r_projects.Rmd

---
title: "Technical Projects"
description:
site: distill::distill_website
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

### Shiny App Play Gomoku [repository](https://github.com/pinchunc/ggomoku-shiny)
<iframe src="https://pinchunchen.shinyapps.io/ggomoku/" width="100%" height="500px">
</iframe>
- 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](https://github.com/pinchunc/ggomoku)

### R Markdown Paper [repository](https://github.com/stats295r-fa20/hw1-pinchunc/blob/main/Paper_PinChun.pdf)


### 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