I am a researcher with a focus on high-dimensional statistical learning, large-scale distributed optimization, reproducible research, and data science.

I also have abundant knowledge on reinforcement learning, deep learning, operations research, economics, finance, sociology, and many other domains.

Meanwhile, I provide data science innovation consulting, designing for my clients tailored solutions to problems that cannot be solved with regular methods.

Get in touch with me if you want to know more about my research or are interested in my expertise.


Current Projects

  • Next-generation distributed optimization
  • Fundamental machine learning questions

Past Projects

Research Interests

  • Nonparametric statistics
  • First-order convex optimization
  • Distributed machine learning
  • Recommender systems
  • Differential privacy & fairness
  • Adaptive data analysis
  • Game theory

Reference Cards



A Distributed Frank–Wolfe Framework for Trace Norm Minimization via the Bulk Synchronous Parallel Model



  • Pokémon GO – using Nash Equlibrium to build minimalist PvE lists for Pokémon GO.
  • Neurons – a concise Python package implementing a Neural Network without hidden layers.
  • Breakout – a simple game on Windows, written in C++.
  • From 2048 to ABCD – a 2048-like game, open-source, written in C++.
  • Multithreading of C++ 11– a document testing C++11 multithreading technology.
  • Park – a Python package of PySpark offline testing environment.
  • Vocabulary Memory – an Excel file helping you memorize vocabulary. You can also customize it to memorize other things. Should enable the macro.
  • Fate_GrandOrder – an IPython notebook to help you visualize the stats growth curves in Fate / Grand Order.

Resources sponsored by GitHub