collaborators

8 papers

eess.SY2026

Semantic Technologies in Practical Demand Response: An Informational Requirement-based Roadmap

Ozan Baris Mulayim, Anand Krishnan Prakash, Yuvraj Agarwal +5

The transition to a modern and efficient future grid relies on the seamless coordination of distributed energy resources and applications such as Demand Response (DR). While this t…

cs.CE2025

Comparative Evaluation of Neural Network Architectures for Generalizable Human Spatial Preference Prediction in Unseen Built Environments

Maral Doctorarastoo, Katherine A. Flanigan, Mario Bergés +1

The capacity to predict human spatial preferences within built environments is instrumental for developing Cyber-Physical-Social Infrastructure Systems (CPSIS). A significant chall…

cs.LG2025

Transformer-Based Indirect Structural Health Monitoring of Rail Infrastructure with Attention-Driven Detection and Localization of Transient Defects

Sizhe Ma, Katherine A. Flanigan, Mario Bergés +1

Indirect structural health monitoring (iSHM) for broken rail detection using onboard sensors presents a cost-effective paradigm for railway track assessment, yet reliably detecting…

eess.SY2025

Comparative Field Deployment of Reinforcement Learning and Model Predictive Control for Residential HVAC

Ozan Baris Mulayim, Elias N. Pergantis, Levi D. Reyes Premer +4

Model Predictive Control (MPC) has demonstrated significant performance improvements over today's control methods for residential Heating, Ventilation, and Air Conditioning (HVAC),…

cs.CV2025

Street View Sociability: Interpretable Analysis of Urban Social Behavior Across 15 Cities

Kieran Elrod, Katherine Flanigan, Mario Bergés

Designing socially active streets has long been a goal of urban planning, yet existing quantitative research largely measures pedestrian volume rather than the quality of social in…

cs.LG2025

Can Time-Series Foundation Models Perform Building Energy Management Tasks?

Ozan Baris Mulayim, Pengrui Quan, Liying Han +4

Building energy management (BEM) tasks require processing and learning from a variety of time-series data. Existing solutions rely on bespoke task- and data-specific models to perf…