Jonathan Cagan

About

Jonathan Cagan is the George Tallman and Florence Barrett Ladd Professor in Engineering at Carnegie Mellon University. His career spans collaborative and innovative work in education, research, and industry. Cagan researches engineering design automation and methods, merging AI, machine learning, and optimization methods with cognitive science problem solving. One focal area is the cognitive basis and computational modeling of designer processes to improve the effectiveness of human designers. Another area includes computational methods for the design and diagnosis of biomechanical systems. An additional focus is in user-centered design and integrated product development practice. In the CMU way, he has collaborated with designers, engineers, psychologists, neuro-scientists, marketers, computer scientists, and architects in his work. At Carnegie Mellon, Cagan co-founded and co-directed the Integrated Innovation Institute. He served as Associate Dean for Graduate and Faculty Affairs, Chief Academic Officer, and Interim Dean of the College of Engineering. Cagan was recently honored with the Robert A. Doherty Award for Sustained Contributions to Excellence in Education. Active in professional societies and editorial boards, Cagan is a Fellow in the American Society of Mechanical Engineers and was awarded with the ASME Design Theory and Methodology Award. He has authored several books, over 250 publications, and is an inventor on multiple patents.

Research topics

• Computer Science
• Artificial Intelligence
• Knowledge management
• Engineering
• Psychology
• Human–computer interaction
• Radiology
• Pathology
• Biology
• Engineering management

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

  Senior authorCorresponding

  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

• A cautionary tale about the impact of AI on human design teams

  Design Studies · 2021 · 112 citations

  Recent advances in artificial intelligence (AI) offer opportunities for integrating AI into human design teams. Although various AIs have been developed to aid engineering design, the impact of AI usage on human design teams has received scant research attention. This research assesses the impact of a deep learning AI on distributed human design teams through a human subject study that includes an abrupt problem change. The results demonstrate that, for this study, the AI boosts the initial perf…

  DOI

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

  Journal of Mechanical Design · 2020 · 53 citations

  Senior 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

• Decoding the agility of artificial intelligence-assisted human design teams

  Design Studies · 2022 · 51 citations

  Senior authorCorresponding

  Although necessary for complex problem solving, such as engineering design, team agility is often difficult to achieve in practice. The evolution of Artificial Intelligence (AI) affords unique opportunities for supporting team problem solving. While integrating assistive AI agents into human teams has at times improved team performance, it is still unclear if, how, and why AI affects team agility. A large-scale human experiment answers these questions, revealing that, with appropriately interfac…

  DOI

• Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks

  American Journal Of Pathology · 2020 · 33 citations

  DOI

Recent grants

• EAGER: Innovative Energy Farm Design

  NSF · $66k · 2009–2011

• Determining Consumer Preference Through an Interactive Virtual Reality Experience

  NSF · $375k · 2012–2016

• MOD: Stimulating Creative Insight - A Cohesive Model of Design Innovation Across Individuals, Groups and Computer Agents

  NSF · $214k · 2008–2011

Frequent coauthors

• Kenneth Kotovsky

  Carnegie Mellon University

  106 shared

• Christopher McComb

  72 shared

• Philip R. LeDuc

  Université Grenoble Alpes

  27 shared

• Christian D. Schunn

  University of Pittsburgh

  26 shared

• Kosa Goucher-Lambert

  University of California, Berkeley

  25 shared

• Guanglu Zhang

  22 shared

• Nicolás F. Soria Zurita

  Pennsylvania State University

  21 shared

• Joshua T. Gyory

  Carnegie Mellon University

  19 shared

Awards & honors

• Robert A. Doherty Award for Sustained Contributions to Excel…
• Fellow in the American Society of Mechanical Engineers
• ASME Design Theory and Methodology Award

Similar researchers at Carnegie Mellon University

• Ana Maria Pinto da Silvah-79
• Jodi Forlizzih-74
• John Zimmermanh-52