Kosa Goucher-Lambert

About

Dr. Kosa Goucher-Lambert is an Associate Professor of Mechanical Engineering at the University of California, Berkeley, and an Affiliate Faculty member in the Jacobs Institute of Design Innovation, Berkeley Institute of Design, and UC Berkeley Human Computer Interaction Group. He serves as the Associate Director of the UC Berkeley Master of Design program. His expertise lies in engineering design theory, methods, and automation, with research focusing on decision-making applied to engineering teams and individuals, ideation and creativity, analogical reasoning in design, preference modeling, and design attribute optimization. His work also encompasses design cognition, neuroimaging methods applied to design, sustainable design, new product development, crowdsourcing, and collaboration. Dr. Goucher-Lambert has received several awards, including an NSF CAREER Award, the 2022 ASME Design Theory and Methodology Young Investigator Award, and the 2019 Excellence in Design Science Award. He has also earned multiple best paper awards from the American Society of Mechanical Engineers and the Design Society.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Engineering drawing
• Knowledge management
• Engineering
• Geometry
• Cognitive psychology
• Mathematics
• Social psychology

Selected publications

• Human confidence in artificial intelligence and in themselves: The evolution and impact of confidence on adoption of AI advice

  Computers in Human Behavior · 2021 · 217 citations

  Artificial intelligence (AI) has shown its promise in assisting human decision-making. However, humans' inappropriate decision to accept or reject suggestions from AI can lead to severe consequences in high-stakes AI-assisted decision-making scenarios. This problem persists due to insufficient understanding of human trust in AI. Therefore, this research studies how two types of human confidence that affect trust, their confidence in AI and confidence in themselves, evolve and affect humans’ deci…

  DOI

• ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional generative model

  Computer Methods in Applied Mechanics and Engineering · 2023 · 53 citations

  In this work, we introduce ShipHullGAN, a generic parametric modeller built using deep convolutional generative adversarial networks (GANs) for the versatile representation and generation of ship hulls. At a high level, the new model intends to address the current conservatism in the parametric ship design paradigm, where parametric modellers can only handle a particular ship type. We trained ShipHullGAN on a large dataset of 52,591 physically validated designs from a wide range of existing ship…

  DOI

• Adaptive Inspirational Design Stimuli: Using Design Output to Computationally Search for Stimuli That Impact Concept Generation

  Journal of Mechanical Design · 2020 · 53 citations

  1st authorCorresponding

  Abstract Design activity can be supported using inspirational stimuli (e.g., analogies, patents) by helping designers overcome impasses or in generating solutions with more positive characteristics during ideation. Design researchers typically generate inspirational stimuli a priori in order to investigate their impact. However, for a chosen stimulus to possess maximal utility, it should automatically reflect the current and ongoing progress of the designer. In this work, designers receive compu…

  DOI

• Do Large Language Models Produce Diverse Design Concepts? A Comparative Study with Human-Crowdsourced Solutions

  Journal of Computing and Information Science in Engineering · 2024-12-05 · 8 citations

  articleSenior author

  Abstract Access to large amounts of diverse design solutions can support designers during the early stage of the design process. In this article, we explored the efficacy of large language models (LLMs) in producing diverse design solutions, investigating the level of impact that parameter tuning and various prompt engineering techniques can have on the diversity of LLM-generated design solutions. Specifically, we used an LLM (GPT-4) to generate a total of 4000 design solutions across five disti…

  PublisherDOI

• StoryDiffusion: How to Support UX Storyboarding With Generative-AI

  2025-10-11 · 4 citations

  articleOpen access

  Storyboarding is an established method for designing user experiences. Generative AI can support this process by helping designers quickly create visual narratives. However, existing tools mainly focus on improving the accuracy of text-to-image generation. There is a lack of understanding on how to effectively support the entire creative process of storyboarding and how to develop AI-powered tools to be integrated into designers' diverse workflows. In this work, we designed and developed StoryDi…

  PublisherDOI

Recent grants

• CAREER: Synthesizing Neuroimaging and Data Driven Approaches to Discover Inspirational Examples in Design

  NSF · $581k · 2022–2027

Frequent coauthors

• Jonathan Cagan

  Carnegie Mellon University

  25 shared

• Vivek Rao

  18 shared

• Alice M. Agogino

  16 shared

• Elisa Kwon

  10 shared

• Ananya Nandy

  Vanderbilt University Medical Center

  9 shared

• Christopher McComb

  9 shared

• Kenneth Kotovsky

  Carnegie Mellon University

  8 shared

• Yakira Mirabito

  University of California, Berkeley

  8 shared

Education

• Ph.D., Mechanical Engineering

  University of California, Berkeley

  2015

• M.S., Mechanical Engineering

  University of California, Berkeley

  2012

• B.S., Mechanical Engineering

  University of California, Berkeley

  2010

Awards & honors

• NSF CAREER Award
• 2022 ASME Design Theory and Methodology Young Investigator A…
• 2019 Excellence in Design Science Award
• several best paper awards from the American Society of Mecha…

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