Python on Ye Joo Park's Blog

Using GPT to Generate Paper Feedback

Mon, 08 Sep 2025 00:00:00 +0000

In 2012, I graded hundreds of papers as a teaching assistant. This was a time-consuming and subjective process, and I often spent days grading just to meet deadlines.

Large language models (LLMs) like GPT-5 are transforming education, including the grading of written assignments. LLMs provide an efficient and scalable way to assess students' writing while also generating personalized feedback. However, my experience using LLMs for grading has been unreliable for the following reasons:

Analyzing College Town Landlords at UIUC, BYU, and PSU

Thu, 30 Jan 2025 00:00:00 +0000

✨ Background¶

I first moved into an apartment at UIUC during the summer of 2007. The apartment was Gregory Place under a management company called JSM. Here is a photo of the apartment from 2007. The construction site shows the second building being built.

Back in 2007, JSM was regarded as one of the better management companies, along with Royse Brinkmeyer and Roland. Almost two decades later, JSM is still highly regraded. Prospective tenants lining up from the early morning in front of JSM's office to secure a lease was a sight to see in recent years.

Sentiment Analysis using spaCy and DistilBERT

Thu, 23 Jan 2025 00:00:00 +0000

Natural language processing (NLP) aims to give computers the ability to understand, process, and even generate human language. This notebook introduces the common preprocessing steps and demonstrates how to use a widely used transformer model (distilbert-base-uncased-finetuned-sst-2-english) to perfrom a sentiment analysis. 😀😦🙁

In [1]:

import pandas as pd
import numpy as np
import plotly.express as px

In [2]:

pd.set_option('display.max_columns', 100)

🗃️ Load data¶

This exercise uses a small dataset that contains reviews of two apartments at Indiana University Bloomington.

Run MOSS Plagiarism Checker on Jupyter Notebooks

Thu, 20 Apr 2023 00:00:00 +0000

MOSS

MOSS (Measure of Software Similarity) is a system to to determine the similarity of programs. It’s most commonly used to detect plagiarism in programming classes. It was developed in 1994 by David A. Aiken, Robert Sedgewick, and Andrew W. Appel at Stanford University.

MOSS for Plagiarism Detection

MOSS (Measure of Software Similarity) is a great tool for catching plagiarism because it can identify even small similarities that may not be obvious to the human eye. MOSS works by comparing the source code of two or more files. It does this by breaking the code down into tokens, which are the smallest meaningful units of code. A token could be a variable name, a function name, or a keyword. MOSS then compares the tokens in each file to see how many of them are the same.

Detecting Local Image Features with SIFT

Fri, 06 Apr 2018 00:00:00 +0000

SIFT (Scale-Invariant Feature Transform) is a computer vision algorithm for detecting and describing local features in images. Developed by David Lowe in 1999, SIFT has become a fundamental tool for various applications due to its robustness and versatility.

Use Cases of SIFT

👀 Object Recognition

SIFT is widely used for detecting and identifying specific objects within complex scenes. Its ability to extract distinctive features that are invariant to scale, rotation, and illumination changes makes it effective for recognizing objects across different viewpoints and conditions.