I am a financial service professional who is passionate about providing smart solutions using data science. I have worked for investment banks such as Credit Suisse, Barclays, Morgan Stanley and Standard Chartered Bank.
Recently, I graduated with a MS degree in Applied Data Science (GPA 4.0) from Univeristy of Michigan Ann Arbor, which is one of the most prestigious public universities in the US.
I am currently looking for a job in Quantitative Research, Algorithmic Trading, Data Science.
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Machine Learning and Statistics
- Cryptocurrency Trading Strategy Creation and Backtest: Use machine learning techniques and statistical arbitrage to capture profitable trading signals. Backtest and evaluate various strategies. Utilize Amazon Web Service serverless architecture to automate and scale the machine learning models in a production environment. Support a variety of classifiers and regressors such as scikit-learn, ensemble of models and neural networks.
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Supervised Learning and Nautral Language Processing (NLP)
- Wikipedia Text Difficulty Classifier: In many real-world applications, there is a need to make sure textual information is comprehensible by audiences who may not have high reading proficiency. The simple Wikipedia, for example, was created exactly for this purpose. It would be very useful to suggest to the editors which part of an article's text might need to be simplified. The goal of this project is to classify each document (sentence) into one of two categories, based on whether it needs to be simplified, using supervised learning approach. The methods used include Bidirectional LSTM, Linear Discriminant Analysis, Xgboost.
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Unsupervised Learning
- Economic Freedom: The goal of the project is to identify important measures affecting the world economic freedom score ranking. Clustering techniques was used to find clusters representing different levels of economic freedom and observe similarities of the countries within the same cluster in terms of geography, demographics, economics, and culture. The methods used include PCA, k-means, agglomerative clustering.
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Exploratory Data Analysis
- Satellites Congestion: As SpaceX and a few other companies requested permission to launch a large number of satellites into low-earth orbit, low-earth orbit is about to get a whole lot busier and this is making many concerned. This project is aimed to analyze low-earth orbit collision risks by parsing and combining satellite time series data from different APIs. Use statistical and visualization techniques to analyze the data.