I am happy to advise master’s and independent study projects in CSE. Here is a list of topics I am interested in:

curriculum development & Software projects
 lab and project development: “Creating labs and assignments using iPython notebooks to introduce the data science and machine learning workflow with realworld applications“
 autograder project: “Developing Gradescope autograder capabilities for cse416a and cse517a“

research projects
 educational research: “Sentiment Analysis for CS Education”
 Techniques
 text mining
 sentiment classification
 opinion mining
 exploratory data analysis/statistics/visualization
 Analyzing Student Data
 we have our own IRBapproved data to work with
 open source: Stanford MOOCPosts Data Set
 Techniques
 graphbased ML: applications of graphbased machine learning using graph kernels or graph convolutional neural networks
 text classification based on graphofword representations
 social network analysis
 classification/regression tasks on biological networks
 images and 3d objects represented as graphs
 educational research: “Sentiment Analysis for CS Education”
Previous and Current Projects
Predicting Student Emotions from Written Feedback using Crowd Sourcing and Machine Learning with Robert Kasumba
Capturing Student Feedback and Emotions in Large Computing Courses: A Sentiment Analysis Approach (SIGCSE TS 2021 paper, presentation) with Robin Linzmayer
When comparing the emotions students had when working on the assignments with the grades they achieved, we observe that a larger fraction of female students have significantly higher grades and a larger fraction of male students have significantly higher emotions as shown in Figure below (middle and right). This means that more female students have high assignment grades but lower emotion scores as shown in the left panel.
Text Classification with Graph Convolutional Neural Network by Walter Wang
 This project aims to achieve traditional text classification via a Neural Network approach, where each word and document are embedded as nodes in a graph and send into Convolutional Neural Network for classification.

Reference: https://arxiv.org/abs/1809.05679
Mat2Py software project: We ported cse517a machine learning implementation projects and autograder from MATLAB to Python.
Sentiment Analysis on Homework Reviews by Zac Christensen
 Some assignments (e.g. hw2 vs. hw4) are perceived better/worse than others by students:
 Sentiment/Emotions do not (or only weakly) correlate with assignment grades:
Study of PATCHSAN: Learning CNNs from Graph Inputs by Yufei Zhou (code on GitHub)
Boulder Finder: Discovering Boulder Areas from LiDAR Topography Data – A DetectionClassificationClustering Approach by Eliot Padzensky
Application of graphs to textural document categorization by Yu Sun (code on GitHub)
Graph Convolutional Neural Networks by Muhan Zhang (code on GitHub, AAAI paper)
Developing and Teaching a Course on Topological Methods for Data Analysis and Machine Learning by Brad Flynn (contact us if you are interested in the course materials)
 The goal of the project was to design and teach a course that provides computer science students with a light understanding of topology and it’s applications for data analysis and machine learning.
Lung Cancer Detection using Convolutional Neural Networks by Jingyu Xin (code on GitHub)
Exploring Deep Learning for Image Generation by Zimu Wang (code and description on GitHub)
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