Undergraduate Research Showcase Showdown
Join us in highlighting and celebrating Spring 2021 Carnegie Mellon Statistics & Data Science Undergraduate Research and Capstones! Every semester we have several divisions, each tackling different kinds of statistics & data science challenges. Click on each division's tab to learn more about the program and this semester's projects. Previous semesters are also available.
On Wednesday, May 12th from 9am-10am EDT, students will be virtually available to answer questions about their projects via this website.
36-315: Statistical Graphics & Visualization (groups; website interaction only)
36-490: Advanced Undergraduate Research (groups)
36-493: Sports Analytics (groups)
36-497: Corporate Capstone/Data Science Experiential Learning (groups)
Senior Honors Theses (individual)
Zoom links will be made available on this site from 9-10am EDT. Sessions will be moderated; any participants using inappropriate language or creating an unprofessional or unsafe environment will be removed and blocked at the discretion of the moderator.
36-315 Statistical Graphics & Visualization
As part of their final project, teams of students build and use interactive statistical graphics & visualizations as part of student-driven research studies. Feel free to take a look at this spring's projects below.
Spring 2021: Professor Zach Branson| |
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| Marketing Analytics Victoria Chang, Sahara Moses, Joanna Yao |
Project |
| Basketball Game Analysis Sean Ream, Daniel Ng, John Tjards |
Project |
| Analysis of Crime in North Carolina Abigail Glaser, Ananya Krishnan, Shamini Wadhwani |
Project |
| World Happiness in 2021 Hugh Cheon, Megan Li, Max Yeh |
Project |
| NYC Airbnb Listings in 2019: Determining Factors that Affect a Listing's Price Jonathan Cao, Thomas Choi, Rahul Khare |
Project |
| Suicide Rate: Visualization and Interpretation of Factors that Play Into Suicide Rates Harine Choi, Victoria Im, Iris Miao, Pamela Yang |
Project |
| Data from The Guardian on Police Killings Donald Dinerman, Max Gamerman, Sean Pogorelc |
Project |
| A Deeper Look into the Academic Performance of Portuguese Students Shivam Desai, Aditya Gandhi, Ashwin Konkimalla, Agam Kumar |
Project |
| Information on Police Killings and Census Data in 2015 Darren Kopa, Harrison Lian, Leon Lu |
Project |
| Examining Data on Cereal Nutrients Bonnie Chan, Elsie Goren, Amanda Hong, Jinah Moon |
Project |
| The Legend of Branson: The Final's Awakening (Video Game Data) Angela Huang, Lucy Liu, James Pak, Yuxi Shi |
Project |
| Alcohol Consumption of High School Students in Portugal Ingrid (Ying) Hu, Steve Wong, Lijing Yao |
Project |
| Serving Up Visualizations: Examining Tennis Data Samarth Gowda, Daniel Liang, Eric Liu, Abhinav Maddineni |
Project |
| Netflix Movies and TV Shows Analysis Catherine Du, Aiyana Huang, Caitlin Huang, Tara Prakash |
Project |
| Say Their Names, Remember Their Lives: Fatal Encounters with Police Anna Niu, Asad Sheikh, Vivian Sun, Wiliam Zeng |
Project |
| Visual Analysis and Prediction of Stroke Yitian Hu, Cynthia (Xiyue) Huang, Megan (Jui-En) Yang, Yizhi Zhang |
Project |
| Google Play Store Apps Richard Kang, Sean Tae Kim, Ash Lanith, Arvind Nachiappan |
Project |
| Olympic Athlete Participation from 1896 to 2016 Sophia Cordova, Dillan Gajarawala, CJ May, Nadina Popoviciu |
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| The Movie Database Lawrence Han, Harrison Lee, Kyra Low, Hal Rockwell |
Project |
| Exploring International Currencies Leandro Lopez Leon, Vincent Liu, Patrick Liu |
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| Bank Customer Churn Yumi Sato, Ashley Wang, Andy (Weichu) Yu, Kezhen Zhao |
Project |
| A Study of Kickstarter Projects Tzen-Hao Ng, Francis Ng, Sabrina Romansky, Fiona Thendean |
Project |
| Analysis of Tweets about COVID-19 Vaccines Dylan Chou, Jihyo Chung, Rebecca Jean-Lewis, Adrian Kelesoglu |
Project |
| Analysis of Successful Instagram Posts Neha Chintamaneni, Shelley Kim, Michael Lim |
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| Hotel Bookings Andrew Butler, Graham Eversdem, Xiao Shen, Keaton Tam |
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| Video Game Sales Diane (Xinyu) Hu, Matthew Lewis, Lauren Tan |
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| Exploring Engagement Rate of Instagram Posts Carol (Yuqian) He, Spoorthi Jakka, Chloe (Hyun Jee) Sung |
Project |
36-490 Undergraduate Research
At Carnegie Mellon Statistics & Data Science, there are multiple opportunites to engage in team research projects. 36-290 Early Undergraduate Research is a fall course targeted for sophomores who do semester-long projects in small groups, concentrating on learning the research process with an introduction to statistical machine learning methodology. 36-490 Undergraduate Research is an advanced research course that happens both semesters 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. Both courses heavily emphasize professional skills development including collaboration and both written and oral communication.
In the spring, we only have advanced-level research projects; feel free to explore the projects below.
Spring 2021 36-490 : Peter Freeman, Zach Branson, Rebecca Nugent
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| Tracking Political Sentiment on Cold War China in Congressional Speeches (with Dani Nedal) Haidan (Eden) Hu, Yanyu (Mason) Lin, Zachary Novack |
Poster | Presentation | Zoom |
| PHIGHT COVID: The Impact of Non-Pharmaceutical Interventions (with Seema Lakdawala) Melody Ma, Alvin Pan, Tracy Wang, Ben Yuan |
Slides Graphs | Presentation | Zoom |
| Matching Sustainable Development Goals to CMU Course Offerings(with Alexandra Hiniker) Lavanya Chawla, Jiayue Guo, Peter Wu, Chloe Yan |
Poster | Presentation | Zoom |
| Predicting Banking Crises (with Daniel Hansen) Andrew Furlong, Yanyu Tang, Kyle Wagner, Zhenxin Zhang |
Poster | Presentation | Zoom |
| Leveraging Information in 10-K Filings to Analyze Risk Trends (with Lars-Alexander Kuehn and Pierre Liang) Michael Chen, Dylan Chou, Spoorthi Jakka, Alden Pritchard |
Poster | Presentation | Zoom |
36-493 Sports Analytics
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, 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.
Feel free to explore the projects below.
Spring 2021 36-493: Brian Macdonald
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| Golf shot location data acquisition and analysis (with CMU Women's and Men's Golf) Aashai Avadhani, Raymond Li, David Yuan |
Slides App | Presentation | Zoom |
| Softball pitch data acquisition and analysis (with CMU Softball) Taylor Cammarata, Steven Lu, Max Schussler |
Slides App | Presentation | Zoom |
36-497 Corporate Capstone
At Carnegie Mellon Statistics & Data Science, students can apply to participate in our data science experiential learning program: 36-497 Corporate Capstone. In this course, 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@stat.cmu.edu), Michael Harding (michaelharding@cmu.edu), and/or Adam Causgrove (causgrove@cmu.edu).
Feel free to explore the projects below.
Spring 2021 36-497 : Rebecca Nugent, Peter Freeman
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| Optum: COVID-19 Modeling Performance Amy Lopata, Steve Wong, Qiuyi (Carol) Yin |
Slides App | Presentation | Zoom |
| PNC/numo: Exploring and Optimizing the Use of Card-Linked Offers Yanbing (Elena) Chen, Yezhen (Elena) Gong, Carl Yang, Sebastian Yang, Lijing Yao |
Slides | Presentation | Zoom |
| NPD: Predicting Wearer Demographics via Integrated Data Sources Deborah Blank, Nickolas Sanderson, Xiaolin (Alisa) Sima, Andrew Ye |
Slides | Presentation | Zoom |
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
Senior Honors Thesis and Independent Study
Qualified Statistics & Data Science seniors can apply for the Dietrich College Senior Honors Thesis Program; these year-long projects are supervised by a faculty member and often involve methodological development in a real-world application context.
Independent Studies can happen at any level but are most common for juniors and seniors. They can be one or multiple semesters and typically involve exploring a research topic through advanced statistical modeling and data analysis. Students find a project through conversation with faculty who often have expertise in the area of interest.
Feel free to explore the projects below.
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| Understanding Multilateral Sanctions using a Multi-way Network Approach Sana Lakdawala (with Nynke Niezink) |
Slides | Presentation | Zoom |
| Deaths in Pennsylvania Prisons Zhenzhen Liu (with Robin Mejia) |
Slides | Presentation | Zoom |
| Association between Prescription Propensity and Medicare Patient Panels' Mean HCC Risk Scores Carlo Duffy (with Mark Patterson and Rebecca Nugent) |
Slides | Presentation | Zoom |
| Measuring the Degree of Gentrification in Texas Parvathi Meyyappan (with Zach Branson) |
Slides | Presentation | Zoom |
| Goals in Misinformation Timothy Kusuma (with Stephen Broomell) |
Slides | Presentation | Zoom |
| Should Your Growth Mindset be Fixed? Examining the Opportunity Costs of Adopting a Growth Over a Fixed Mindset QX Teo(with Danny Oppenheimer) |
Slides | Presentation | Zoom |