Undergraduate Research Showcase Showdown

Join us in highlighting and celebrating Fall 2023 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: Undergraduate Research (groups)
36-497: Corporate Capstone Project (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 Fall 2022
Spring 2023

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 spring's project posters below.

Fall 2023: Spencer Koerner

Projects
Statistical Analysis of International Football Results from 1872-2023
Mussa Abbo, Michael Fourchy, Fengyi Wang, Helena Yu 		Project
An Analysis of Quality of Life Across Counties and States in the United States
Isha Agrawal, Neri Jung, Andy Lee, James Pascale 		Project
Analyzing Childcare Costs: Impact on Disadvantaged Families
Emaan Ahmed, Jeffrey Chen, Kaylin Li, Ruoxi Yang 		Project
US Economy vs. Rest of the World Analysis
Abhinav Arya, Eshaan Joshi, Mihir Mathur, David Rotunno 		Project
Housing Data
Justin Austin, Dennis Chen, Kavya Iyer, Neel Mandapati 		Project
Traffic Stops in Hartford, Connecticut
Yeonji Baek, Anika Joshi, Catharine Ramage, Natalie Sarabosing 		Project
2021-2022 European Leagues Player Stats
Anusha Bhat, Nandini Neralagi, Noelani Phillips, Marcos Pi Marrero 		Project
Exploring Factors Influencing Presence of Diabetes in Patients
Anishka Bompelli, Ryan Driscoll, Noel Mamo, Sathwika Manda 		Project
Sleep Is Significant
Aidan Booher, Christian Lanuza, Esha Rao, Breana Valentovish 		Project
Beyond the Wilderness: Determining Success and Trends in Alone
Jordan Brown, Ananya Manglik, Tasnim Rida 		Project
A Statistical Investigation on College Characteristics Impacting Undergraduate Students' Post-Graduation Income
Daiyan Chen, Peter Gui, Tom He, Donna Huang 		Project
Airbnb Data Analysis - New York City
Jamie Chen, Yunseong Jung, Chang Bum Lee, Michelle Zhu 		Project
Depression and Health Over Time in a Sample of Young Adults
Phoebe Chen, Zane Kelley, David Phan, Nicole Sorensen 		Project
An Analysis of NYC's Good Food Purchasing Program
Victoria Chen, Lauren Chin, Prina Doshi, Hannah Hayes 		Project
Decoding Political Stability Through World Bank Data
Levi Crosby, Claire Liu, Vivien Lu, Clement Ou 		Project
Analysis of Trends in US Public Transit Ridership Data
Matthew Dai, Robert Dai, Steven Liu, Charlie Murphy 		Project
Global Economic Freedom
Nisha Fernandes, Jessica Liu, Akash Mankude, Helen Zhao 		Project
Factors Influencing Housing Prices in Ames, Iowa
Ayush Gupta, Krishna Singla, Aarav Tanti, Willy Yeh 		Project
Characteristics of Police Violence and Accountability
Kyle Hynes, Adrian Liu, Sydney Tallan 		Project
Analyzing the Housing Market in Ames, Iowa to Predict House Prices
Chong Lee, Haomin Ng, Celine Park, Andrew Wang 		Project
The Effectiveness of Telemarketing Campaigns for a Portuguese Bank
Rita Liu, Catherine Mathews, Katherine Shaw, Stephanie Xu 		Project
Household Habits Die Hard: Customer Spending on Family- and Individual-Levels
Benjamin Lu, Jianing Shi, Jackie Wang 		Project
Identifying Evolution in the NBA
Ryan Mulcahy, Otto Sharples, Rick Zhang, Stephen Zhong 		Project
Factors Influencing Adult Language Learning
Stanley Ou, Andy Ouyang, James Wang 		Project
Popularity of Mashable Articles
Marco Rayner, Jin Song, William Wei, Ryan Zhang 		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.


Fall 2023: Eli Ben-Michael, Cosma Shalizi

Projects
ML-based US Stock Return Prediction and Asset Allocation
(with Lars-Alexander Kuehn - CMU: Tepper School of Business)
Lucy Hu, Wenhan Li, Tianze Shou, Mia Zhang (with Eli Ben-Michael & Cosma Shalizi - Statistics & Data Science) 		Poster Presentation
Analyzing the Evolution of Children's Literature: A Comparative Study of Lists by Caroline Hewins and Anne Carroll Moore
(with Rebekah Fitzsimmons - CMU: Heinz College)
Vernon Luk, Patrick Phelan, Yasemin Rees (with Cosma Shalizi - Statistics & Data Science) 		Poster Presentation
Analysis on Sensorimotor Adaptation and Learning Performance
(with Jonathan Tsay - CMU: Psychology)
Noah Gonzalez, Lucas Yi, Aoran Zhang, Helen Zhang (with Cosma Shalizi & Eli Ben-Michael - Statistics & Data Science) 		Poster Presentation
Detecting Premature Heartbeats in ECG Data
(with Katharyn Mitchell - Cornell: College of Veterinary Medicine)
Prakruthi Pradeep, Sabrina Rodriguez, Xinyi Ke (with Ron Yurko - Statistics & Data Science) 		Poster Presentation
Optimizing Medal Count for the US Artistic Gymnastics Teams at the Paris 2024 Olympics
(with USOPC)
Anusha Bhat, Sarah Li, Shivani Ramalingam (with Ron Yurko - Statistics & Data Science) 		Poster Presentation

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).

Fall 2023: Eli Ben-Michael, Cosma Shalizi

Projects
Modeling Performance with CGMs Using Device Specifications
Dexcom
Ruiwen Liu, Wenyuan Shen, Zoë Thorpe (with Cosma Shalizi - Statistics & Data Science and Wils Corrigan - Dexcom) 		Poster Presentation
Investigating the Relationship Between Dexcom Clarity Notification Settings and Change in Users' Time-in-Rang Over 90 Days
Dexcom
Annika Lee, Xiaohan Liu, Victor Wen (with Eli Ben-Michael - Statistics & Data Science and Mark Derdzinski - Dexcom) 		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, Allegheny County Health Department, Pittsburgh Parks Conservancy

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 2023: Peter Freeman

Projects
Factors Impacting US COVID-19 Vaccine Adoption: A Statistical Exploration
H. Akhbariyoon, A. Jawali, W. Mishra, A.-T. Pham, A. Ananda Rao 		Poster
Classifying Wine Quality
K. Kedia, M. Nagyal, E. Ouanemalay, K. Sanchez, N. Uzunlar 		Poster
Utilizing Socioeconomic Factors to Predict the Occurrence of a Civil War in a Country
N. Chinta, S. Lee, S. Paradkar, S. Selvarajan, U. Tash 		Poster
Utilizing Machine Learning Methods for Hate Crime Prediction in the USA
J. Daley, J. Jung, K. Kim, S. Manikantan, H. Styles 		Poster
Utilizing Demographics and Home Features to Predict Median House Value
J. Beltran, G. Chabra, K. Manchala, R. Varanasy, R. Wang 		Poster
Predicting Galaxy Mass from SDSS Emission Data
A. Garcha, C. Ji, A. Naik, A. Vetturini 		Poster
Predicting U.S. Hospital Ratings with Performance Data
F. Menares Paredes, X. Wang, F. Yuan, J. Zhu 		Poster
Predict Diamond Price by Its Properties
X. Chen, R. Shen, R. Tang, A. Wu 		Poster