Hi there! I am Simian (or Simon if you prefer), currently a PhD student in Department of Information Systems and Analytics at NUS School of Computing.

My research interests lie in analytical modelling and mechanism design for online streaming media platforms. I am also exploring opportunities to employ structural model for empirical works. If you have interesting research ideas, I would be happy to discuss with you via my email zhang.simian at u.nus.edu.

Education

• Ph.D. in Information Systems and Analytics, NUS (Aug 2023 – Present)
• B.Sc.(Hons.) in Data Science and Analytics, NUS (Aug 2019 – May 2023)

Work & Research Experience

• Student Research Assistant, NUS IORA, Singapore (Jan 2022 – July 2023)
  • Conducted Bayesian structural estimation for comparative statics, processed data from multiple sources in Python and R, and assisted in model diagnostics in multiple projects including contest design and pilot training
  • Performed model robustness checking with extensive simulations for herd detection in financial forecasts using large-scale Bayesian structural estimation methods
• Data Engineer Intern, ByteDance, Singapore (May 2021 – July 2021)
  • Constructed and optimised stable data pipelines using Hive, Spark, Kafka to account for costs and revenues of video services such as storage, transcoding, playing and content delivery network (CDN)
  • Collaborated with product managers and engineers to provide accurate and timely dashboard reporting
  • Built forecasting model in Python to predict usage of bandwidth by video services with mean absolute error less than 3% (improved from > 10%) to help budget planning
• Data Analytics Intern, International SOS, Singapore (Dec 2020 – Jan 2021)
  • Assisted data scientists and data analysts with timely data cleaning, modelling and reporting
  • Wrote R scripts to automate data quality checking of more than 1,000,000 entries in data migration project
  • Analyzed medical data, applied clustering model with feature engineering to provide operational insights

Skills

• Python: Deep Learning, Statistical Learning, Data Analysis, Data Cleaning, Data Visualization, etc.
• R: Statistical Learning, Causal Inference, Data Analysis, Data Cleaning, Data Visualization, etc.
• SQL: CRUD, Data Analysis, Data Warehousing, Data Pipeline, etc.
• Visualization Tools: Tableau, PowerBI, etc.
• Others: Java, MATLAB, Stata, etc.