Undergraduate Research Showcase

Join us in highlighting and celebrating Spring 2025 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.

36-295: Independent Study
36-297: Early Undergraduate Research
36-315: Statistical Graphics & Visualization
36-460/660: Special Topics: Sports Analytics
36-490: Undergraduate Research
36-493: Sports Analytics Capstone
36-497: Corporate Capstone Project

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

Spring 2020 Fall 2020
Spring 2021 Fall 2021
Spring 2022 Fall 2022
Spring 2023 Fall 2023
Spring 2024 Fall 2024

Each spring, we, in association with Meeting of the Minds, run the Statistics Poster Competition. The winners for the 2024-25 academic year are:

1st Place TITLE
by AUTHORS
2nd Place TITLE
by AUTHORS
3rd Place TITLE
by AUTHORS

36-295 Independent Study

36-295 Independent Study is an early undergraduate independent study course, in which a student collaborates with researchers and scientists in other disciplines to solve problems via data processing, visualization, and analysis.

Spring 2025: Peter Freeman

Projects
The Long-Term Stability of Bird Populations in Monteverde
(with Deborah Hamilton - Monteverde Institute)
Mekhi Hernandez (with Peter Freeman - Statistics & Data Science) 		Poster

36-297 Early Undergraduate Research

36-297 Early Undergraduate Research is an research course for sophomores and juniors. Groups of students collaborate with researchers and scientists in other disciplines to solve problems via data processing, visualization, and analysis.

Spring 2025: Peter Freeman

Projects
Pittsburgh's Parks in Action: Understanding Public Engagement with PPC
(with April Roberson & Alana Wenk - Pittsburgh Parks Conservancy)
A. Yan, F. Zhang, N. Rayce, T. Paek (with Peter Freeman - Statistics & Data Science) 		Poster

36-315 Statistical Graphics & Visualization

As part of their final project, students develop their own research study featuring a variety of statistical graphics and visualizations. See this spring's project reports below.

Spring 2025: Ron Yurko

Projects
Demographics of 2015 U.S. Police Killings
M. Xie, A. Brownlee, M. Pilson, J. Lai 		Project
Pokemon
R. Song, G. Morris 		Project
Baseball Analysis
E. Lee, W. Cheng, J. Wu, L. Yao 		Project
Unlocking Opportunity: How Income, Scores, and Location Shape Access to Elite Colleges
Z. Saif, M. Cody, L. Hurwitz, A. Jawahar 		Project
Global Cybersecurity Threats
H. Manikandamurthy, M. Saraf, F. Hamdan, S. Lathi 		Project
Nuclear Explosions
K. Satluri, J. Chen, O. Taebunyanuphap 		Project
A Data-Driven Look at Two Decades of Super Bowl Ad Strategies
S. Kim, C. Tang, B. Xu 		Project
Cards, Credit, and Churn - Discovering Behavioral and Demographic Factors Across the Credit Cards
A. Chen, J. Ye, H. Hao 		Project
Lifestyle Factors and What They Tell Us About Sleep Health
V. Sui, R. Orth, D. Heraclio 		Project
Global Food Wastage
M. Lei, S. Mou, Z. Liu 		Project
An Analysis of the 1994 US Census Income Data
V. Karnala, A. Paliwal, E. Shau 		Project
Building a Champion Level NBA Team!
C. Tuttle, A. Hou, S. Urabe 		Project
Summit Strategies: Factors Shaping Success and Safety in Himalayan Expeditions
Z. Li, J. Chen, C. Wang, F. Zhai 		Project
Cheeses
E. Song, A. Singh, G. Muthusamy 		Project
Coffee Bean Sales
S. Wu, J. Zheng, J. Wang 		Project
Instagram Ocean Destinations
S. Krishnaswamy, E. Sanchez, M. Grugan 		Project
Identifying Trends in Patients with Brain Tumors
S. Srinivasan, A. Lee, A. Yang, N. Mesa-Cucalon 		Project
Musical Features and Chart Success: Insights from the Most Streamed Spotify Songs in 2023
N. Nanduri, R. Stamm, C. Xu 		Project
Netflix Movies and TV Shows
J. Wu, S. Zheng, T. Chen, Y. Ren 		Project
Global Water Consumption
A. Lalwani, A. Mane, D. Shah, E. Forcucci 		Project
Sleep Duration VS GPA for Freshmen
T. Zhang, B. Yang, A. Yang, S. Ma 		Project
Report of Research on Polling Errors in Political Polls
F. Sun, E. Enkhbold, A. Palmer, J. Scharpf 		Project
Armed Conflict Location and Event Data
A. Plummer, A. Liu, Y. J. Kim, T. Paek 		Project
Exploring Lifestyle and Demographic Drivers of Obesity
A. Nkwoji, S. Cheng, K. Maddipatla, H. Wu 		Project
Measuring Joy: What Makes a Nation Happy?
B. Xiao, J. Lee, S. Parikh, M. Chen 		Project
Investigating the Role of Clinical Variables in Surgical Outcomes
S. Putta, V. Walia, E. Helms, L. Hazard 		Project
The Movie Database
C. MacSwain, M. Olorunsola, J. Sun, Q. Zhao 		Project
Substance Use Treatment Admissions
V. Mazeeva, A. Sultan, H. Amon, K. Wu 		Project

36-460/660 Special Topics: Sports Analytics

36-460/660 Special Topics: Sports Analytics is a methods course for juniors, seniors, and masters students that covers fundamental topics and techniques in sports analytics. Students develop their own research projects in the course across a wide variety of sports and research questions, with written reports that you can view below.

Spring 2025: Ron Yurko

Projects
Baseline Bias or Net Gain? (1st Place: Undergraduate Competition)
J. Qiu, A. Koul, A. Soetanto, K. Rock 		Project
What’s Driving the Decline in Batting Averages in Major League Baseball? (Tied 1st Place: Graduate Competition)
S. Archie, J. Cohen, W. Dudding, R. Shah 		Project
Leveling the Court: Modeling Shot Success Across Men’s and Women’s Professional Basketball
J. Jeckering, A. Manglik, A. Schmid, A. Wang 		Project
World Cup Fan Base Impacts
A. Gupta, S. Srinivasan, A. Ramanathan, A. Heddadji 		Project
Modeling Tennis Match Outcomes for Grand Slams
S. Danthurthy, R. Kim, A. Robert, A. Suresh 		Project
Momentum Makers: Value Beyond Salary and Stats
E. Yang, D. Wei, C. Zhou 		Project
Winning Factors in Volleyball
J. Dong, J. Li, A. Zhang, J. Zheng 		Project
Expected Passer Rating in the NFL: Contextualizing QB Performance Using Tracking Data (Tied 1st Place: Graduate Competition)
A. Williams, A. Wei, J. Lauer, N. Jacimovic 		Project
Outdrive to Survive
S. Banerjee, W. S. Kang, N. Krishnan, V. Seifi 		Project
The Effect of Missed Calls on Pitching Strategies
B. Yi, J. Shah, G. Morris 		Project
Leveraging Random Effects to Exploit Market Inefficiencies in UFC (Runner-Up: Undergraduate Competition)
J. Nichols, A. Mantro, V. Samuel 		Project
Shooters Shoot... But Should They Have Passed?
S. Glick, E. Wang, G. Yim 		Project
Skating around the Figures: Analyzing Factors that Impact Figure Skating Scores
M. Pilson, J. Pascale, D. Blum, L. Klucinec 		Project
NBA Scoring Success
E. Buera, E. Yoon, L. Liang, R. Wetten 		Project
Impact of Load Management on NBA Player Performance
D. Chen, E. Huang, C. Ou, S. Parikh 		Project
Spatial and Positional Effects on Defensive Events in the NBA
N. Mamo, S. Gibbs, P. Lakhani, R. Mulcahy 		Project
Factors Contributing to Shot-Making Probabilities in the NBA
S. Yarger, Y. Luo, J. Liang, S. Manda 		Project
Impact of Turnovers in Early Season vs. Late Season Win-Rate In the WNBA
C. Connolly, P. Yang, P. Harish, P. Moffatt 		Project

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.

Spring 2025: Zach Branson & Joel Greenhouse

Projects
Trends in Children's Book Reviews: Language and Sentiments
(with Rebekah Fitzsimmons - Heinz College)
E. Ozince, R. Sun, V. Sui (with Zach Branson - Statistics & Data Science) 		Poster
Cell-Signaling in Streptococcus Pneumoniae (SPN)
(with Corine Jackman Burden - University of Maryland Baltimore County and N. Luisa Hiller - Biological Sciences)
J. Deng, C. Zhang, B. Hou (with Joel Greenhouse - Statistics & Data Science) 		Poster
Classifying Newsbooks from London, 1649
(with Chris Warren - English)
B. Leng, C. Liu, L. Stampfli (with Joel Greenhouse - Statistics & Data Science) 		Poster
Urban Well-Being Through Data: a Pittsburgh Study
(with Gabriella Gonzalez - Richard King Mellon Foundation and Kristen Kurland - Heinz College & Architecture)
B. Liu, K. Mawalkar, S. Yao (with Joel Greenhouse - Statistics & Data Science) 		Poster
Injury Severity Analysis of Traffic Incidents
(with Leonard Weiss - University of Pittsburgh)
D. Gao, C. Jestin (with Joel Greenhouse - Statistics & Data Science) 		Poster

36-493 Sports Analytics Capstone

Carnegie Mellon and the Department of Statistics & Data Science is actively involved in sports analytics from cutting-edge research to conferences to student clubs to summer programs to outreach initiatives. In 36-493: Sports Analytics Capstone, we partner with the Carnegie Mellon Athletics Department on a set of ground-breaking projects that integrate previously unlinked, disparate data sets to build interactive applications and statistical models that can be used by coaches and staff to better understand and predict student-athlete performance. To learn more about our general Carnegie Mellon Sports Analytics work, please visit the CMSAC site.


Spring 2025: Ron Yurko

Projects
"Put Me in Coach!": An Analysis of Fatigue
(with CMU Football)
C. Ramage, M. Mathur, J. Madan (with Ron Yurko - Statistics & Data Science) 		Poster
Optimizing Game Schedule for CMU Women's Volleyball Team
(with CMU Women's Volleyball)
H. Sun, Y. Du (with Ron Yurko - Statistics & Data Science) 		Poster

36-497 Corporate Capstone Project

36-497 Corporate Capstone Project 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).

Spring 2025: Zach Branson & Joel Greenhouse

Projects
Analysis of User Data at Augie Studios
Augie
A. Gupta, J. Wang Zheng, M. Cheong
(with Zach Branson - Statistics & Data Science and Coleman Isner - Augie) 		Poster
Analysis of User Behavior on a Children's Video Book Platform
Vooks
M. Chen, A. Menon, H. Hayes
(with Zach Branson - Statistics & Data Science and Ryan Shary - Vooks) 		Poster



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, Allegheny County Health Department, Pittsburgh Parks Conservancy, Dexcom