Dr. Dalzell's Research Students

These are the excellent students I am currently working with on a statistical research project, applied data project, writing project, or other work.

Thesis Research Students

Ziyi (Sophie) Li
Spring 2022 - Fall 2022
METHODS AND CONSIDERATIONS FOR MODELING FAKE NEWS
Focus: Networks
Yining Liu
Fall 2021 - Spring 2022
Marketing Insights Through Text Analysis: Topic Modeling and lexicons
Tianyi (Sherry) Mao
Fall 2021 - Spring 2022
Exploring Education: Creating a guide on how to use visualizations to explore a complex data set

Statistical Communication Project Team

Christy Lai and Elaine Jin
  • Hundreds of our students take STA 112, our introductory regression course.
  • Where in this class do students usually get stuck, and how can we design activities and tools specifically designed to address these conceptual troubles?
  • Christy and Elaine are working to identify these trouble areas and create tools that faculty can use to help clarify these statistical concepts for students.
  • The students are researching best practices in statistical communication and statistical writing and applying these to create activities and guidelines to improve student understanding.

Previous Research Students


2021 - 2022

Senior Capstone Students

Project 1: An Analysis of Stock Market Features
  • Jessica Cao
  • Patrick Fan
  • Andre Gheilerman
  • Ethan Lewis
  • Caitlyn Marsac

Project 2 - An Analysis of Disaster Tweets on Twitter
  • Sylvia Li
  • Echo Liu
  • Victoria Zhang
  • Katherine Zhu

Project 3 - Building a Predictive Model to Identify Fake Job Postings
  • Aiai Chen
  • Sixuan Liu

Project 4 - Online Education During the COVID 19 Pandemic - An Analysis
  • Mackenzie Wood
  • Amy Zinnia

2020 - 2021

Full Year Students

(22) Taylor Longmire
  • Project 1: A Longitudinal Analysis of Behavioral Trends and Cultural Norms across 4 Generations in California.
  • Project 2: An Analysis of Difference in Online Platform Uses in K-12 Schools during COVID-19.


2019 - 2020

Full Year Students

(21) Benjamin Liu:
  • Differential Privacy and Synthetic Data.

(20) Tian (Simon) Yun:
  • Natural Language Processing and Latent Dirichlet Allocation.

(19) Guanqi Zeng:
  • Bayesian Approaches to Record Linkage

(18) Binfeng (Bill) Xu:
  • Iterative Constringency Optimization: Preclustering Approach to Agent Interactive Data.

Fall Semester Students

(17) Weixi (Frank) Cheng:
  • A Bayesian Approach to Market Data Modeling in Financial Engineering.

(16) Gus Conwell:
  • An Analysis of the Impact of the New Gym on Student Gym Behavior at Wake Forest University.

(15) Siyi Liu:
  • Understanding Poverty in New York City using Linear and Non-Linear Modeling Approaches.
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(14) Alison Scarlett:
  • An Analysis of Student Health Data at Wake Forest.

(13) Matt Sherrick:
  • Canton, Ohio Forecasting: Using Statistical Analysis and Visualization to Predict Future NFL Hall of Fame Inductees.

(12) Yijie (Eric) Yang:
  • Meta-analysis for the Relationship between Violent Video Games and Violent Tendencies.

(11) Hang (Oriana) Zhao:
  • Customer Buying Habits - What do they buy together or buy next?

2018 - 2019

Full Year Students

(10) Yiwen Tang:
  • Classifying Hate Speech Using a Two-Stage Model.

(9) Dave Segall:
  • Promoting Effective Teaching Through Comprehensive Evaluation Systems.

Fall Semester Students

(8) Echo Dong:
  • The Data Scientist Job Market: Using Text Analysis to Distinguish Requirements of Different Types of Statistics and Data Science Jobs.

(7) Curtis Golden:
  • S& P, Moody’s, Fitch: Credit Profiles within Business.

(6) Weston Morgan:
  • Urban Air Pollution Analysis using Random Intercept Models.

(5) Jian Shue:
  • Categorical Data Methods for Chicago Crime Data.

(4) Karina Tang:
  •  Using Mixed Effects Models to Predict the Potential of Professional Soccer Players.

(3) Wenbo Ye:
  • Bayesian Game Theory and its Application in Economics and Finance.

(2) Stanley Yuan:
  • An Exploration in Machine Learning Approaches with an NBA data application.

2017 - 2018

Full Year Students

(1) Peter Fabian: Play-by-play Win Probability in NFL Football Games.