Exhibitors & Event Agenda

Browse the research fair floor plan, list of exhibitors, and event agenda.

Undergraduate Computer and Data Science Research Fair

Event Date: Thursday, November 2, 2023 (5:00 PM - 8:00 PM)
Location: Carleton Commons - Mudd Building - 4th Floor

2023 Agenda

To give our undergraduate exhibitors time to meet and network, the fair will be split into two exhibitor groups. Projects will switch out in the middle of the fair.

This is a grazing style event, in which students will be posted by their projects. Attendees should feel free to engage with the work, ask questions, and provide helpful feedback.

Food and beverages will be available throughout the event - head across the hall to the DSI Suite for catering!

5:00 PM - 6:20 PM: GROUP A

6:20 PM - 6:30 PM: Intermission & Remarks

  • Susan McGregor, Data Science Institute

  • Undergraduate Organizing Committee

6:30 PM - 7:50 PM: GROUP B

7:50 PM - 8:00 PM: Awards!

  • Clifford Stein, Interim Director of the Data Science Institute, will issue the awards for Best in Each Track, and Best Overall

  • Joyce Robbins, Lecturer in Discipline in the Statistics Department at Columbia, will issue a special sponsored price “Best Use of Statistical Innovations”

Floor Plan

List of Exhibitors

GROUP A
5:00 PM - 6:20 PM

D01: Digital Twin of NYC as a Platform to Study Naturalistic Driver-Pedestrian Interaction

  • Submitted by: Ellie Yan, Barnard College, Junior, Computer Science

  • Track: Create

D02: Effective English Language Learner Tutoring with Language Models

  • Submitted by: Daniel Ben-Levi, Columbia College, Freshman, Computer Science + Cognitive Science

  • Track: Create

D03: Open Source Economics

  • Submitted by: Ramya Subramanian, Barnard College, Sophomore, Computer Science

  • Track: Converge

P01: TensorShop: A Visualization Suite to Facilitate Fast Matrix Multiplication

  • Submitted by: Jonah Stockwell, Columbia College, Freshman, intended Computer Science

  • Track: Change

P02: Enhancing Time Series Forecasting through a Hybrid LSTM-Transformer Model

  • Submitted by: Spencer Austin, SEAS, Junior, Operations Research: Analytics

  • Track: Change

P03: Assessing Look-Ahead Bias in Stock Return Predictions Generated By GPT Sentiment Analysis

  • Submitted by: Caden Lin, Columbia College, Sophomore, Mathematics, Computer Science

  • Track: Change

P04: Space Microbiology: Mitochondrial Transfer in Spaceflight Environments

  • Submitted by: Theodore Nelson, Columbia College, Senior, Computer Science

  • Track: Converge

P05: Designing Interactive Music Devices

  • Submitted by: Aditi Haiman, Barnard College, Senior, Computer Science

  • Track: Converge

P06: Identity Crisis? Self-identification versus Group Selection in the 2024 Taiwanese Presidential Elections

  • Submitted by: Sunny Fang, Barnard College, Junior, Data Science

  • Track: Converge

P07: MultiModal Hierarchical Classification (MMoCHi) for CITE-Seq

  • Submitted by: Will Specht, SEAS, Junior, Biomedical Engineering

  • Track: Converge

P08: ML Scan-Number Imputation

  • Submitted by: Youssf Hegazy, Columbia College, Junior

  • Track: Converge

P09: OpenDataVal: a Unified Benchmark for Data Valuation

  • Submitted by: Kevin Jiang, Columbia College, Senior, Mathematics + Statistics

  • Track: Create

P10: Telematics for Risk Scoring Cyber Attacks

  • Submitted by: Heeyun Kim, SEAS, Junior, Computer Science

  • Track: Create

GROUP B
6:30 PM - 7:50 PM

D01: Digital Twin of NYC as a Platform to Study Naturalistic Driver-Pedestrian Interaction

  • Submitted by: Ellie Yan, Barnard College, Junior, Computer Science

  • Track: Create

D02: Safe and Reliable Medical Records: Assessing the Robustness of openEMR

  • Submitted by: Angel Cui, Barnard College, Senior, Computer Science

  • Track: Create

P01: Prediction of Tumor RNA-Seq Expression from Whole-Slide Images using Deep Learning

  • Submitted by: Nolan Tremelling, SEAS, Senior, Electrical Engineering

  • Track: Converge

P02: Development of an Automated Pre-Processing Method for Feature Identification in Carotid Arterial Wall Motion Waveforms: A Feasibility Study In Vivo

  • Submitted by: Rahi Mitra, SEAS, Sophomore, Computer Science

  • Track: Change

P03: A Systematic Benchmarking and Optimization of Single-Nucleus RNA Sequencing for Biological Analysis

  • Submitted by: Manan Vij, Columbia College, Sophomore, Computer Science + Biology

  • Track: Converge

P04: A Custom-Designed App for Comprehensive Proteomics Analysis Reveals Exosomal Biomarker Candidates for Non-invasive Diagnostics in Non-Small Cell Lung Cancer

  • Submitted by: Manan Vij, Columbia College, Sophomore, Computer Science + Biology

  • Track: Converge

P05: Examining the Drivers of Irregular Migration out of Central America and West Africa

  • Submitted by: Eva Das, Barnard College, Junior, Computer Science

  • Track: Converge

P06: Investigating the Impact of Early Life Stress on Spontaneous Alternation Tasks in Mice: A Quantitative Approach

  • Submitted by: Lucy King, Columbia College, Sophomore, Computer Science

  • Track: Converge

P07: Reinforcement Learning in Dual-Sourcing Problem: A Focus on Real and Simulated Demand Shocks

  • Submitted by: Yihan Shen, SEAS, Junior, Computer Science

  • Track: Create

P08: An Efficient Algorithm to Build Atomic-Structure Datasets for Machine Learning Potential Training

  • Submitted by: Aleksandra Pawlowska, SEAS, Sophomore, Chemical Engineering

  • Track: Create

Booths

Data Science Institute, Columbia University (Website)

Columbia Data Science Society (Website)