Yuanmo He

Experience

• 

  UCL

  Sept 2017 - Dec 2017

  Transition Mentor

  - Organised weekly discussions and mentoring meetings (1-10 people) to support the first-year students’ transitions in various aspects of life from school to university.

• 

  The London School of Economics and Political Science (LSE)

  Sept 2021 - now

  - Led, designed, and executed end-to-end research projects that use advanced quantitative and computational methods (including machine learning, natural language processing, causal analysis, and social network analysis) to study socioeconomic inequality in daily behaviour and social interactions with large-scale digital trace data.- Designed and implemented ETL (extract, transform, load) pipelines tailored for various projects, leveraging Python, R, SQL, Spark, Git, Azure Cloud Computing, Twitter API, Google Geocoding API, and Facebook Marketing API. - Employed Python to manipulate, analyse and visualise various types of data (mobile-tracking mobility data, US Census data, and data from social media platforms including Twitter, Facebook, and Yelp).- Proposed, implemented, tested, and validated a new unsupervised learning method to estimate the individual Twitter users' socioeconomic status (SES) for 3.5 million users (prior studies' benchmarks ~50,000). - Conducted regression modelling, graph embedding, clustering, and community detection with Python toolkits and self-developed code to test hypotheses about consumption inequality.- Designed natural experiments with matching methods and observational data to show that Twitter users with high SES have significantly higher social capital (measured by network properties) than those with low SES.- Implemented text analysis methods, including sentiment analysis, topic modelling, word embedding, and large language models to explore the differences in social interactions on Twitter between users with high and low SES.- Collaborated with people from diverse academic, technical, and cultural backgrounds (e.g., physicists, biologists, ecologists, computer scientists, economists, and writers).- Presented research findings at top academic conferences.- Published in a top peer-reviewed academic journal. Show less Courses: MY470 Computer Programming, MY474 Applied Machine Learning, ME314 Introduction to Data Science and Machine Learning- Communicated theoretical concepts and practical implementations of computer programming, machine learning, and natural language processing to students with diverse academic, technical, and cultural backgrounds.- Led daily 1.5-hour computer lab teaching to two classes of 20+ students on understanding and performing machine learning and quantitative text analysis with R at the intensive three-week LSE summer school course ME314. Adjusted the teaching materials to be updated and relevant. Graded mid-term and final projects in R.- Assisted weekly/biweekly 1.5/2-hour computer lab teaching to classes of 30+ students on computer programming with Python & R and machine learning & deep learning with R. Graded weekly/biweekly computer programming & data analysis assignments and final projects in R & Python with GitHub Classroom. Show less

  • Fellow in Advanced Quantitative Methods

    Sept 2024 - now

  • Doctoral Researcher in Computational Social Science

    Sept 2020 - Sept 2024

  • Class Teacher/Graduate Teaching Assistant (multiple posts)

    Sept 2021 - Apr 2023