Subjective attributes play a significant part in the assessment of user-facing products. Unlike performance requirements, these quantities are best evaluated through human feedback. While people share commonalities in their evaluations, allowing personalization when quantifying these subjective attributes may improve the alignment between computational and human representations of design information. We investigate this topic through a study in which participants (N = 56) make a series of pairwise decisions between parameterized mugs, and indicate their perceptions of how comfortable each is to hold. Interactive Bayesian optimization is used to adaptively arrive at a design that optimizes this subjective quantity. Participants guide the model through only their own decisions or make decisions using a model that has already been trained with simulated data (N = 25) or data from the real decisions of other participants (N = 31). The resulting designs are evaluated across the different cases, showing the impact of capturing individual and aggregate perceptions of subjective quantities. The findings imply that balancing aggregate and individual-level decisions within models simultaneously results in the best alignment with human perceptions of subjective attributes. Further implications for design include the potential for personalized control over subjective attributes for designers, users, or users-as-designers.
@inproceedings{nandySubjectiveAttributes2023,title={Adaptive Optimization of Subjective Design Attributes: Characterizing Individual and Aggregate Perceptions},booktitle={{{ASME}} 2023 {{International Design Engineering Technical Conferences}} and {{Computers}} and {{Information}} in {{Engineering Conference}}},author={Nandy, Ananya and {Goucher-Lambert}, Kosa},year={2023},month=aug,publisher={{American Society of Mechanical Engineers Digital Collection}}}
ICED
VR or Not? Investigating Interface Type and User Strategies for Interactive Design Space Exploration
Computational design tools allow the generation of vast numbers of possible designs, entrusting the human designer with describing constraints or specifications to guide exploration of the design space. Designers can have many different decision considerations when conducting this type of exploration, including form, function, users, or context. In this work, we investigate strategies that emerge when people are tasked with exploring a large design space within either a non-immersive (2D) or immersive (VR) interface and equipped with action-based interactions to set or envision specifications related to their considerations. Results from a 28 participant user study uncovers that people have varying strategies to enact their decision considerations that are not unique to the type of interface. However, the interfaces differ in perceptions of enabling breadth or depth of exploration holistically, with preference towards 2D interfaces to compare options, and VR to understand single designs. These results have implications for the user experience of systems that allow designers to explore the outputs of large design spaces, both at the interaction and interface levels.
@inproceedings{nandyVRorNot2023,title={VR or Not? Investigating Interface Type and User Strategies for Interactive Design Space Exploration},booktitle={International Conference on Engineering Design '23},author={Nandy, Ananya and Smith, James and Jennings, Nicholas and Kuniavsky, Mike and Hartmann, Bjoern and {Goucher-Lambert}, Kosa},year={2023},month=jul}
2022
DCC
Best Paper
How does machine advice influence design choice? The effect of error on design decision making
Engineering design relies on the human ability to make complex decisions, but design activities are increasingly supported by computation. Although computation can help humans make decisions, over- or under-reliance on imperfect models can prevent successful outcomes. To investigate the effects of assistance from a computational agent on decision making, a behavioral experiment was conducted (N = 33). Participants chose between pairs of aircraft brackets while optimizing the design across competing objectives (mass and displacement). Participants received suggestions from a simu-lated model which suggested correct (i.e., better) and incorrect (i.e., worse) designs based on the global design space. In an uncertain case, both options were approximately equivalent but differed along the objectives. The results indicate that designers do not follow suggestions when the relative design performances are notably different, often underutilizing them to their detriment. However, they follow the suggestions more than expected when the better design choice is less clear.
@inproceedings{nandyMachineAdviceDesignChoice2022,title={How does machine advice influence design choice? The effect of error on design decision making},booktitle={Design Computing and Cognition '22},author={Nandy, Ananya and {Goucher-Lambert}, Kosa},year={2022},month=jul,langid={english}}
CHI LBW
GeneratiVR: Spatial Interactions in Virtual Reality to Explore Generative Design Spaces
Computational design tools can automatically generate large quantities of viable designs for a given design problem. This raises the challenge of how to enable designers to efficiently and effectively evaluate and select preferred designs from a large set of alternatives. In GeneratiVR, we present two novel interaction techniques to address this challenge, by leveraging Virtual Reality for rich, spatial user input. With these interaction methods, users can directly manipulate designs or demonstrate desired design functionality. The interactions allow users to rapidly filter through an expansive design space to specify or find their preferred designs.
@inproceedings{jennings2022generativr,title={GeneratiVR: Spatial Interactions in Virtual Reality to Explore Generative Design Spaces},author={Jennings, Nicholas and Nandy, Ananya and Zhu, Xinyi and Wang, Yuting and Sui, Fanping and Smith, James and Hartmann, Bjoern},booktitle={CHI Conference on Human Factors in Computing Systems Extended Abstracts},pages={1--6},year={2022},month=may,doi={10.1145/3491101.3519616}}
J. Mech. Des.
Do human and computational evaluations of similarity align? An empirical study of product function
Function drives many early design considerations in product development, highlighting the importance of finding functionally similar examples if searching for sources of inspiration or evaluating designs against existing technology. However, it is difficult to capture what people consider is functionally similar and therefore, if measures that quantify and compare function using the products themselves are meaningful. In this work, human evaluations of similarity are compared to computationally determined values, shedding light on how quantitative measures align with human perceptions of functional similarity. Human perception of functional similarity is considered at two levels of abstraction: (1) the high-level purpose of a product and (2) how the product works. These human similarity evaluations are quantified by crowdsourcing 1360 triplet ratings at each functional abstraction and creating low-dimensional embeddings from the triplets. The triplets and embeddings are then compared to similarities that are computed between functional models using six representative measures, including both matching measures (e.g., cosine similarity) and network-based measures (e.g., spectral distance). The outcomes demonstrate how levels of abstraction and the fuzzy line between “highly similar” and “somewhat similar” products may impact human functional similarity representations and their subsequent alignment with computed similarity. The results inform how functional similarity can be leveraged by designers, with applications in creativity support tools, such as those used for design-by-analogy, or other computational methods in design that incorporate product function.
@article{nandyHumanComputationalEvaluations2022,title={Do {{human}} and {{computational evaluations}} of {{similarity align}}? {{An empirical study}} of {{product function}}},shorttitle={Do {{Human}} and {{Computational Evaluations}} of {{Similarity Align}}?},author={Nandy, Ananya and {Goucher-Lambert}, Kosa},year={2022},month=apr,journal={Journal of Mechanical Design},volume={144},number={4},pages={041404},issn={1050-0472},doi={10.1115/1.4053858}}
J. Mech. Des.
Evaluating Quantitative Measures for Assessing Functional Similarity in Engineering Design
Nandy, Ananya, Dong, Andy, and Goucher-Lambert, Kosa
The development of example-based design support tools, such as those used for design-by-analogy, relies heavily on the computation of similarity between designs. Various vector- and graph-based similarity measures operationalize different principles to assess the similarity of designs. Despite the availability of various types of similarity measures and the widespread adoption of some, these measures have not been tested for cross-measure agreement, especially in a design context. In this paper, several vector- and graph-based similarity measures are tested across two datasets of functional models of products to explore the ways in which they find functionally similar designs. The results show that the network-based measures fundamentally operationalize functional similarity in a different way than vector-based measures. Based upon the findings, we recommend a graph-based similarity measure such as NetSimile in the early stages of design when divergence is desirable and a vector-based measure such as cosine similarity in a period of convergence, when the scope of the desired function implementation is clearer.
@article{nandyEvaluatingQuantitativeMeasures2021,title={Evaluating {{Quantitative Measures}} for {{Assessing Functional Similarity}} in {{Engineering Design}}},author={Nandy, Ananya and Dong, Andy and {Goucher-Lambert}, Kosa},year={2022},month=mar,journal={Journal of Mechanical Design},volume={144},number={3},pages={031401},issn={1050-0472},doi={10.1115/1.4052302}}
2021
IDETC
Nomination
Aligning Human and Computational Evaluations of Functional Design Similarity
Nandy, Ananya, and Goucher-Lambert, Kosa
In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Aug 2021
Nomination
Aligning Human and Computational Evaluations of Functional Design SimilarityIn ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Aug 2021
A Comparison of Vector and Network-Based Measures for Assessing Design SimilarityIn ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Aug 2020
Fabrication Process and Electromechanical Characterization of a Sliding Connector System for Modular Stretchable ElectronicsMassachusetts Institute of Technology, Bachelor’s Thesis May 2019