Center for Smart Streetscapes VALIDATE Accelerator

This non-dilutive program focuses on building and scaling successful early-stage companies through in-depth education, mentorship, and business model design. As a pillar of the NSF Engineering Center for Smart Streetscapes, Columbia University is partnering with other CS3 institutions and Columbia’s Start Me Up Bootcamp to offer startups a customized program composed of two key modules: 1) product-market fit and customer discovery module and 2) smart cities-specific educational content. Module 1 introduces participants to the Lean Launchpad methodology and techniques to direct customer discovery through field interviews. Successful completion of the program make teams eligible to apply for the National Science Foundation’s I-Corps Teams program, which includes up to $50,000 in grant funding.

Apply by Nov. 15, 2024:

VALIDATE Accelerator Application

Become a mentor:

Mentor Form

Program Details

Eligibility

At least one team member must be a faculty, student, or recent graduate affiliated with a CS3 participating institution (Columbia University, Florida Atlantic University, Lehman College CUNY, Rutgers University, University of Central Florida)
Applicant team is working on an idea or prototype for an innovative smart city solution
Team must be ready to commit 5-10 hours per week to customer discovery, business validation, and product build for their ventures
Team has received no institutional funding at the time of application (grants and awards are acceptable)
Preference for two team members per team – both willing to commit to program’s requirements (I-Corp and CS3-specific programming)
Team must commit to attending ALL weekly sessions

I-Corp Funding

Teams may apply for travel funding to continue customer discovery and attend a conference or industry event. Bootcamp requirements must be completed by the team including 20 customer interviews, attend bootcamp sessions and office hours. Funding support levels vary for teams $1,000 – $3,000.
Stipends are available for bootcamp team members who complete all requirements with a financial support of $500. Underrepresented students and post-doctoral researchers are encouraged to apply.
Upon completion of the program, teams are eligible to apply for the National Science Foundation’s I-Corps Teams program, which includes a $50,000 grant.

Review Process

Program Information

Our Alumni Teams

Spring '24

Team: Marco Tedesco

Affiliation: Columbia University, Research Professor at the Lamont-Doherty Earth Observatory

LinkedIn: Marco Tedesco

Email: mtedesco@ldeo.columbia.edu

Climate Impact detects flooded city areas using web cameras and images as well as artificial intelligence technology, providing estimations of water levels in impacted areas. Climate Impact’s detector has both stand-alone and plug-and-play capabilities, allowing it to generate ubiquitous real-time, accurate estimates of flooding. Through the Climate Impact product, cities can better monitor flood trends and mitigate the effects of flooding on their infrastructures and communities.

Team: Mahshid Dehkordi, Jeremy Johnston

Affiliation: Columbia University, Columbia PhD Candidates in Electrical Engineering

LinkedIn: Mahshid Ghasemi, Jeremy Johnston

Email: mahshid.ghasemi@columbia.edu, jj3057@columbia.edu

Cyrus is an AI-native security system that can identify an extensive range of abnormal behaviors and activities that can endanger safety and security standards in indoor and outdoor areas. Cyrus can turn any security cameras (regardless of their manufacturing brand) into a non-intrusive safety and security patrol system that works in real-time. Cyrus does not require fine-tuning and retraining to adapt to a new environment. Cyrus can detect subtle indicators of safety concerning behaviors or objects, such as verbal or physical altercations and various types of weapons, with high accuracy in real-time without any extra fine-tuning.

Team: Caspar Lant

Affiliation: Columbia University, PhD Candidate in Computer Science

LinkedIn: Capsar Lant

Email: caspar@cs.columbia.edu

In an era of increased risk from vehicular traffic, Walkwise protects the most vulnerable road users. Through their GPS-based pedestrian intent prediction tools, which leverage a transformer-based machine-learning model that inputs live, local GPS-positioning data (as recorded by a user’s phone), Walkwise provides information on pedestrians intersection interactions. By transmitting crossing predictions to intersection signaling gateways, Walkwise can provide dynamic, optimal solutions for any traffic circumstance.

Team: Georgios Sklivanitis, Dan Zimmerman

Affiliation: Florida Atlantic University, Faculty in Electrical Engineering and Computer Science; Florida Atlantic University, Graduate Student in Electrical Engineering and Computer Science

LinkedIn: Dan Zimmerman, Georgios Skilvanitis

Email: dzimmerman2021@fau.edu, gsklivanitis@fau.edu

FloodSense improves the accuracy of flood forecasts in areas lacking weather infrastructure. By crowdsourcing hydrologic data, FloodSense offers an entirely new data economy that rewards people and cities who deploy and maintain FloodSense’s flood monitoring sensors. Through a dense, decentralized flood marketplace that users can purchase and sell hydrologic data on, FloodSense can improve citywide flood awareness.

Team: Christopher Grullon

Affiliation: Columbia University, Undergraduate Student at the Fu Foundation School of Engineering and Applied Science

LinkedIn: Christopher Grullon

Email: grullonc455@gmail.com

Sustainable Safe Smart Intersections is equipping cities with technology that enables more sustainable living. Through their intersection simulation model, Sustainable Safe Smart Intersections offers an analysis of the impact of intersection redesigns. Trained on integrated, real-world data points, the product offers simulations similar in accuracy to real-life, intersection situations.

Team: Izaac Martinez

Affiliation: Florida Atlantic University, Undergraduate Student in Data Science and Analytics

LinkedIn: Izaac Martinez

Email: izaacmartine2022@fau.edu

The FloodFinder product detects road flooding, alerting the public of optimal rerouting options. The FloodFinder product leverages both FloodFinder cameras’ feeds and pre-existing traffic cameras to identify flooding. Using neural networks to develop software that analyzes photos and video, the FloodFinder alarm light will warn the public about high-flood areas, keeping drivers safer and better informed.