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Welcome to my website! My name is Waleed Elshanshoury, An Architectural Design Technologist with 8 years of professional experience, specializing in computation, environmental design, and data-driven form finding. A Fulbright alumnus with a Master of Science in Architecture from IIT, Chicago, and a recipient of a fully-funded PhD scholarship from UWE, Bristol. Driven by a dual passion for design and technology, contributed to the design and design development of various project typologies includes hospitality, residential, commercial, high-rise, mixed-use, hospitals, and airports across Asia and Africa.
GENERATIVE URBAN DESIGN
MACHINE LEARNING AND GENERATIVE DESIGN INURBAN ENERGY MODELING
Doctoral research seeks to enhance the efficiency of Urban Building Energy Modeling by employing Artificial Neural Network Models (ANNs) and Generative Design. The objective is to accelerate the modelling process to real-time speeds, enabling instantaneous urban generation. This is achieved by numarizing urban geometric characteristics, simulated energy demand and solar radiation outcomes to train machine learning models.
A parametric design seamlessly incorporates environmental simulations, including radiation analysis, outdoor comfort assessments, and visibility analysis, to optimize the performance of urban generation output. LadyBug tools are employed for the seamless execution of simulations alongside parametric generations.
ARCHITECTURAL DESIGN
ACADEMIC PROJECT SAMPLES
Program: Master of Science in Architecture, IIT, Chicago
Program: Master of Science in Architecture, IIT, Chicago
Program: Master of Science in Architecture, IIT, Chicago
Program: Master of Science in Architecture, IIT, Chicago
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Program: Master of Science in Architecture, IIT, Chicago
Kinetic Canopy
Program: PhD in Architecture, UWE, Bristol
Program: Master of Science in Architecture, IIT, Chicago
Program: Master of Science in Architecture, IIT, Chicago
DESIGN TECHNOLOGY
Program: Master of Science in Architecture, IIT, Chicago
AVA
The project endeavours to develop a voice command-driven virtual assistant tailored for designers and architects, offering support in the creation of geometric forms and the management of parametric models. The Minimum Viable Product (MVP) was constructed within the Grasshopper environment, utilizing Python and C# to enable voice recognition and text translation into actionable commands, all while facilitating voice-based responses.
OTHER PROJECTS
Reach out for more projects and portfolio PDF file
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