Practitioners in creative domains such as web design, data visualization, and software development face many challenges while trying to create novel solutions that satisfy the guidelines around practical constraints and quality considerations. My dissertation work addresses two of these challenges. First, guidelines may conflict with each other, creating a need for slow and time-consuming expert intervention. Second, guidelines may be hard to check programmatically, requiring experts to manually use multipage style guides that suffer from drawbacks related to searchability, navigation, conflict, and obsolescence. In my dissertation, I focus on exploring mixed-initiative methods as a solution to these challenges in two complex tasks: biological network visualization where guidelines may conflict, and web design where task requirements are hard to check programmatically. For biological network visualization, I explore the use of crowdsourcing to scale up time-consuming manual layout tasks. To support the network-based collaboration required for crowdsourcing, I first implemented a system called GraphSpace. It fosters online collaboration by allowing users to store, organize, explore, lay out, and share networks on a web platform. I then used GraphSpace as the infrastructure to support a novel mixed-initiative crowd-algorithm approach for creating high-quality, biological meaningful network visualizations. I also designed and implemented Flud, a system that gamifies the graph visualization task and uses flow theory concepts to make algorithmically generated suggestions more readily accessible to non-expert crowds. Then, I proposed DeepLayout, a novel learning-based approach as an alternative to the non-machine learning-based method used in Flud. It has the ability to learn how to balance complex conflicting guidelines from a layout process. Finally, in the domain of web design, I present a real-world iterative deployment of a system called Critter. Critter augments traditional quality assurance techniques used in structured domains, such as checklists and expert feedback, using mixed-initiative interactions. I hope this dissertation can serve to accelerate research on leveraging the complementary strengths of humans and computers in the context of creative processes that are generally considered out of bounds for automated methods. / Doctor of Philosophy / Practitioners in creative domains such as web design, data visualization, and software development face many challenges while trying to create novel solutions that satisfy the guidelines around practical constraints and quality considerations. My dissertation work addresses two of these challenges. First, sometimes the guidelines may conflict with each other under a certain scenario. In this situation, tasks require expert opinion to prioritize one guideline over the other. This dependence on expertise makes the design process slow and time-consuming. Second, sometimes it is difficult to determine which guidelines have been fulfilled. In this scenario, experts have to manually go through a list of guidelines and make sure applicable guidelines have been successfully applied to the final product. However, using a list of guidelines has its own drawbacks. Not all guidelines are applicable to a project, and finding a relevant guideline can be strenuous for experts. Moreover, a design process is not as simple as following a list of guidelines. Design processes are dynamic, non-linear, and iterative. Due to these reasons, a simple list of guidelines does not align with the designers' workflow. My dissertation focuses on exploring mixed-initiative methods where computers and humans collaborate in a tight feedback loop to help follow guidelines. To this end, I present solutions for two complex creative tasks: biological network visualization where we can compute how well a design adheres to the guidelines but guidelines may conflict and web design where task requirements are hard to check programmatically. I hope this dissertation can serve to accelerate research on leveraging the complementary strengths of humans and computers in the context of creative processes that are generally considered out of bounds for automated methods.

This paper proposes a new ideation technique for how to ideate using sketching and a generative design system based prototype tool. The technique is aimed to support ideation through the exploration of design shape deviations. The tool generates 3D model artifacts with variations in their shapes. The overall shape and deviations are based on a design concept specified by the user of the tool. These design artifacts are then used as a means to rediscover one's own design concepts through the theory of estrangement. A study was conducted where university-level design students tested the technique in a scenario where they were tasked with ideating product design solutions. The performed study (along with post-study interviews) revealed promising results where participants of the study described their experiences as fun and interesting. The design outcomes of the study display how participants worked and iterated on their conceptual artifacts. They explored different and new angles of the design space relative to their concepts by using the generated design artifacts. This may suggest that this technique, using computer-generated concept deviations can be a successful way of supporting ideation by expanding on one's design concepts, rediscovering concepts, moving beyond obvious solutions and iterating design concepts.