Decision Making and Data Visualization in Design

Welcome to Decision Making and Data Visualization in Design

Description: Designing is a complex process that involves the consideration of a plethora of aspects, such as different design criteria and performance aspects that often are at odds with each other. To tackle the multidimensionality of the performance-based design, designers either need to ponder simultaneously different aspects or resort to (semi)automated search procedures to examine better the potential trade-offs of a wide range of design solutions that compose the design space. For example, it is becoming common to use parametric design and multi-objective optimization techniques borrowed from other disciplines (e.g., computer science, artificial intelligence, mathematics, statistics, sociology) to explore the design space of a multi-criteria design problem more effectively. While the trade-offs of two performance/decision variables are easy to examine, the complexity of analyzing the output of parametric design explorations and multi-objective optimization processes exponentially increases along with the number of decision variables, thus hindering decision-making processes during the design process.

This course will examine the crucial role of data visualization in analysis and decision-making processes in complex multi-criteria design problems. Through a series of readings, lectures, workshops and practical exercises, it will explore different visualization methods to improve analysis tasks and statistical techniques to simplify the complexity of the problem (e.g., dimensionality reduction). Thus, the course requires a multidisciplinary view that involves the knowledge of different fields such as computer simulation, statistics, data visualization, and parametric and goal-oriented design. The goal is to develop better ways to steer the design of buildings or products that involve several performance indicators and enable students to fully address the complexity of multi-criteria performance-based design.

Prerequisites: Given the course's multidisciplinary nature, the target group consists of architecture, urban design, industrial designers, engineering, and other PhD design students interested in complex design problems and performance-based design processes. Some knowledge of basic statistics, basic data visualization, parametric design is preferable. Basic programming skills are welcome but optional.

Learning objectives: 

• Course participants are able to choose appropriate data visualization techniques for different multi-criteria design problems
• Course participants are able to critically discuss and reflect on fundamental principles of optimization that support design search procedures
• Course participants are able to develop tailored visualizations to better explore the trade-offs of different design performance variables
• Course participants are able to critically discuss and reflect on principles of data visualization
• Course participants are able to critically discuss and reflect on principles of post-processing of parametric analysis and multi-objective optimization output data
• Course participants gain knowledge data processing, analysis, and visualization related to design processes

Deadline for enrolment: 12. November 2026

Important information concerning PhD courses: There is a no-show fee of DKK 3,000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before the start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start of the course.

We cannot ensure any seats before the deadline for enrolment, all participants will be informed after the deadline, approximately 3 weeks before the start of the course.

You may find more information in our FAQ: https://phd.moodle.aau.dk/local/page/faq 

For inquiries not described in the FAQ, please contact the PhD administration at phdcourses@adm.aau.dk. When contacting us please state the course title and course period. Thank you.