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Updated 12/30/19 (scroll down to read all)
My name is Rania Labib, I'm a mom to three beautiful children and an assistant professor at Prairie View A&M University. I have been teaching various undergraduate and graduate courses at both Texas A&M and Prairie View A&M universities for the past five years.
I'm an educator and researcher, I have studied at Texas A&M which is a top 20 graduate architecture program in the United States. I educated myself on many computer science topics through completing certificate programs from top universities in Switzerland, Spain, and the USA. I am forever a student at heart. Architecture, technology, and people are my favorite subjects and I aspire to connect them through my research and teaching. Over the last few years, my research interests have transitioned from daylighting design to building performance simulations, high-performance building design, coding, data analytics, artificial intelligence, and robotics.
My General research interests are: High performance building design, coding for building performance simulations, smart buildings and cities, and big data.
Specific research interests: Smart buildings that communicate with each other via embedded devices (IOT) to facilitate occupants comfort, simulation tools in the early stages of the design process especially tools used for energy analysis and prediction of daylighting performance and its effect on cooling load and occupants comfort, and advanced daylight redirecting systems.
About my doctoral dissertation:
For the purpose of completing my PhD research, I established the Facade Internet of Things (F-IoT), a framework that would have not possible to develop without performing an interdisciplinary research that united two aspects—building physics and computer science. F-IoT is a model of communication between building facades that transforms the facades into intelligent ones that can communicate with each other to achieve the ultimate comfort of building occupants and to maximize energy savings. The F-IoT model offers a solution to various issues—such as glare and thermal discomfort—that are caused by reflective facades in urban areas. The communication between building façades is facilitated through embedded intelligence that is enabled by Internet-connected wireless sensor network (WSN).
Research and teaching experience:
I have received broad training during my graduate studies at reputable universities, such as the University of California at Irvine, Michigan University, University of Pennsylvania, and the College of Architecture at Texas A&M, where I specialize in high building performance design and building performance simulations. Given my expertise in building performance simulation, I was asked to give a four-session workshop on daylighting performance simulation within parametric modeling environments. The workshop was offered to graduate students who were enrolled in an architectural lighting course at the College of Architecture at Texas A&M University. The workshop introduced students to in-depth daylighting, glare, and thermal performance using up-to-date simulation tools. Additionally, advanced topics were discussed such as integrating daylighting and energy simulations, dynamic shading, and working with advanced Radiance materials that contain custom modifiers. At the end of the workshop, the students were able to model and analyze the daylighting and thermal performance of an existing small building.
While developing my PhD research, I applied advanced artificial intelligence techniques such as computer vision recognition, which I used to analyze millions of glare images, other advanced methodologies include, simulating a WSN using IBM’s Watson cloud platform, Python coding, and writing a Linux-based framework to route large-scale simulations for execution on thousands of computing nodes that are part of a super-computer, a framework that I presented at the 2019 International Building Performance Simulations Association (IBPSA) conference in Rome. The conference presentation is titled” Using Python to Automate the Preparation and Execution of Thousands of Daylighting and Glare Simulations on a Cloud Parallel Computing Environment for Time-efficient Simulations “. In this presentation, I demonstrated the use of Python language to facilitate the execution of millions of building performance simulations within few hours, a process that would have taken few months to complete. Furthermore, given my training in computer programming, big data, and research statistics, I was able to automate the analysis, plot, and extraction of important information from the results of the simulations, which contained over 6 million pieces of information. The conference paper went through a rigorous peer-review process for inclusion in the conference. In addition to presenting at the IBPSA conference, I was chosen to serve in the conference’s committee to peer-review some papers that were submitted to the conference.
Recently, I have been involved in developing new grasshopper components from scratch using Python. The components I’m developing work with other components from Grasshopper’s plugins Ladybug and Honeybee, which are climate analysis and building performance plugins. The most recent component I developed is titled “Shading and Reflection Analysis”, this component is used to assess the effect of reflective façades on surrounding buildings in the initial design phase. The component has been tested by Ladybug’s developers, who later recommended including the component in the next Ladybug and Honeybee release (upon full testing).
Future research goals:
I see my doctoral research study as the starting point for my long-term research goal of expanding the field of smart buildings and smart cities by taking building performance and occupant comfort into consideration as the primary drivers of smart design. My future research project will focus on a sensible facade design that balances the comfort of both occupants in interior spaces and outdoor pedestrians. In order to reduce the urban heat island effect, a sensible facade will be explored. The facade will have a WSN to collect pedestrians’ flow patterns and building performance data, such as daylighting, thermal, and acoustic performance. The collected data can be analyzed for real-time feedback and to predict the future flow patterns of building occupants and outdoor pedestrians, which can be a major asset in designing future urban settlements. This future study could serve as a seed to establish a high-performance-smart buildings and cities lab that is similar to the labs at top universities worldwide, such as ETH Zurich.
Future teaching goals:
I embrace every teaching opportunity that I can find, and for the last four years, I have worked enthusiastically and effectively with students from different backgrounds at the undergraduate and graduate levels. With my training and experience, I am prepared to teach building performance courses at all levels, focusing on low carbon design, building simulations, daylighting performance, energy modeling, climate analysis, smart cities, and smart building design. I am also interested in teaching building performance-integrated design, for example, a design studio that is based on light as form generator. In addition to the existing typical courses within the architectural department, I am planning on developing innovative graduate courses on emerging revolutionary fields such as the use of small robotics and embedded devices for sensible building facades, Grasshopper components development using Python for a better building performance assessment.
Considering my expertise in both building performance simulations and Grasshopper component development, I am planning on conducting a research study that will be based on teaching a graduate course on Grasshopper component development for better building performance simulations. Based on the outcome of the course, my research study will be able to examine the effect of having a better understanding of the code that is hidden in the Grasshopper simulations component on performing better building simulations that will lead to accurate simulations result. A parallel study can be accomplished during the graduate course, where new Grasshopper plugin is developed for better analytics of the simulations results.
Current scientific committee involvement:
As an active daylighting committee member at the Illumination Engineering Society (IES), I am currently taking part in revising the IES’s RP-5-13 (a recommended practice guide published by the IES titled “Recommended Practice for Daylighting Buildings”). Revising the RP-5-13 takes place every 3-5 years. This month I had the privilege to start revising the guide alongside with five other committee members, my duties include sourcing the latest research studies in the daylighting field, creating annex information including writing new case studies, creating new images and drawings for clarification purposes, and reviewing the 50% draft guide. The revision process is an ongoing effort with an estimated time frame of 12-18 months. Being able to participate in such a process has given me the opportunity to network and demonstrate my leadership and teamwork abilities.