Returning for its second year, the Undergraduate Computer and Data Science Research Fair will exhibit original research being conducted by undergraduate students across Columbia. This student-led fair provides undergraduates with a unique opportunity to share their research interests and experiences, and to receive feedback from faculty and industry experts. The fair is hosted by the Data, Media and Society Center at the Data Science Institute.

Find all details on the fair website:

Fair Website

Event Information

Date: Thursday, November 2, 2023 (5:00 PM – 8:00 PM) – IN-PERSON

Location: Carleton Commons, Mudd Building, Columbia University

Tracks

This year’s fair is inspired by the way that interdisciplinary research brings together ideas from different fields to generate new insights and perspectives. This is expressed in the three themes —  Create, Change, and Converge — which highlight the full lifecycle of scientific research from initial idea to iteration and implementation across diverse domains. Student projects will be organized across these three tracks.

Create – Experiment: There are a large number of current methodologies and perspectives for analyzing interdisciplinary computer and data science problems. As computer processing evolves and data generation accelerates, new modes of thought are emerging about how to push research forward. What state-of-the-art technologies are possible thanks to these rapid technological developments? This track is focused on novel, unconventional, and/or experimental systems and tools.

Change – Iterate and Transform: Innovation creates a critical need for iteration. How can we predict and troubleshoot potential problems, and learn from the past to ensure that new technologies are produced responsibly, ethically, and with reduced embedded biases? This track is focused on projects that build upon foundational knowledge from different disciplines to change and advance how data and computer science are practiced and applied.

Converge – Applications of Computer and Data Science: Data and computer science have applications across nearly every discipline, from finance and healthcare to public policy and the social sciences. How can researchers best collaborate across disciplines? This track is focused on projects whose implementations and applications may be principally outside of the computer and data science sectors.