Analysis of 17-18 EPL Team Attack Patterns (Fall 2024 / Revised Spring 2025)

STAT 430 - Practice of Applied Statistics (Report) (Code)

• Utilized publicly available spatio-temporal football data from Pappalardo et al. (originally from WyScout) to construct attacking sequences based on event types, spatial coordinates, and timing features

• Applied unsupervised learning using Fréchet and Gower distances with K-medoids clustering to define latent attack patterns, followed by statistical testing to verify team-level associations

• Discovered that Big 6 teams shared structurally similar attacking clusters with tactical variation, while Leicester City uniquely relied on fast, efficient duel-based sequences—achieving the highest goal output outside the Big 6

European Football League Player Stats (Spring 2022 / Re-developing from June 2025)

STAT 385 - Statistical Programming Methods (Streamlit) (Shiny App / Old Version)

• Developing an interactive Streamlit web app using Python to analyze and visualize major European football league (Big 5 & Eredivisie/Primeira Liga/Jupiler) players' performance data from Fbref, building on a prior R Shiny version

• Integrating data scraping (BeautifulSoup), radar chart visualization (Plotly), and player comparison features using cosine similarity to allow dynamic selection and comparison of similar players based on diverse in-game performance stats

Pitching Similarity (Spring 2023)

STAT 430 - Baseball Analytics (Shiny App)

• Created an R Shiny app with MLB Statcast data

• Designed pitching location heatmaps with ggplot to visualize and compare selected pitchers' pitching location and diverse stats such as pitch type and speed

Partial Dopaminergic & Tyrosine Hydroxylase of Dopamine Synthesis (Spring 2024)

STAT 530 - Bioinformatics (Report)

• Used MERFISH dataset to figure out the existence of partial dopaminergic neurons in preoptic area (POA) and spatial evidence of tyrosine hydroxylase (TH) and L-DOPA exocytosis for dopamine synthesis

• Implemented dimensionality reduction algorithms such as UMAP and found out a spatial structure and molecular features of partial dopaminergic cells within POA that are potentially interacted with dopamine synthesis

• Conducted spatial autocorrelation to test evidence of spatial location difference between TH/Ddc (dopadecarbosylase) groups and detected a notable pattern of L-DOPA exocytosis from concentrated to dispersed

Analysis & Forecast of Chinese Stock Market (Fall 2023)

STAT 430 - Time Series Machine Learning (Report)

• Analyzed 500 Chinese stocks price data with diverse time-series perspectives (Classic ARIMA / Clustering / Deep Learning GRU)

• Worked on cluster analysis part by applying algorithms like DBSCAN to group stocks with similar price trends and found real estate / Food / Medicine showing notable growth and stabled fluctuation

Beijing Housing Prices (Fall 2022)

STAT 432 - Basics of Statistical Learning (Report) (Presentation)

• Conducted both supervised and unsupervised analysis with a Beijing housing price dataset from Kaggle to understand price trends in Beijing, China

• Applied diverse regression algorithms such as LASSO and XGBoost to predict 'price' and figured out Number of rooms / Age & Condition / Distance from an epicenter to be the most significant predictors

• Clustered the data using K-Means, grouped the observations as 'over'/'under' priced within their clusters and discovered the trend of 'overpriced' properties to be relatively old and close to the epicenter

Lyrics Generator (Fall 2022)

STAT 430 - Fundamentals of Deep Learning (Report) (Presentation) (Github)

• Built a neural network structure (LSTM) with PyTorch to generate lyrics with a dataset containing 6 million songs from Kaggle

• Trained and tested the model with different hyperparameters (Sequence length / Batch size) by genres and discovered longer sequence length not necessarily guarantee better model performances

• Generated sample lyrics and observed Rap to be relatively short and negative whereas Country and R&B to be long and romantic

Consulting Project: Air Pollutant in Gucheng, China (Spring 2022)

STAT 443 - Professional Statistics (Report) (Presentation)

• Consulted with a mock client about a project using an air pollutant dataset from Gucheng, China using R

• Visualized diverse time-series-related plots with ggplot and observed relatively higher air pollutant (PM2.5) level in winter, weekend, and nighttime

• Tested various algorithms (Linear Regression / Multinomial / K-Nearest Neighbors) to find the best model which predicts a next day's air pollutant (PM2.5) level based on its previous day's meteorological variables values

Denoise Autoencoder (Spring 2022)

CS 307 - Modeling & Learning in Data Science (Report)

• Built autoencoders with 'FashionMNIST' data in PyTorch to observe a relationship between noisy data and the autoencoders' performances

• Trained the autoencoders using manipulated data with three different noise type (No noise / Gaussian / RandomErasing)

• Evaluated the autoencoders' performances by comparing their average train & test loss and visualizing t-SNE plots of latent spaces

Market Segmentation of Credit Card Customers (Fall 2021)

STAT 430 - Unsupervised Learning (Report & Code) (Presentation)

• Explored a bank data from Kaggle to discover notable insights of customers' leaving status based on their demographics and banking information using Python

• Applied diverse algorithms (Hierarchical Agglomerative, K-Prototype Clustering) from Scikit-learn to visualize a clustering structure and set an assumption of customers with lower and unstable income to be less economically active and likely to leave referring to the structure and exploratory data analysis

• Computed supervised evaluation scores (Complete/Homogeneity/V-Score) of the algorithms' predicted clustering label created based on the assumption to compare with the pre-assigned label and identified poor match between the predicted and original labels (weak or no association between customers' info and their leaving status)