Arachna Networks
Team: Jacob Kahn, Gabriel Garcia
Advisor: Dr. Georgios Sklivanitis
Affiliation: Florida Atlantic University, Undergraduates in Computer Engineering with faculty advisor
Arachna Networks wants to implement emergency fast deployable networks to smart city enchaining utilities that operate with a full independent network stack that is not centralized. We aim to bring Arachna Networks to public safety, law enforcement, and defense markets. Arachna Networks is an open-source based solution built on commercial off-the-shelf low-cost hardware. During the program, our goal is to validate if Arachna Networks will be a software-hardware or software-only product and develop a commercialization plan of our solution.
InfraInsight
Team: Ines Khoulder, Samarth Agrawal
Affiliation: Columbia University, Undergraduates in School of Engineering and Applied Science
Building Managers face challenges in optimizing and tracking energy efficiency given the high cost of IoT retrofits. Although scaling is hindered by the wide variety of building types, augmenting “digital-twin” building solutions with satellite and remote sensor data can provide good-enough quantitative insights to affordably model potential improvements. This project is the confluence of 2 projects undertaken throughout Fall 2024 as part of academic coursework. The team hopes to pinpoint the ideal area of product-market fit and come to a concrete go-to-market strategy.
S.T.R.I.D.E
Team: Muhammad Shahbaz, Md Mahmudul Islam
Advisor: Dr. Shaurya Agarwal
Affiliation: University of Central Florida, PhD student and Graduate Research Assistant in Civil, Environmental, and Construction Engineering with faculty advisor
Urban Sentinel aims to improve traffic sensing and management using an AI-enabled software suite that seamlessly integrates multiple sensors -- cameras, lidars, radars and more, regardless of manufacturer, to accurately detect, track, predict, and analyze road-user trajectories. Using multi-modal deep learning models for advanced sensor fusion and large language models fine-tuned for reasoning, Urban Sentinel empowers cities by providing decision-level intelligence. The application areas include but are not limited to traffic management, traffic safety and accident prevention, and support for connected and autonomous vehicles.
Viarithm
Team: Dr. Mehmet Turkcan
Affiliation: Columbia University, Associate Research Scientist in Civil Engineering and Engineering Mechanics
Viarithm develops and openly shares reliable computer vision models for urban environments, backed by comprehensive real-world datasets and evaluation frameworks. They enable trustworthy AI applications in cities by providing thoroughly tested models, hardware and transparent evaluation metrics that reflect actual street conditions. Viarithm is the only platform providing the complete stack: datasets, models, evaluation, and hardware deployment.
SatSight
Team: Jackson Ye, Joseph Bajor
Advisor: Dr. Alexandre Morozov
Affiliation: Rutgers University, PhD student in Electrical and Computer Engineering with faculty advisor
SatSight is an affordable AI platform that analyzes satellite imagery in real time, providing urban planners with up-to-date insights on traffic flows, infrastructure conditions, and environmental health, enabling proactive, cost-effective decisions that promote sustainable growth, reduce pollution, and enhance safety. The technology stands apart from competitors due to its cutting-edge Object-Centric State Space Models (SSM) AI model, which achieves near-linear memory complexity while delivering a global perspective on urban dynamics.
SINA
Team: Bowen Fang, Ruijian Zha, Hongcheng Tian
Advisor: Dr. Sharon Di
Affiliation: Columbia University, PhD and Master’s students in Industrial Engineering and Operations Research with faculty advisor
SINA differentiates itself by offering context-aware and highly personalized route recommendations that go beyond what competitors like Google Maps or Citymapper provide. The app incorporates real-time contextual information derived from natural language inputs, images, and integrations with third-party applications such as Notion. By dynamically syncing with calendars and other productivity tools, SINA can proactively adjust plans based on real-time data, such as unexpected delays, weather changes, or new user priorities. SINA processes diverse and complex user inputs, such as dash cam images or detailed queries, to provide actionable insights that competitors cannot match. By combining advanced AI capabilities, real-time adaptability, and third-party integrations, the solution redefines transit planning for a smarter, more responsive user experience.
