Bio
Born and raised in Shanghai, China, Ron moved to Portland, Oregon with his parents at the age of nineteen. Five years later, he became a US citizen. He graduated from University of Oregon with a degree in Mathematics, then he moved to New York City to study grad level Math and Physics at New York University and Columbia University for 5 years.
His past experience includes front-end: web design (Javascript, Bootstrap, Phantom), back-end: database (Django, MySql), trading strategies (hedging, pricing), data mining (machine learning, computer vision, NLP). He is also volunteering for NYPD. In his spare time, he enjoys basketball and rock climbing.
Skills
Activities
Hackerrank Programming Competition
As of April 2017, I ranked 98.44 percentile among 1 million developers on Hackerrank.
Familiar with advanced algorithms and data structures, including runtime analysis, sorting, searching, graph algorithms (Dijkstra, Floyd-Warshall, minimum spanning tree), dynamic programming (scheduling, rod cutting, knapsack), number theory (Fermat little, Chinese remainder, sieve of Eratosthenes, Ethiopian multiplication), game theory (Grundy numbers), advanced data structure (segment tree, suffix tree), string manipulation, linear programming (convex hull), approximate algorithms and randomized algorithms.
Kaggle Machine Learning Competition
Finished the Kaggle 2-sigma global contest in the 29th place (silver medal) against 2000+ data scientists.
Familiar with machine learning open sources: Scikit-learn, tensorFlow, Spark, PyCUDA; implemented generalized linear models, mixed Gaussian, Ridge, Lasso, gradient boosted trees, random forests, SVM, KNN, KMean, deep neural net, convolution neural net, recurrent neural net, reinforcement learning, big data machine learning.
WorldQuant Algorithmic Trading Competition
Participated in WorldQuant trading contest, received bronze prizes.
Familiar with algorithmic trading technique, including factor models (arbitrage pricing theory), Black-Litterman (CAPM, portfolio optimization), Unit-root test (mean-revert pair trading, synthetic hedge), momentum forecast (vector autoregressive model, exponential smoothing), Kalman filter (hidden Markov process), automatic trading robot (reinforcement learning), CPU friendly genetic programming for trading (neural network, deep learning), antithetic Monte Carlo pricing in parallel (GPU computing).
