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machine learning engineer

Los Angeles, CA, 90066
December 05, 2019

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Work experiences

*/**** – 3/2019 Research assistant, Tsinghua University, Beijing, China.

Machine Learning:

--Data cleaning and feature engineering

--Using machine learning algorithms such as genetic programming, SVM and neural networks for generating trading strategies.

--Model evaluation and model selection

--Built a platform to generate trading strategies using genetic programming algorithm. Made money successfully using the strategies.

--Distributed computing involving tens of computers


Python 2 yrs, developed a trading strategy generation system using machine learning algorithms.

C++ 2 yrs, data preprocessing, feature generation, application of API.

Java 1 yr, built several games

Matlab 6 yrs, developed a laser propagation simulation platform.


2010–2016 Ph.D., Engineering Physics, Tsinghua University, Beijing, China.

− Build up a computational platform to simulate the laser propagation in optical fibers.

− Dissertation: Nonlinear effects of partially coherent light in silica fibers.

− Publication: published 4 papers as first author in top journals in the field of laser optics 2012–2013 Visiting Student, Physics, University of California, Berkeley.

2006–2010 B.Eng., Engineering Physics, Tsinghua University, Beijing, China.


Certificates earned on Coursera:

"Neural Networks and Deep Learning" by

"Convolutional Neural Networks" by

"Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization" by

"Structuring Machine Learning Projects" by

"Sequence Models" by


Experience of various machine learning algorithms (e.g. Genetic programming, DNN, CNN, RNN, SVM), Experience with deep learning frameworks (e.g. TensorFlow, Keras), Proficiency with SQL, Strong software engineering skills (Python, C++, Java), Excellent math background, Prototyping new ideas quickly

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