As a Senior computational designer and researcher in building science, I am dedicated to solving some of the most pressing challenges facing modern architecture and design. With expertise in areas such as energy consumption, daylighting, acoustic and thermal comfort, I have developed a deep understanding of how to leverage new technologies and methods to optimize building performance.
Throughout my career, I have collaborated on numerous projects with top-ranked universities, contributing valuable research insights and developing innovative solutions to complex problems. As an advanced teacher of Grasshopper, Ladybug Tools, I have also shared my expertise with others in the field, helping to empower architects and engineers to perform environmental analysis and simulation more effectively.
Now, I am excited to be exploring new frontiers in the development of web apps and tools for architects and designers. By combining my extensive knowledge of building science with software development skills, I am confident that I can make a meaningful impact on the industry and help usher in a new era of sustainable, high-performance building design.

 

  • Freelance Available
  • Address Seattle, USA
  • Phone 00989361759625
  • E-mail mhtaba@uw.edu
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Education

  • 2023 – now ___ PHD in Built Environment 

University of Washington 

  • 2020 – 2023 ___ Master of Energy and Architecture 

University of Tehran ( GPA 4 / 4 ) – ( Rank 15 among 20,000  in the national exam )

  • 2015 – 2020 ___ Bachelor of Architecture Engineering 

Art University of Esfahan ( GPA 3.75 / 4 )

  • 2010 – 2015 ___ Diploma of Math and Physics

Adl High School

Experience

  • 2021 – 2023 ___ BSPsim 

Computational Designer & IEQ Specialist 

  • 2021 – 2023 ___ Innovation and Technology Center of Shahid Beheshti University

Advanced Teacher 

  • 2016 – 2020 ___ More Office 

Co-Founder – Architecture Designer

  • 2015 – 2016 ___ Green Volume Accelerator

Architecture Designer

Competitions and Workshops

  • 2022 ___ Neural Networks for Predicting Subjective Design Evaluation

Held by Digitalfutures

  • 2022 ___ Rhino and Grasshopper for Architects Workshop

Held by How To Rhino

  • 2022 ___ Generative design and Machine Learning For architects Course

as an instructor (Innovation and Technology Center of Shahid Beheshti University)

  • 2021 ___ Generative design and Data Generation For architects workshop

as an instructor (Innovation and Technology center of Shahid Beheshti University)

  • 2021 ___ Kinectoscapes: Architecture of Performative Intelligence

Held by Digitalfutures – Dr. Mona Ghandi 

  • 2021 ___ Machine Learning in Architecture 

Held by Dr.Morteza Rahbar 

  • 2021 ___ Parametric Design in Architecture – Grasshopper

Held by Dr.Morteza Rahbar 

  • 2021 ___ Architecture Portfolio Design workshop

as an instructor

  • 2019 ___ Evolo Competition 2019 

Held by Evolo

  • 2018 ___ Photoshop and Graphic Design course 

as an instructor (Scientific Association of Isfahan Art University)

  • 2017 ___ Tensegrity Structure Workshop 

Held by Art University of Esfahan

  • 2016 ___ Furniture Design

Held by Art University of Esfahan

Software Skill

  • Grasshopper
    90%
  • Rhinoceros
    80%
  • Energy plus
    80%
  • Revit
    70%
  • Autocad
    60%
  • Lumion
    90%
  • Adobe Photoshop
    100%
  • Adobe Indesign
    60%
  • Python
    70%
  • Machine Learning
    80%
  • Figma
    80%
  • Wordpress
    90%

Indoor environmental quality Skill

  • Energy Consumption
    90%
  • Thermal Comfort
    70%
  • Daylight
    80%
  • Acoustic
    90%
  • Renewable Energy
    60%

Languages Skills

  • Persian ( Mother tongue )
    100%
  • English
    80%

Interests

  • Artificial Intelligence
    90%
  • Robotic
    90%
  • Sustainability
    90%
  • Digital Marketing
    90%
  • Graphic Design
    90%
  • Teaching
    90%
  • Soccer
    90%
  • Fitness
    90%

Researches

  • Design and experiment of a desiccant based Evaporative cooling Shading System 

( 2022 ) – Submitted

Due to high energy consumption in the cooling sector in hot and dry regions, passive evaporative cooling is a good option in reducing energy consumption and increasing thermal comfort. Silica gels could be used as an effective evaporative media in evaporative cooling due to their adsorption properties. This research presents a novel evaporative cooling louver shading system using silica gels. The main idea of this research is to add the passive evaporative cooling capability to shadings. For this purpose, Silica gels, as a desiccant material, are inserted in the louvers and placed in a water chamber, turning the louvers into a wet evaporating plate. The shading system is implemented on the indoor side of the window opening, resulting in evaporative cooling. The system performance is evaluated through experimental tests and simulations by Comsol in Isfahan’s hot and dry climate to measure the effect of this system on the room’s air temperature and relative humidity. Based on experimental evaluations in a controlled environment, the shading system reduced the temperature by 5° and increased the relative humidity by approximately 4% after 100 minutes. The simulation results showed that the temperature and the humidity difference after employing the shading system in the test box are up to two degrees and 9%, respectively. Also, the temperature and the humidity difference after employing the system in a room model is 1 degree and 4.5%. by optimizing and improving the presented novel shading system(ESS), this system will be energy efficient and promises to popularize in the hot and dry regions.

  • Early design stage evaluation of architectural factors in building acoustic using Pix2pix and CatBoost model; Case study: Educational buildings

( 2023 ) – Master thesis

Educational buildings are one of the places that have different acoustic needs, and the unfavorable sound quality can have destructive effects on students. Today, many building regulations and building rating systems have proposed different definitions and ranges of acoustic comfort. Therefore, calculating acoustic indicators is one of the needs that designers and every person in the building industry need. One of the essential parts of acoustic comfort is related to the acoustic calculations inside the building, and T30, EDT, D50, C80, SPL, and STI indicators are among the most important. Indoor acoustic indicators can be calculated using static, geometric, and data-based methods. Despite their high speed, static methods ignore details and cannot be effective. The geometric methods used today with advanced software such as Odeon are highly accurate, and many details can be added to them, but due to their specialization, long simulation time, and expensive software, they are less used. Data-based methods such as machine learning have high accuracy, speed, and availability. This research aims to develop the framework of a comprehensive system for evaluating the acoustic environment, which, based on machine learning, can predict numerical acoustic indicators in an educational building. In this research, extensive simulations have been carried out in the Grasshopper environment and using the Pachyderm plugin in the scale of a single educational space. T30, EDT, D50, C80, and SPL indicators in six different frequencies and STI in three different modes are considered for numerical simulation. In the development of the data set, the CatBoost algorithm is used to predict the indicators. The results showed that the model could have an accuracy between 89 and 99 percent. This research can be available as a web tool, and activists and enthusiasts in the field of construction can use it in the stages of design, construction, and improvement.

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