Technical Projects

Shiny App Play Gomoku repository

• Implement an R package and Shiny app “ggomoku” that allows users to play the board game gomoku
• Design a personal professional website using R to display “ggomoku”

R Package ggomoku repository

R Markdown Paper repository

Hacking Sleep for Better Memory Using Closed-loop tACS, individual project, Summer 2019 - Winter 2020

• Implement an online sleep features detection algorithm that deliver electrical stimulation
• Design and implemented memory tasks using Matlab
• Perform EEG recording, electrical stimulation protocol, and analyzed behavioral and survey data using R
• Conduct sleep scoring and power spectral analysis on sleep physiological data using Matlab

Comparing MANOVA Statistics Using Empirical Power Analysis, individual project, Spring 2019

• Implement Pallai’s Trace, Wilk’s Lambda, Hotellings Trace, and Roys largest root in R
• Use empirical powers to assess the performances of the four tests and compared these tests using different variance-covariance matrices and sample sizes using R

Wine Recognition using Multivariate Classification Methods, individual project, Spring 2019

• Duties: Implement principal component analysis (PCA), Fisher’s linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA) in R to classify 3 types of wines with 13 constituents found in each wine.
• Results: Classification accuracy above 95%

Using Time-Series Methods to Capture Eye-opening Brain States, individual project, Fall 2018

• Implement spectral analysis, cross-correlation, and cross-spectra (coherency) to detect eye-opening state by analyzing EEG data using R

Alzheimer’s Disease Early Detection: Attrition Analysis and Retest Effects, individual project, Spring 2018

• Implement GLMs, linear mixed model, and generalized estimating equations on longitudinal data by using R

Unsupervised Sleep Stages Classification from PSG Data (EEG, EOG, EMG, ECG), team of 2, Fall 2017

• Duties: Implement covariance matrix, autoencoder, probabilistic modeling, and clustering using Python
• Results: Won the NVIDIA GPU Grant

Neural Networks Modeling in Well-being Prediction with Wearable Sleep Data, individual project, Spring 2017

• Duties: Predict subjective sleep quality by implementing CNN, RNN, LSTM by using R and Python.
• Results: Increased 30% explained variability in prediction after adding this model

Python Chatterbot, team of 4, Fall 2016

• Duties: Implement a chatterbot that can help people perform a variety of statistical analysis and plots
• Results: Helped more than 30 students perform statistical analysis in their projects

Media Violence and Real-World Aggression: An Eye-tracking Study, team of 2, Spring 2016

• Duties: Design the experiment, and analyzed eye-movement data with Matlab and R
• Results: Demonstrate that media violence can increase 40% fixation time (attention) toward potential weapons