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Oliz888/README.md

Hi there 👋

  • 🤗 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: sz614@georgetown.edu
  • 😄 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 📹

As a Data Scientist 👾

  • 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)

As a Policy Analyst 📑

Project experience 💻

💡 Network Analysis of Twitter to Identify Opinion Leader, Emotion Cascade and Community Structure
  • 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
💡 How Consensus can be Involved through Innovating Voting Mechanism?
  • 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.
💡 Predicting Attitudes toward UBI in EU using Machine Learning Techniques
  • 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
💡 Using DID to Evaluate the Impact of Intensive Case Management Services
  • Designed DID to construct quasi-experiment setting for causal inference to quantify the impacts
💡 Can Remittances Compensate for Parental Absence? Evaluated by the Psychological Well- being and Educational Outcome
  • 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.

Pinned

  1. hk2019protest_network_analysis hk2019protest_network_analysis Public

    HTML

  2. Policy_Memo Policy_Memo Public

  3. Predicting_UBI_Machine_Learning Predicting_UBI_Machine_Learning Public

    Predicting Attitudes toward UBI in EU using Machine Learning Techniques

    HTML

  4. Voting_Consensus_Detection Voting_Consensus_Detection Public

    Jupyter Notebook 1

  5. Data_ethics_and_communication Data_ethics_and_communication Public

    HTML

  6. Economics_and_Statistics Economics_and_Statistics Public

    MATLAB