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General Information

Full Name Kelvin Ng
Date of Birth 7th May 1997
Languages English, Cantonese, Mandarin

Academic Interests

  • Representation Learning
    • Interpretable representation learning methods for building so-called world model/system identification
    • Continual learning of representation for building world model
    • Computer vision based representation learning
    • How spatial semantics and temporal structure of data emerge
  • Reinforcement Learning/Control Theory
    • Optimal control with learned world model
    • How optimal control task may impact representation learning

Education

  • 2019
    BSc in Mechanical Engineering
    The University of Hong Kong
    • 1st Class Honours.
      • Microsoft Innotech Law Hackathon 2018, First Runner-up
      • Fuzzy system and neural network (A-)
  • 2023
    Data Science Summer Camp - Theory, Algorithms and Applications
    Institute of Data Science, The University of Hong Kong
    • Machine learning performance evaluation, Bayesian classifier, linear programming, nonlinear programming, network theory
  • 2022
    Reinforcement Learning Specialization - Online Course
    University of Alberta
    • Understand the space of RL algorithms (TD learning, Monte Carlo, Sarsa, Q-Learning, Policy Gradient, Dyna and more)
    • Implement RL algorithms (TD, Expected Sarsa, Q-Learning, Dyna, Actor-Critic) on toy datasets, in both discrete and continuous state environments
  • 2022
    Natural Language Processing with Sequence Models - Online Course
    Deeplearnging.ai
    • Learn about neural networks for sequential data modeling (RNN, LSTM, GRUs)
    • Implement sequential model on different tasks (Named Entity Recognition, Next-Word Generator, Sentiment Analysis)

Experience

  • 2021 - Present
    Performance Manager - Commercial Team
    Foodpanda Hong Kong
    • Implement LLM algorithm for competitor and product level analysis
      • OpenAI API
    • Develop dashboards, visualizations and regular reports to facilitate efficient data analysis on the topics of market competition, profit and loss, vendors acquisition and churn
      • Tableau, Looker Studio, BigQuery
    • Manage ETL process for data warehouse
      • DBT, Airflow
    • Build vendor grading machine learning model to predict vendor performance and churn risk
      • Python, Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, Seaborn, Plotly, Dash, Streamlit
  • 2020 - 2021
    Data Science Analyst
    Hong Kong Telecom
    • Build machine learning model for customer churn prediction of LTS service and automate feature engineering and model training pipeline
    • Create and schedule pipelines of web-crawling to extract data from external websites for sales strategies enhancement, competitor alert and machine learning model feature enrichment
    • Conduct customer segmentation for Club Shopping from cross business unit data to understand customer interests and preferences for target marketing

Projects

  • Whitebox Transformer Implementation
    • Implement CRATE architecture for image classification task on Fashion-MNIST dataset
    • Visualize the low-rankness of the representation, autocorrelation of subspace learnt in each layer of MSSA
    • Visualize the semantic meaning of each attention head
  • Resemblance of Cross Attention like Operator with Conditional GMM Denoiser
    • Train conditional GMM denoise.
    • Visualize the conditional sampling results and interpret the results
    • Illustrate the resemblance of cross attention like operator with conditional GMM denoiser
  • (WIP) Mini World Model with CRATE
    • Implement LeWM from scratch
    • Replace ViT with a more interpretable architecture, CRATE, to study how spatiotemporal structure of data emerge in latent space
    • Explore, with CRATE replacing ViT, whether attention patterns can enable unsupervised video segmentation
    • Familiarize myself with Hydra and wandb for efficient experiment tracking