Undergraduate Research Showcase Showdown

Join us in highlighting and celebrating Spring 2022 Carnegie Mellon Statistics & Data Science undergraduate research and capstone projects! Students in CMU's Statistics & Data Science have multiple opportunites to engage in team research projects from the time that they are sophomores through graduation. Click on each class's tab in the navigation bar to the left to learn more about this semester's class projects. The bar also allows you to access projects from previous semesters.

36-315: Statistical Graphics & Visualization (groups)
36-490: Advanced Undergraduate Research (groups)
36-497: Corporate Capstone/Data Science Experiential Learning (groups)
36-600: Overview of Statistical Learning and Modeling (groups; website interaction only)

Click on the appropriate link to see projects from a previous semester:

Spring 2020 Fall 2020
Spring 2021 Fall 2021
Spring 2022

36-315 Statistical Graphics & Visualization

As part of their final project, teams of students build and use interactive statistical graphics and visualizations in a research study. See this fall's project posters below.

Fall 2022: Zach Branson

Analysis of Arbnb in New York City
Steve Zho, Tianyou Zheng, Martina Gai
Tackling the Future of Jobs
Joonseok Park, Tony Hwang, Mason Kim
Spotify Top 10s in the 2010s
Srihita Nangunuri, Shivani Ram, Shayan Panjwani
Online Shoppers Purchasing Intention
Emily Zhang, Fuyang Lu, Lesley Yan, Michelle Wang
An Analysis of the FIFA 2022 Game Male Player Dataset
Max Shushkovsky, Ethan Vertal, Zachary Leventhal
Manrattan – A Look into NYC’s Rats
Alex Cheng, Liz Chu, Arthur Jakobsson, Kevin Ren
Happiness Around the World, 2015-2022
Elizabeth Szeto, Nicole Sim, Melody Wang, Eileen Xiao
Consumer Behaviors
Amor Ai, Christina Choi, Nidhi Singh
Credit Card Customers’ Behaviors
Yiting Chen, Yiyang Wei, Judy Xu, Iris Wang
Decision-Making in the Used Cars Market
Wenhan Li, Jenny Guo, Miranda Zhu, Cynthia Cai
A Study of the Resale Vehicle Market of the United States
Gary Gao, Lana Lan, Jessie Yung
Bike Sharing in Seoul and D.C.
Juran Zhang, Qianfan Luo, Yuntian Shen
iMDB Top 250 Movies Analysis
Madeline Fidel, Nick Mlodzienski, Megan Baker, Brayden Gess
Understanding relationships regarding academic performance
Aris Chang, Nathan Dekhovich, Jonathan Park, Vishal Saikrishnan
Leveraging Hotel Data for Business and Travel: A Comprehensive Analysis
Jack Kiefer, Liam Gersten, Yu-An Chen
Starbucks Drinks Analysis
Chaiyatat Chawaldit, Ritu Chatterjee, Chanaradee Leelamanthep, Monica Paz
NBA Dataset Visualization
Tyshanti Montgomery, Jason Lin, Hongyou Chen, Hao Cui
RollerCoaster Tycoon: An Analysis of Coaster Construction
Claudia Lyu, Ivana Lin, Lingruo Pan, Megha Koshy
Analysis of an F1 Dataset
Daniel Zhu, Ravi Patel, Kruthi Thangali, Ezra Boldizsar
Graphical Narrative of Roller Coasters
Nanditha Niranjan, Suha Niyas, Yilin Wang, Zixuan Ye
Winning in the NWSL
Gustavo Garcia-Franceschini, Connor O’Keefe, Dhruva Naidu, Harvey Zheng
Analysis of Recidivism in Georgia
Isha Muthyala, Sarah Tandean, Matthew Visco, and Jerry Wang
Analysis of Brain Stroke Individuals
Amy Gu, Katherine Hu, David Lurie, Owen Xia
IMDb Movie Analysis
Josh Braverman, Pranav Reddy, Seonho Park
The NBA is a Game of Statistics
Jeffrey Key, Nathan Yeager, Peyton Moffrat, Sukwoo Kwon
The Legacy of Game of Thrones: A Data-Driven Analysis
James Chen, Eric Rohrer, Tengjing Wang
Success Related Factors for the Astros and the Phillies in the 2022 World Series
Eric Moore, Aidan Powers, Jin Yu Kim

36-490 Undergraduate Research

36-490 Undergraduate Research is an advanced research course for juniors and seniors. Groups of students collaborate with researchers and scientists in other disciplines and use advanced statistical methodology to tackle real-world challenges. The course heavily emphasizes professional skills development, including collaboration and both written and oral communication.

Fall 2022: Peter Freeman, Jamie McGovern, Zach Branson, David Brown, Ron Yurko

Classification of Children's Literature
(with Rebekah Fitzsimmons (Heinz College))
S. Xie, C. Liu, Y. Pan, W. Huang (with David Brown - English)
Poster Presentation
How to Increase CMU Advising Survey Response Rate?
(with Joanna Dickert - Dietrich College and Shannon Foster - Institutional Research and Analysis)
S. Kong, K. Nam, L. Fan, Y. Chen (with Zach Branson - Statistics & Data Science)
Poster Presentation
A Disturbance in the Force? Modeling QB Pressure with Force-Based Metrics
A. Liang, H. Lian, J. Quan, J. Choi (with Ron Yurko - Statistics & Data Science)
Poster Presentation
Big Data Derby
W. Deng, S. Mei, F. Wang, L. Wang (with Ron Yurko - Statistics & Data Science)
Poster Presentation

36-497 Corporate Capstone

36-497 Corporate Capstone is a course in which we closely collaborate with both commercial and non-profit partners on real data science problems through educational project agreements. These projects can vary in scope but most commonly center on data integration, visualizations, statistical machine learning algorithms, data analysis and modeling, and proof-of-concept prototypes. Professional development skills such as collaboration and written/oral communication are heavily emphasized.

To learn more about partnering opportunities with Carnegie Mellon and Statistics & Data Science, please feel free to contact Rebecca Nugent (rnugent AT stat.cmu.edu) and/or Jessie Albright (jfrund AT cmu.edu).

Fall 2022: Peter Freeman, Jamie McGovern

ACHD: Mon Valley Air Toxics Study
Allegheny County Health Department
S. Betko, M. Koo, M. Kwon (with Peter Freeman, Statistics & Data Science)
Poster Presentation
Characterizing Pittsburgh Parks Conservancy Donors
Pittsburgh Parks Conservancy
D. Hoque, E. Hu, D. Lu, A. Niu (with Peter Freeman, Statistics & Data Science)
Poster Presentation

Previous Partners: C.H. Robinson Worldwide, Inc, Black & Veatch, The NPD Group, The Principal Financial Group, CivicScience, TruMedia, Steady (App), Giant Eagle, Penguin Random House, Pack Up + Go, IKOS, ThermoFisher Scientific, PNC/Numo, USOPC, Optum

36-600 Overview of Statistical Learning and Modeling

This course is targeted to non-statistics graduate students at CMU. In their final project, teams of students utilize methods of EDA and statistical learning to analyze datasets. Their posters are linked to below. If you have any questions or comments about these posters, please send them to Peter Freeman (at pfreeman@cmu.edu), who will forward them to the appropriate student teams.

Fall 2022: Peter Freeman

Using Procedural Evaluations to Predict Hospital Ratings
A. Eswaran, J. Cervenak, J. Mirchandani, P. Kukkapalli, S. Mishra, Y. Dugar
Predicting the Length of Flight Delays
A. Cao, A. Li, Z. Li, M. Rodriguez Landron De Guevara, M. Wang, S. Yang
Civil War Prediction Using Statistical Learning Methods
B. Wallace, G. Kant, I. Malhotra, P. H. Le, S. Sarwari, Z. Palinrungi
Can Median House Value be Predicted Based on Population Characteristics?
A. Dey Sarkar, E. Moore, I. Iacob, O. Oladeji, T. Jaffe, S. Pitell
Predicting Galaxy Masses in SDSS Using Emission Data
T. Kondareddy, J. Miner, Q. Peng, K. Sharma, W. Frieden Templeton, S. R. S. Vasanthawada
Using Environmental Factors to Predict COVID-19 Vaccine Acceptance
J. Kang, A. Kroll, T. Ling, P. T. Ngoc, J. S. Yoo, A. Zilberberg
Predicting Hate Crime Rates Following the 2016 Election
T. Ayers, J. Ha, B. Tusmagambet, G. White, Z. Wu