oliz888,Shirui (Oli) Zhou,github

🤗 Welcome to my website
🔭 I’m currently working on Social Network Analysis, Machine Learning, NLP
🌱 I’m currently learning LLM application on digital governance, reinforcement learning in autonomous system
👯 I’m looking to collaborate on investigative data journalism project
🤔 I’m looking for help with game design, VJing
💬 Ask me about dencentralized social graph, cybernetics, posthumanism, AI-governance and music production
📫 How to reach me: [email protected]
😄 Pronouns: They/them 🌈
🎵 Arranger (in Logic): post-punk, mathrock, shoegaze
⌨ Random Thoughts: in English, in Mandarin
⚡ Fun fact: A human really love sea 🏝 Skateboarding 🛹 Guitar 🎸 and photography 📹

My skills: Programming & Tools: Python (Pandas, Sklearn, Matplotlib, Seaborn, NetworkX, Nature Language Toolkit), SQL (Advanced), Tableau, PowerBI, R (RCT, DID, RDD, IV), PySpark, AWS, Google Analytics, MS Office
Specialization: supervised methods (decision tree, KNN, Ensemble methods, Naive Bayes and SVM) and unsupervised methods (PCA, Clustering, Text Mining and Analysis and Association Analysis)

Deployed K-core decomposition to examine the community structure, applied NLP including Name Entity Recognition, Sentimental Analysis and Topic Modelling on tweets to investigate the emotion cascade

Using PCA and UMAP to visualize the participants’ stance on a 2-dimensional map, uses Kmeans to cluster and classify group A and B, and uses centroid coords calculation to get the distance between two groups.

Built and trained Logistic Regression, DecisionTree, SVM, RandomForest, XGBoost and GBDT to identify 5 primary indicators and 35 secondary indicators the key influential factor on the attitude of EU citizen towards UBI

Designed DID to construct quasi-experiment setting for causal inference to quantify the impacts

Developed a multivariate model in STATA incorporating Difference-in-Difference and Propensity Score Matching
Estimated the indirect effects and direct impacts of remittances in the context of parental work-related migration on the well-being and academic achievements of left-behind children.

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