edge Prediction in a Social Graph: My Solution to Facebook's User Recommendation Contest on Kaggle

Soda vs. Pop with Twitter

Infinite Mixture Models with Nonparametric Bayes and the Dirichlet Process

Instant Interactive Visualization with d3 + ggplot2

Movie Recommendations and More via MapReduce and Scalding

Quick Introduction to ggplot2

Introduction to Conditional Random Fields

Winning the Netflix Prize: A Summary

Stuff Harvard People Like

Information Transmission in a Social Network: Dissecting the Spread of a Quora Post

Introduction to Latent Dirichlet Allocation

Introduction to Restricted Boltzmann Machines

Topic Modeling the Sarah Palin Emails

Filtering for English Tweets: Unsupervised Language Detection on Twitter

Choosing a Machine Learning Classifier

Kickstarter Data Analysis: Success and Pricing

A Mathematical Introduction to Least Angle Regression

Introduction to Cointegration and Pairs Trading

Counting Clusters

Hacker News Analysis

Layman's Introduction to Measure Theory

Layman's Introduction to Random Forests

Netflix Prize Summary: Factorization Meets the Neighborhood

Netflix Prize Summary: Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights

Prime Numbers and the Riemann Zeta Function

Topological Combinatorics and the Evasiveness Conjecture

Item-to-Item Collaborative Filtering with Amazon's Recommendation System