The DSI Smart Cities Center will convene a series of programs focused on the future of urban systems and the data science and AI-driven technologies shaping them. The center supports research that addresses the challenges of aging infrastructure while advancing innovations in smart grids, intelligent transportation systems, and networked sensing technologies that enable real-time monitoring and decision-making.
Through its event programming, the center will bring together researchers exploring how AI, machine learning, and emerging digital infrastructure can improve the resilience, efficiency, and sustainability of dense urban environments. Topics will span predictive maintenance, mobility optimization, energy distribution, urban analytics, and the evolving role of intelligent systems in shaping everyday city life.
The series is open to Columbia University faculty members and affiliated senior researchers. If you’d like to join these meetings, contact Erin Elliott, Events and Marketing Coordinator at ee2548@columbia.edu to receive the location and calendar invite.
Yinhai Wang, Thomas and Marilyn Nielsen Endowed Professor in Engineering, Director of PacTrans and STAR Lab, University of Washington
Title: Edge AI in Motion: Transforming Roads into Safer, Smarter, and More Community-Centered Mobility Systems
Abstract: Despite decades of investment and conventional approaches, transportation systems continue to face persistent challenges in safety and mobility. There is an urgent need for more effective,deployment-ready solutions that leverage emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT). However, many existing solutions rely on cloud-centric architectures that suffer from high and unpredictable latency, bandwidth limitations, and substantial deployment and operational costs. These limitations highlight a growing need for affordable, edge-based technologies that can operate reliably on site in real-world transportation environments.
In this talk, the speaker will present his team’s research on edge computing and customized AI methods for mobility and safety applications at the University of Washington’s Smart Transportation Applications and Research Laboratory (STAR Lab). Several representative efforts aimed at transforming roads into safer, smarter, and more community-centered mobility systems will be highlighted, including the award-winning Mobile Unit for Sensing Traffic (MUST) edge AI system, Agentic Edge, and Edge-Retrieval Augmented Generation (Edge-RAG). Strategies for tailoring edge systems to transportation tasks—such as scenario-specific customization and the deployment of lightweight models—will also be discussed.
The Smart Cities Research Center hosted a delegation from Japan’s Ministry of Land, Infrastructure, Transport and Tourism (MLIT), along with representatives from the Mitsubishi Research Institute and International Access Corporation, for a research exchange on next-generation urban transportation technologies. The visit highlighted Columbia’s work on hybrid digital twins and the COSMOS testbed, while the delegation shared perspectives on related projects underway in Japan.
Columbia Presentations:
Date of Talk: Thursday, December 4, 2025
Speaker: George Pechlivanoglou, Director General, Eunice Energy Group; and CEO and President, Joltie
Abstract: Europe’s energy transition has outpaced its digital transition. As renewable penetration rises and grid inertia vanishes, traditional models of forecasting and control collapse under the weight of nonlinear complexity. The European energy market, an intricate, data-rich ecosystem of zonal prices, balancing mechanisms, and infrastructural constraints, demands better data analytics.
This lecture presents a new frontier: the creation of a secure, full-scale digital twin of the European power system, capable of modeling the network itself. By unifying market dynamics with physics-based models of substations, transmission lines, and generation assets, the digital twin reconstructs and simulates the grid’s hidden state in near real time. This model extends beyond energy forecasting toward predictive energy economics and provides a framework to model not only how the grid behaves, but how it will behave under stress.
Modern grids face not only physical instability from renewables but systemic threats from cyberattacks, cascading failures, and coordinated disruptions. Within the digital twin, artificial damage simulations, including cyber-physical intrusion scenarios, enable testing of defense mechanisms, recovery strategies, and autonomous response models. By integrating telemetry, behavioral anomaly detection, and threat intelligence into the energy models, we can prototype techniques with broad applicability across the EU.
Eunice Energy Group provides a unique empirical base for this study by acting as an operational and asset-level data across the Mediterranean’s most dynamic and vulnerable systems. Greece stands as a living laboratory ideal for this type of study: a semi-isolated grid, high in renewable volatility, where resilience and cybersecurity are operational necessities.
The challenge to the data science community is to design the intelligence layer of Europe’s next-generation energy infrastructure through analysis and integration of modalities that have never been combined before with far-reaching individual benefit. Through causal inference, graph neural networks, hybrid AI–physics modeling, and adversarial resilience simulation, data previously restricted to an energy company’s internal frameworks can be unique opportunities for data scientists interested in green energy and climate change.