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Future Forward: AI and the Next Decade
Columbia Undergraduate Data Science and AI Research Fair 2025
This event was held on Thursday, November 6 (5:00 PM – 7:30 PM) | Mudd Building, 4th Floor | Carleton Commons & 407 Suite
Over the past three years, AI has rapidly moved into classrooms, workplaces, and everyday life. Yet beyond the hype lies a deeper question: what kind of future do we want to build? As AI technologies become mainstream, we collectively face opportunities, responsibilities, and risks.
This year’s theme invites students to look past short-term novelty and consider how AI might create lasting impact over the next 10 years. Projects may explore both the potential and the limitations of AI across technological, social, and ethical dimensions. The fair offers students a space to imagine possible futures and new processes, whether they are optimistic, cautious, or reflective. Students considered the following prompts as starting points to guide and inspire their submissions:
| If you could design one new AI application to truly improve human life by 2035, what would it be? | What does a positive future with AI look like? Who benefits from AI, and who is left out? | It’s 2035. Will AI be heavily legislated, or a free-for-all? What future do you want to see? |
| Could AI become a creative partner or even a friend? And what responsibilities would come with that? | How do we weigh the environmental and social costs of AI against its potential benefits? | How will AI reshape the skills needed for the next generation of students, workers, and leaders? |
| How can we build systems that are transparent, fair, and resilient against misuse? | What are the limits of automation? Where do we still need human judgment, creativity, and oversight? | How can we move beyond today’s AI hype to build tools and practices that last for the next decade of growth? |
Hosted by: Data, Media and Society Center at the Data Science Institute, Columbia University
Co-Sponsors: Department of Statistics, Arts and Sciences, Columbia University; Department of Computer Science, Columbia Engineering; Department of Computer Science, Barnard College; Vagelos Computational Science Center, Barnard College
Organizing Committee: Ann Chengying Li, Junior, Columbia Engineering; Alexa Kafka, Junior, Barnard College; Christine Li, Junior, Columbia Engineering; Daniel Alejandro Manjarrez, Junior, Columbia Engineering; Rebecca Frey, Junior, Barnard College; Rayhana Mouaouia, Barnard College; Riley Stacy, Junior, Barnard College; Isik Aysel Kiymac, Sophmore, Barnard College; Linda Mukarakate, Senior, Columbia College
2025 Thematic Tracks
In their exhibitor applications, students will select which of the following three thematic tracks is the best fit for their work. Students are welcome to submit projects that extend beyond these tracks, including current works in progress.
AI in Society: Human Futures: How AI is reshaping work, culture, politics, and everyday life. Projects may examine its effects on jobs and education, questions of trust and misinformation, or broader issues of ethics, access, and regulation. Students can also imagine speculative futures, considering how AI might evolve beyond current applications.
Responsible and Sustainable AI: Exploring the risks and responsibilities of scaling AI systems. Projects may address environmental impacts, security and privacy challenges, or issues of transparency and governance, with an eye toward best practices and safeguards that can guide AI’s responsible growth.
AI for Discovery and Innovation: How AI expands knowledge and sparks new ideas. Projects may explore research and scientific discovery, applications in fields such as health, climate, or finance, or creative prototypes that imagine the next generation of AI use cases. Students might also focus on advancing the technology itself, including developing more efficient and accurate AI models.
List of Exhibitors
Demonstrations
D01: StayNova
- Track: AI in Society: Human Futures
- Lead: Lionel Makumba Ebebe, Junior, Computer Science, Columbia Engineering; Minor: Applied Mathematics
D02: Cocode
- Track: AI in Society: Human Futures; Responsible and Sustainable AI
- Lead: Diya Nair, Senior, Computer Science, Barnard College; Minors: Philosophy, Policy
D03: Forking for Agentic Systems
- Track: Responsible and Sustainable AI
- Lead: Kevin Durand, Senior, Computer Science, Columbia Engineering
D04: RecallMate
- Track: AI in Society: Human Futures
- Lead: Khalifa Alsuwaidi, Sophomore, Computer Science, Columbia Engineering; Minor: Applied Mathematics
D05: Context-Aware Climate Intelligence: Integrating Community Data into AI Disaster Models
- Track: Responsible and Sustainable AI
- Lead: Justine Mach, Sophomore, Computer Science and History, Columbia College
- Team Members: Kenny Frias, Sophomore, Computer Science and Mathematics, Columbia College
Posters
P01: Technology, Legal Literacy, and Justice: Evaluating CourtClarity as a Tool for Defendant Empowerment
- Track: AI in Society: Human Futures
- Lead: Zoie Geronimi, Senior, Computer Science, Columbia College; Minor: African American Studies
P02: Predicting Gun Policy Stance on Social Media Platforms
- Track: AI in Society: Human Futures; Responsible and Sustainable AI
- Lead: Sultana Yeasmin, Junior, Computer Science, Columbia Engineering
- Team Members: Riley Stacy, Junior, Computer Science, Math, and Human Rights, Barnard College
P03: Investigating Snowpack-Shrub Interactions in the Arctic Tundra using Machine Learning and Process Models
- Track: Responsible and Sustainable AI
- Lead: Isabella Lu, Sophomore, Computer Science, Columbia Engineering
P04: From Headlines to Holdings: Deep Learning for Smarter Portfolio Decisions
- Track: AI for Discovery and Innovation
- Lead: Jinghe Zhang, Sophomore, Mathematics and Computer Science, Columbia College
- Team Members: Yun Lin, Senior, Economics and Mathematics, Barnard College; Jiawei Lou, Senior, Mathematics and Statistics, Barnard College
P05: Deciphering the Biophysical Determinants of Peptide-Binding Domain Specificity Using Machine Learning
- Track: AI for Discovery and Innovation
- Lead: Bridget Liu, Sophomore, Computer Science and Biochemistry, Columbia College
P06: Assessing AI-Enabled 3D Reconstruction for Architectural Workflows
- Track: AI for Discovery and Innovation
- Lead: Ardalan Tayebi, Senior, Architecture, Columbia College
P07: Phrase Importance Estimation for LLMs
- Track: Responsible and Sustainable AI
- Lead: Yuchen Huang, Senior, Computer Science, Barnard College
P08: AI Is Moving Fast, but the Law Isn’t
- Track: Responsible and Sustainable AI
- Lead: Claryssa Tarigan, Junior, Mathematics and Computer Science, Barnard College
P09: Hybrid CNN-Transformer Enables Accelerated and Robust Reconstruction of Ktrans
- Track: AI for Discovery and Innovation
- Lead: Akito Yamauchi, Sophomore, Electrical Engineering, Columbia Engineering
P10: Benchmarking OpenAI’s ChatGPT-4o for Clinical Case Entity Extraction
- Track: AI for Discovery and Innovation
- Lead: Miriam Zhu, Sophomore, Applied Mathematics, Columbia Engineering; Minor: Computer Science
P11: A Stochastic Algorithm for Initial Portfolio Sizing Under Depletion Time and Withdrawal Constraints
- Track: AI for Discovery and Innovation
- Lead: Shreeyans Dhamane, Sophomore, Computer Science, Columbia Engineering; Minor: Statistics
P12: Structure Over Signal: A Globalized Approach to Multi-relational GNNs
- Track: AI for Discovery and Innovation
- Lead: Amber Li, Junior, Mathematics-Statistics, Computer Science, Columbia College
- Team Members: Aruzhan Abil, Sophomore, Computer Science, Mathematics, Columbia College; Juno Marques Oda, Junior, Applied Mathematics, Columbia College
P13: Towards Quantitation of a Biomarker Signature to Distinguish NSCLC subtypes using a CNN-based Deep Learning Algorithm
- Track: AI in Society: Human Futures; AI for Discovery and Innovation
- Lead: Manan Vij, Senior, Computer Science and Biology, Columbia College
P14: An Approach for Systematic Decomposition of Complex LLM Tasks
- Track: AI for Discovery and Innovation
- Lead: Tianle Zhou, Senior, Computer Science, General Studies
P15: AI-ASSISTED BEHAVIORAL PHENOTYPING IN MICE UNDER STRESS
- Track: AI for Discovery and Innovation
- Lead: Erin Yoo, Freshman, Biology, Columbia College
P16: MedREK: Retrieval-Based Editing for Medical LLMs with Key-Aware Prompts
- Track: Responsible and Sustainable AI; AI for Discovery and Innovation
- Lead: Shujun Xia, Junior, Computer Science, General Studies
P17: Transcriptional Regulators of Plasticity in Colorectal Cancer Metastasis
- Track: AI for Discovery and Innovation
- Lead: Matthew Lee, Junior, Biochemistry, Columbia College; Minor: Computer Science
P18: A Comparative Study of LLM Prompting and Fine-Tuning for Cross-genre Authorship Attribution on Chinese Lyrics
- Track: AI for Discovery and Innovation
- Lead: Yuxin Li, Senior, Computer Science, Columbia Engineering; Minor: Industrial Engineering
- Team Members: Lorraine Xu, Senior, Computer Science, Columbia Engineering
P19: Demographic Predictors of Flu Vaccine Uptake Pre-, During, and Post-COVID-19
- Track: AI in Society: Human Futures
- Lead: Olivia Huang, Sophomore, Operations Research: Financial Engineering, Columbia Engineering
- Team Members: Jacob Hahn, Sophomore, Data Science, Columbia College; Athena Ke, Junior, Applied Mathematics, Barnard College
P20: PCA Cluster and Sentiment Analysis of New York City PPRs Demonstrate Typological Differences in Content and Success Rate of DOI Interventions in City Governance
- Track: AI in Society: Human Futures
- Lead: Ishaan Barrett, Senior, Urban Studies, Columbia College; Minor: Political Science
Sponsor Tables
S01: Masters in Data Science Program, Data Science Institute and Columbia Engineering
S02: Masters in Computer Science Program, Columbia Engineering
S03: Vagelos Computational Science Center, Barnard College
Join the Fair
Exhibitor Information
Helpful Information for Student Exhibitors
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To find your project at the fair, please see your poster or demo number and refer to the map below. Location: Carleton Commons, Mudd Building, 4th Floor
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AI in Society: Human Futures – Exploring how AI will transform work, culture, politics, and everyday life
Responsible and Sustainable AI – Focusing on the costs, risks, and responsibilities of scaling AI systems
AI for Discovery and Innovation – Where data science and AI expands the boundaries of knowledge and practice -
A variety of presentation formats will be on view at the fair, including:
Research Posters: Posters should fit on an easel. Foam boards in sizes 20×30 and 30×40 inches, along with easels, will be provided. If you require a different size, please bring your own foam board. Thumbtacks will be available. You may use the DSI poster template to format your poster.
Table-Based Demonstrations and Visualization Projects: This category includes digital projects displayed on a laptop or monitor, such as data visualizations, coding projects in action, or robotics demonstrations. A standard table and power outlet will be provided. Please bring your own laptop or monitor. You may also bring additional printed materials or small installations that fit on a standard table.
Startup and Entrepreneurial Projects: Startups may showcase a live demo of their technology (on a laptop or phone), a slide presentation about the company, or a physical product. A standard table and power outlet will be provided. Bring printed materials that highlight your company’s mission and brand.
Creative Tech, VR/AR Installations, or Performances: This category is open to a wide range of interpretations. We will work with you to find the best space for your project at the fair. Examples include VR/AR experiences, video games, installation art using data, sound experiments, and more. -
If you have any questions about the fair, please reach out to the DSI Events team: datascience@columbia.edu