Yuting BAI

About Me

Proficient in end-to-end machine learning workflows, including data preprocessing, feature engineering, model training, and evaluation. Experienced with Python-based data science tools such as Pandas, NumPy, Scikit-learn, and PyTorch, with strong visualization skills using Matplotlib and Seaborn. Skilled in exploratory data analysis and predictive modeling, especially using Graph Neural Networks for complex structures. Comfortable working in Jupyter Notebook within the Anaconda environment. Also familiar with R, Tableau, and PostgreSQL for statistical analysis and dashboard reporting.

Skills

Python

Pandas

NumPy

Scikit-learn

PyTorch

Feature Engineering

Model Evaluation

Predictive Modeling

GNN

Data Analysis

Matplotlib

Seaborn

Jupyter Notebook

Tableau

PostgreSQL

JIRA

Git

Education

Curtin University

Master of Philosophy (Computing)

02/2025 – Present

Curtin University

Master of Computing (Artificial Intelligence)

02/2024 – 12/2024

Curtin College

Masters Qualifying Program

02/2023 – 12/2023

Projects

Project 1: Deep Learning for Hydrogen Storage Prediction in Metal–Organic Frameworks (MOFs)

Developed a deep learning framework to predict hydrogen storage performance in MOFs by integrating structural and chemical features. Designed and evaluated multiple neural network architectures and implemented a multi-task learning approach to predict various storage metrics under different conditions. Applied the model to identify potential high-performance materials and extract structure–property insights to support data-driven materials discovery for clean energy applications.