Innovation Expo

The Innovation Expo highlights cutting-edge projects from across Columbia University’s research, technology, and innovation ecosystem. Join us to explore live demonstrations from Columbia-affiliated start-ups, and hear graduate students and Data Science Institute (DSI) scholars present their latest research breakthroughs.

Get to know the featured start-ups and projects below.

Explore Our Featured Start-Ups and Projects

Arlex.AI

  • Arklex provides an automated AI agent testing platform that automatically stress-tests AI agents by simulating thousands of real-world and adversarial interactions to surface failure modes and safety risks before deployment. It gives teams the confidence and the benchmarks to ship AI responsibly.

  • Yi Ju
    zoe.ju@arklex.ai

    Andy Yao
    andy.yao@arklex.ai

  • Access here:
    https://www.arklex.ai/

A Census-Tract Analysis of Data Center Climate Vulnerability in the United States

  • We study the location of existing and proposed data cenetrs in relationhips to outages, weather and other risks.

  • Marco Tedesco
    mtedesco@ldeo.columbia.edu

Data Flow Control

Delegated Control Under Attack: A Measurement-Driven Threat Model for Exposed AI Agents

  • Explore the hidden vulnerabilities of modern AI with our live, high-interaction honeypot agent, deployed on a public platform to capture real-world attacks in the wild. Attendees will see firsthand how adversaries use prompt injection and social engineering to hijack an agent's delegated power, revealing the critical risks of the 'confused deputy' problem in autonomous systems.

  • Weiliang Zhao
    weiliang@cs.columbia.edu

Linking Data Centers, Energy, and Weather Risks via AI

  • Our project ais at building an explanatory model for modeling energy security and resiliency via explanatory tools and the fusion of energy, data centers, socio-economic, weather and climate risks.

  • Marco Tedesco
    mtedesco@ldeo.columbia.edu

Quantifying Video Storyline: A Theory-Guided Multimodal LLM Approach For Creative Insights

  • The project is about how to design story arcs and story delivery strategies to improve ad performance. We will show how AI can be used to generate actionable story insights for video creatives design and experimentation.

  • Yu Ming Yang
    yyang24@gsb.columbia.edu

Reliable Vibe Coding

  • Our poster shows two ways to make AI coding agents more reliable. We combine deterministic constraint verification for web apps with LLM-driven policy enforcement.

  • Reya Vir
    reyavir@cs.columbia.edu

    Jenny Ma
    jm5676@columbia.edu

Tactorum Inc.

  • Current rodent pain/touch assays are logistically difficult, costly, have pervasive confounds, and don't translate well to humans. The ARM System solves these problems by automating these tests through a combination of robotics with AI automation and high-speed behavioral analysis.

  • Justin BurdgeJustin.burdge@tactorum.com

    Agnes Gjekmarkaj
    agnes.gjekmarkaj@tactorum.com

  • Access here:
    https://tactorum.com/

AI-Driven Clinical Decision Support for Diabetes Treatment Selection

  • What if you could ask "what does the research say about this patient?" and actually get a useful answer in seconds? Our project does exactly that, drawing on 80+ published papers from three landmark diabetes trials.

  • Luis Enrique Ignacio Gomez Ordonez
    lg3441@columbia.edu

    Emma Elizabeth DiLauro
    eed2167@columbia.edu

RateMate Energy

Inside Outside Health

  • Inside Outside Health is a personalized digital platform designed to support athletes’ mental health and performance through tailored content, habit-building tools, and a trusted peer community. Our demo will include a live app walkthrough, an interactive product demonstration, and a short video highlighting how athletes can access real-time support, build healthy routines, and connect in a safe, empowering environment.

  • Isabella Gartner
    img2152@columbia.edu

    Emily Cory
    ec3987@columbia.edu

    Angela Seo
    es4423@columbia.edu

  • Access here:
    https://insideoutsidehealth.org/

Metlas

  • At Metlas, we build highly reliable, custom AI agents that actually work in the real world. Powered by our unique evaluation technology and backed by over a decade of award-winning research on Trustworthy AI. Co-founded by a Columbia University Computer Science Professor (Stanford PhD, ACM Fellow) and a Columbia PhD, our team combines deep expertise in reliable, secure software systems with cutting-edge AI.

  • Wei Hao
    wei@metlas.ai

  • Access here:
    https://metlas.ai/

LakeAgent: Search and Reasoning
Over Large Heterogeneous Data Lakes

  • We present LakeQA, a benchmark for search-centric question answering over large heterogeneous data lakes of tables, text, and metadata, together with AutoLakeAgent, an agentic system that answers natural-language questions by automatically discovering relevant datasets, constructing knowledge graphs, traversing cross-file relationships, and returning answers grounded in retrieved evidence. an agentic system that answers natural-language questions by discovering, linking, and analyzing relevant data sources — addressing the challenge of exploratory QA in settings where useful evidence is not pre-provided but scattered across heterogeneous tables, text, and metadata, requiring automatic knowledge-graph construction, agentic search and reasoning, and cross-file relationship traversal to return answers grounded in retrieved evidence.

  • Haonan Wang
    hw2983@columbia.edu

    Jiaxiang Liu
    jl6235@columbia.edu

Mirror AI

  • Mirror AI lets you survey 2000 digital twins - built from real demographic and psychographic data - to get instant, scalable market research insights. Come test a live query against our twin population and see how synthetic respondents compare to traditional survey panels.

  • Oliver Toubia
    ot2107@gsb.columbia.edu

    George Gui
    zg2467@gsb.columbia.edu

    Naveen Venkat
    nv2444@columbia.edu

Doppel Market

  • We will be demoing an interactive and agentic “digital twin”, which, unlike other agents, is hyperpersonalized for a specific person. We will be demonstrating the Doppel Market's marketplace platform, and how the twin can answer research surveys on the user's behalf.

  • Rebeca 'Beca' Almeida
    RAlmeida26@gsb.columbia.edu

    Bruno Neira
    bruno.neira@columbia.edu

  • Access here:
    https://doppelmarket.com/

Curiously AI

Fair Aggregation in Virtual Power Plants

  • Virtual power plants (VPPs) aggregate customers’ flexible resources to support the electric grid under growing pressure from data centers, electric vehicles, and renewable energy, but differences in customer flexibility can create fairness concerns. This demonstration explores what fairness should mean in VPP operations and how different fairness designs affect consumer participation and market outcomes.

  • Hyemi Kim
    hk3181@columbia.edu

AI and the Tapestry of Ethnic Placemaking in American Suburbs

  • Using AI and computer vision, we have mapped the tapestry of languages used in the suburbs to document how different ethnic groups have settled and developed so-called “ethnoburbs” around Detroit, Atlanta, and Washington, DC, whose physical characteristics differ from those of twentieth-century ethnic communities. We present a work in progress, centered on Metro Detroit, for an exhibition contribution to the National Building Museum in Washington, DC.

  • Anthony Vanky
    a.p.vanky@columbia.edu

Checkpoint-lite

  • What if an AI agent could snapshot a half-finished task, restore it later, and explore different next steps from the same live working state? Our demo shows how checkpoint-lite captures and restores live terminal environments, so agents can easily revisit intermediate states for structured exploration instead of repeatedly restarting from scratch.

  • Jiakai Xu
    ax2155@columbia.edu

    Danielle Gillai
    deg2184@barnard.edu

  • Access here:
    https://daplab.cs.columbia.edu/projects/checkpoint-lite/

AskAI Projects (Explainable Asset Allocation and Portfolio Construction; and AI Deployment Framework for Consumer Credit Models)

  • Credit cards are the most popular payment method in developed countries and their growth is extending internationally. On two datasets, one real and one simulated, we build and describe the entire pipeline of credit risk modelling, including exploratory data analysis, feature engineering, model assessment, interpretability, and assignment of limits.

  • Miao Wang
    mw3302@columbia.edu

    Noah Dawang
    noah.dawang@ask2.ai

    Federico Klinkert
    federico.klinkert@ask2.ai