About
MY BACKGROUND
I am an incoming CIT intern at Pacific Northwest National Laboratory and a Purdue University graduate with B.S. degrees in Data Science and Applied Statistics. As a former Stamps Scholar with three years of research experience, including work with Purdue’s Rosen Center for Advanced Computing and Sustainable Transportation Systems Research Group, I am committed to advancing research in data science and related quantitative fields.
Publications
WHAT I'VE WRITTEN
Generating Frequently Asked Questions from Technical Support Tickets using Large Language Models
Christina Joslin, David Burns, FNU Ashish, Elham Barezi, Ph.D
This paper was presented at the 12th International Workshop on HPC User Support Tools at Supercomputing 2025 in St. Louis, Missouri on November 16, 2025.
Used or New Electric Vehicles? Public Preferences and Market Segments
Bruno Cesar Krause Moras, Christina Joslin, Konstantina Gkritza, Ph.D
This paper was published in October 2025 in the International Journal of Sustainable Transportation.
Presentations & Awards
WHERE I'VE PRESENTED
Summer 2025 Undergraduate Research Conference
(Virtual Presentation)
Purdue University, July 28 - August 1, 2025
Transforming Technical Support with Artificial Intelligence: Structured Question Generation from Support Tickets
Christina Joslin, David Burns, Ashish
Practice and Experience in Advanced Research Computing (PEARC25)
(Student Program Attendee & Lightning Talk Presenter)
Columbus, Ohio, July 20-24, 2025
Selected for the PEARC25 Student Program and awarded scholarship.
Christina Joslin
Spring 2025 Undergraduate Research Conference
(Poster Presentation)
Purdue University, April 8, 2025
Preference for Charging Station Venues - A Comparison between Electric Vehicle Users vs. Non-Electric Vehicle Users
Christina Joslin, Bruno Cesar Krause Moras, Konstantina Gkritza, Ph.D
2025 Transportation Research Board Annual Meeting
(Poster Presentation: TRBAM-25-02448)
Washington DC, January 6, 2025
Used or New? Investigating Consumer Preferences in the Electric Vehicle Market
Bruno Cesar Krause Moras, Christina Joslin, Konstantina Gkritza, Ph.D
Fall 2024 Undergraduate Research Expo
(Poster Presentation)
Purdue University, November 18, 2024
How Does Experience Affect Perceptions of Electric Vehicle Characteristics? A Users vs. Non-Users Comparison
Christina Joslin, Bruno Cesar Krause Moras, Konstantina Gkritza, Ph.D
NEXT-GEN Transport Systems Conference
(Poster Presentation)
Purdue University, September 21-22, 2024
Best Poster Presentation Award
Does Experience Shape Perceptions? A Comparison of Users' and Non-Users' Views on Electric Vehicle Characteristics
Christina Joslin, Bruno Cesar Krause Moras, Konstantina Gkritza, Ph.D
The Data Mine Corporate Partners Symposium
(Poster Presentation)
Purdue University, April 24, 2024
Caterpillar Inc. Electric Vehicle Charging Operations
Christina Joslin - Team Leader/Teaching Assistant for the CAT EV Charging Operations Team consisting of 12 graduate/undergraduate students.
Provisional Patent Submitted April, 2024
Methods And Systems For Charging Electric Machines with On-Site Mobile Charging Stations
Khalil, S., Tuckmantel, P., Almgren, G., Sinha, R., Wu, R., Deepak, A., Linn, T., and Joslin, C.
Spring 2024 Undergraduate Research Symposium
(Poster Presentation)
Purdue University, April 9, 2024
Public Adoption of New versus Pre-owned Electric Vehicles
Christina Joslin, Bruno Cesar Krause Moras, Konstantina Gkritza, Ph.D
The Data Mine Corporate Partners Symposium
(Poster Presentation)
Purdue University, April 24, 2023
BASF Forecasting Analogue Years for Corn & Soybeans
Christina Joslin - Student member of BASF Corn & Soybean Team.
Projects
WHAT I’VE CREATED
PromptBeaver
Description: Developed PromptBeaver, a prompt engineering tool designed to help undergraduate students studying CS/DS/AI build, analyze, and refine prompts for LLMs. Rather than generating answers directly, PromptBeaver guides users toward crafting stronger prompts that elicit customized conceptual explanations to simulate the type of iterative learning support students might receive during TA or professor office hours. Hosted as a Streamlit web application, the platform features two core workflows: Build a Prompt and Analyze My Prompt. This project was developed as part of Purdue University’s CS 475 Human-Computer Interaction course in Spring 2026.
ML Job Compass
Description: Created ML Job Compass, a Streamlit dashboard that guides students and professionals in charting career paths in machine learning. Using natural language processing, Retrieval-Augmented Generation (RAG), and data visualization, the app analyzes over 1,000 U.S. job postings to highlight domain-specific skill demands. Users can explore industry trends, then generate personalized skill checklists or phased roadmaps tailored to their target role and specialization. The system leverages a local Chroma vector store with an Ollama-backed LLM (i.e., Granite-3.3) for grounded recommendations, and supports deployment via Docker Compose or local Python environments. A manuscript based on this work has been submitted for publication.
Dog and Cat Breed Classifier with MobileNetV2 Transfer Learning
Description: This project implements a Convolutional Neural Network (CNN) in Python to classify images from 104 dog and cat breeds, utilizing a dataset of 29,000 images. The MobileNetV2 architecture, known for its efficiency and scalability, was used with transfer learning to achieve an F1 score of 84% on the test set. Key components include data preprocessing, model training with early stopping, and comprehensive evaluation metrics, making the model suitable for large-scale image classification tasks.
Description: This project builds and fine-tunes a Natural Language Processing (NLP) model in Python to classify disaster-related tweets using Hugging Face's Transformers library. It covers text tokenization, model fine-tuning with a pre-trained DistilBERT, performance evaluation, and achieving an F1 score of 83% on the test set.
Disaster Tweet Classifier: Fine-Tuning DistilBERT for NLP
Amazon Review Classifier: Fine-Tuning Multilingual BERT
Description: This project develops an NLP pipeline for multilingual sentiment analysis using Python and the pre-trained BERT model nlptown/bert-base-multilingual-uncased-sentiment. It includes data preprocessing, fine-tuning with Hugging Face's Trainer, and evaluation with metrics such as accuracy and F1-score. The fine-tuned model classifies text sentiment (negative, neutral, positive), achieving 77% accuracy and 76% F1 on the test set using a stratified subset of 84,000 examples.
CNN Digit Recognizer
Description: This project implements a Convolutional Neural Network (CNN) in Python to recognize handwritten digits from the MNIST dataset. It includes data preprocessing, model training, evaluation, and saving the trained model. The GitHub version achieves a test accuracy of 99.0%, while the Kaggle version attains approximately 98.8% accuracy.
Note. In the GitHub repository, the entire MNIST dataset is loaded directly from TensorFlow, resulting in differences in data preprocessing between the Kaggle Notebook and the GitHub repository.
Titanic Survival Prediction
with XGBoost Classifier
Description: This project develops a comprehensive machine learning pipeline in Python to predict Titanic passenger survival using the XGBoost classifier. The process encompasses data preprocessing—employing standard scaling, one-hot encoding, and K-Nearest Neighbors (KNN) imputation—followed by hyperparameter optimization via RandomizedSearchCV. The model's performance is thoroughly evaluated and achieved a test accuracy of 78% with final predictions exported to a CSV file.