Undergraduate Research Showcase Showdown
Here we highlight and celebrate the Fall 2020 Carnegie Mellon Statistics & Data Science Undergraduate Research and Capstones! In December 2020, we had four divisions, each tackling different kinds of statistics & data science challenges. Click on each division to learn more about the program and the semester's projects.
36-290/490 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.
Feel free to explore the projects below.
Fall 2020 36-290: Peter Freeman
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| Classifying ROSAT X-ray Sources Lauren Janicke, Janice Lee, Peicheng Qiu, Jenny Shan |
Poster | Presentation | Zoom |
| Predicting BCG Mass from Brightness and Shape Athena Dai, Megha Raavicharla, Bin Zheng |
Poster | Presentation | Zoom |
| Predicting Galaxy Mass from Sky Coordinates and Brightness Neha Choudhari, Cherie Hua, Eric Huang, Joanna Yao |
Poster | Presentation | Zoom |
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| PHIGHT COVID (with Seema Lakdawala) Melody Ma, Alvin Pan, Tracy Wang, Ben Yuan |
Poster | Presentation | Zoom |
| Understanding Pesticide and Salt Effects on Developmental Neuroplasticity in Amphibians (with Sarah Woodley) Henry Ma, Alyssa Montgomery, Erica Oh |
Poster | Presentation | Zoom |
| Understanding US-China Relations: Text Analysis on Congressional Speeches (with Dani Nedal) Sylvia Ding, Daniel Liang, Angela Zheng |
Poster | Presentation | Zoom |
| Exploring Trends in CMU Grant Data (with Huajin Wang, Sarah Young) Kyra Balenzano, Melissa Dy, Michael Li, Veda Lin |
Poster 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.
Fall 2020 36-497 : Rebecca Nugent, Peter Freeman
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| Optum: Modeling COVID-19 in UK Yuhao Chen, Andrew Hong, Lily Qiao, Kezhen Zhao |
Poster | Presentation | Zoom |
| ThermoFisher Scientific: Transforming Unstructured Product Data Grace Bae, Anna Tan, Peter Wu, Chenxiang Zhang |
Poster | Presentation | Zoom |
PNC/numo: Exploring and Optimizing the Use of Card-Linked Offers
Elena Chen, Jiyeon Lee, Daniel Weiss, Carl Yang
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
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. These honors thesis students are showing their mid-point progress; join us in May for the final results!
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| Understanding Sanctions with 3-Way Networks 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 |