# preferredTitle
Associate Professor at Prairie View A&M University

# positions[].organizations[].label
Prairie View A&M University - (Prairie View, Texas, United States)

# overview
Dr. Rania Labib is an accomplished Assistant Professor at Prairie View A&M University, part of the Texas A&M University system. As the founder and director of the Artificial Intelligence for High-Performance Buildings Lab (AI + HPB), she expertly blends her knowledge in energy-efficient buildings, building performance standards (BPS), energy modeling and simulations, energy management, buildings energy codes, AI, and big data to advance STEM education and research.

Dr. Labib's impressive academic background includes a Ph.D. from Texas A&M University in Architecture, where she specialized in integrating Internet of Things technologies into building facades for energy efficiency and occupant comfort. She has further expanded her expertise by participating in Stanford University's prestigious Energy Innovation and Emerging Technologies Program at the School of Engineering (anticipated completion June 2023) and is set to complete her Certified Energy Manager (CEM) certification by May 2023.

With over a decade of of experience in research, education, and architecture practice, Dr. Labib has established herself as an expert in various subjects, such as building energy analysis and modeling, energy codes, energy management, building electrification and decarbonization, digital twins, advanced built environment modeling and simulation tools, environmental inequality, indoor air quality, building automation, computer science, AI, solar -energy calculations, and big data.

Dr. Labib's numerous accomplishments include an honorable mention in the 2016 National Science Foundation's Graduate Research Fellowship Program and serving as the current president of the IBPSA-USA Houston Chapter, which recently received the 2022 IBPSA-USA Outstanding Chapter Award. She has authored and co-authored numerous scientific publications, presenting her research work both nationally and internationally. Her work encompasses machine learning-enabled building performance simulations, computer vision recognition, high-performance computing, and daylighting and visual comfort of indoor spaces.

Dr. Labib is affiliated with ASHRAE, IBPSA, and IES.

# keywords[]
Networking And Information Technology R&d (nitrd)

# keywords[]
Machine Learning And Artificial Intelligence

# keywords[]
Networking And Information Technology R&d (nitrd)

# keywords[]
Machine Learning And Artificial Intelligence

# keywords[]
30 Agricultural, Veterinary And Food Sciences

# keywords[]
Networking And Information Technology R&d (nitrd)

# keywords[]
Machine Learning And Artificial Intelligence

# keywords[]
30 Agricultural, Veterinary And Food Sciences

# keywords[]
Machine Learning And Artificial Intelligence

# keywords[]
4606 Distributed Computing And Systems Software

# keywords[]
Networking And Information Technology R&d (nitrd)

# keywords[]
Python For Building Performance Simulations

# keywords[]
Networking And Information Technology R&d (nitrd)

# keywords[]
Machine Learning And Artificial Intelligence

# educationAndTraining[].organization.label
Texas A&M University - (College Station, Texas, United States)

# publications[].label
Is computer programming beneficial to architects and architecture students for complex modeling and informed performative design decisions?

# publications[].label
Analysis and quantification of visual glare caused by photovoltaic panels installations in urban canyons

# publications[].publisher
11th Conference on Advanced Building Skins

# publications[].label
Towards A Solution for the Inevitable Use of the Glazed Faades in the Arid Regions via the Parametric Design Approach.

# publications[].label
Machine learning-based framework to predict single and multiple daylighting simulation outputs using neural networks

# publications[].label
Trade-off method to assess the interaction between light shelves and complex ceiling forms for optimized daylighting performance

# publications[].publisher
The 9th International ENERGY FORUM on Advanced Building Skins

# publications[].label
Utilizing physics-informed neural networks to advance daylighting simulations in buildings

# publications[].label
The Negative Impact of Solar Reflections Caused by Reflective Buildings' Facades: Case Study of the Nasher Museum in Texas

# publications[].label
Ten questions concerning Large Language Models (LLMs) for building applications

# publications[].label
Utilizing High Performance Computing to Improve the Application of Machine Learning for Time-Efficient Prediction of Buildings Daylighting Performance

# publications[].label
Investigation of the Non-visual Effects of Lighting on the Elderly

# publications[].label
Leveraging Multimodal Large Language Models for Enhanced Learning and Application in Building Energy Modeling

# publications[].label
Improving daylighting in existing classrooms using laser cut panels

# publications[].label
Integrating Machine Learning with Parametric Modeling Environments to Predict Building Daylighting Performance

# publications[].label
Trade-off method to assess the interaction between light shelves and complex ceiling forms for optimized daylighting performance

# publications[].label
THE FUTURE OF Artificial Intelligence In Buildings

# publications[].label
Automated Computer Vision Recognition Based Method to Determine Glare Causing Patches on Reflective Building Faades

# publications[].label
Automated Execution of Large-Scale Daylighting and Glare Simulations in a Cloud-Based Parallel Computing Environment

# publications[].label
Faade Internet of Things (FIoT): A Human-Sensing,Two-Faade Communication Approach to Achieve Glare Reduction, Optimized Daylighting, and Solar Energy Collection

# publications[].label
Evaluating Machine Learning Models for Sustainable Building Design: Energy, Emissions, and Comfort Metrics

# organizations[]
Prairie View A&M University - (Prairie View, Texas, United States)