Selected Publications

This study presents a research approach by comparing machine learning models for predicting the lifetime consumption of posts published in brands’ Facebook pages. It turns out the XGBoost model performs the best among all the single models and the ensemble one. Also, feature analysis is done to understand how each of the seven input features influenced the response (category, page total likes, type, month, hour, weekday, paid). The page total likes was considered the most relevant feature for the model, with the largest importance. More- over, categories “Category” and “Type” are consistently important, which is consistent with the empirical study.
Vol. 13, Num. 2, JGBM, 2017.

Recent Publications

Predicting Brand Advertisement Consumption on Facebook by Model

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DNA fragmentation detection from human blood cells.

Keras tutorial

Keras tutorial with backend of Theano for deep learinng.

Faster LASSO

We propose a faster lasso computation algorithm which can apply on high-dim data inference.

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Artificial Intelligence with IoT dashboard.

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PyTorch tutorial from zero.

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I am a teaching assistant for the following course at George Washington University:

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