Spring 2022 Capstone Presentations
Tuesday, May 3, 2022
10:00 am - 1:00 pm
Tuesday, May 3, 2022
10:00 am - 1:00 pm
The Capstone course provides a unique opportunity for students in the M.S. in Data Science program to apply their knowledge of the foundations, theory and methods of data science to address data driven problems in industry and government. In this course, student teams work with representatives from DSI Industry Affiliate companies and Columbia faculty on semester-length data science projects. The resulting projects synthesize the statistical, computational, engineering and social challenges involved in solving complex real-world problems.
Join our event to explore the projects, see demos, and meet with the participating students and mentors. Find project themes and companies below.
DSI Industry Affiliates have access to Capstone projects following the event. Please reach out to datascience@columbia.edu with any questions about the Capstone program.
2:00 PM: Join the Event. The event will be held on Gatherly, an interactive virtual platform where guests can walk around and meet new people, just like in real life. Attendees can navigate Gatherly floors, designed based on Capstone project topic area, where students will stand by their project to give short presentations and answer questions.
2:05 PM: Introduction from Capstone Faculty. Learn more about the Capstone program and its impact across the Data Science Institute and Columbia University at large.
2:10 PM: Presentations. Presentations will be open until 5:00 PM ET; guests are welcome to float in and out of Gatherly floors to see all of the demos, or focus on exploring projects within areas of interest.
5:00 PM: Event ends.
Access the DSI help desk, where representatives of our student services team will be available to assist you. Move your mouse to the elevators, where you can head to the floors to see the student projects.
POSTER 1: Who Gets Access to the Internet?
POSTER 2: Visualizing Fleet Data for Operational Change
POSTER 3: ML Techniques for High Precision and High Explainability
POSTER 4: Transfer Learning for Control
POSTER 5: Quantifying Contributions of Different Features to Unfairness in AI
POSTER 6: Table Extraction via Eye Gaze Tracking
POSTER 7: Building Speech Emotion Recognition Systems for Low Resourced Languages
POSTER 8: Exploring Equity Markets Closing Auction
POSTER 9: Hierarchical Forecasting for External Cost
POSTER 10: Exploring and Predicting Loss of Exclusivity and its Business Impact
POSTER 11: Topic Directionality in Financial Statements
POSTER 12: Social Media Product Quality Insights Generation
POSTER 13: Performing NLP Tasks on Unstructured Financial Documents
POSTER 14: Interest Diversity and Brokerage in Networks: A Case Study of Twitter
POSTER 15: Data-driven Competitor Product Identification and Ranking
Sining Chen, Adjunct Professor of Industrial Engineering and Operations Research, Columbia University
Adam S. Kelleher, Adjunct Assistant Professor of Computer Science, Columbia University
Katie Jooyoung Kim (Course Assistant)