About

Kyle Lewis is a Professor Emerita at the University of California, Santa Barbara, where she specializes in studying how organizations leverage individual and collective knowledge. Her research primarily examines team performance, especially those engaged in knowledge work such as professional services, new product development, science and engineering, and project-based tasks. Lewis has published extensively in top scholarly journals including Management Science, Academy of Management Review, Academy of Management Journal, Organization Science, and others, and has served in prominent editorial roles such as Senior Editor for Organization Science and Associate Editor for Management Science. Her work on team performance and innovation has received international recognition, and she has been honored with the Jay Wright Forrester Award for her research involving system dynamics, which is a computer-aided modeling approach to understanding phenomena over time with feedback in complex systems. Lewis's background includes early training at Duke University, where she earned degrees in Computer Science and Mathematics, followed by an M.S. in Industrial Administration and a Ph.D. in Management from the University of Maryland. She has professional experience in technology strategy, designing and implementing information systems, AI product management, and human resources management. Prior to her current position, she was a Professor of Management and Faculty Director of the Master of Science in Technology Commercialization at the University of Texas at Austin. Her research on team dynamics, cross-understanding, and organizational learning employs system dynamics modeling to explore how disruptive events impact organizational performance and knowledge integration. She has also contributed to teaching courses on leading people, managing diverse teams, and collaborative innovation, and has engaged in speaking engagements such as keynotes at UC Tech and UCSB Alumni Weekend.

Research topics

• Computer Science
• Artificial Intelligence
• Systems engineering
• Human–computer interaction
• Knowledge management
• Engineering

Selected publications

• Efficient and Robust Deep Learning for Real Time Plant Disease Detection Using CNN

  2025-01-23 · 2 citations

  article

  Early recognition of plant diseases is important to crop losses and ensure agricultural yields. Traditional manual methods are difficult,time-consuming, and unreliable, especially on large farms. This research supports on-the-fly deep learning methods and robustness analysis using CNN. This method analyzes leaf images to address common plant diseases such as Cercosporin leaf spot, bacterial blight, and viral diseases. The main functions are image capture, processing, segmentation, feature extraction and segmentation. The system combines a pipeline powered by a CNN architecture and is implemented using Streamlet to be highly accurate and efficient in disease detection and classification.

  PublisherDOI

• Recommendations for REFEDS Assurance Framework 2.0 Implementation for InCommon Identity Providers

  2025-07-01

  reportOpen access1st authorCorresponding
  PublisherOA PDFDOI

• SIRTFI Exercise Working Group End of 2023 Report

  2023-11-28

  reportOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Human Liver Organoids with myeloid lineages model the multi-cellular crosstalk in Non-Alcoholic Steatohepatitis

  Journal of Hepatology · 2022-07-01

  article
  PublisherDOI

• SIRTFI Exercise Working Group End of 2022 Report

  2022-12-15

  reportOpen access1st authorCorresponding
  PublisherOA PDFDOI

• Cross-understanding will help complex and diverse teams achieve mutually agreeable solutions

  London School of Economics and Political Science Research Online (London School of Economics and Political Science) · 2021-03-04

  articleOpen access

  Teams whose members have diverse backgrounds can experience differences in task knowledge, sensitivities to various aspects of the task system, as well as beliefs and preferences about how to best approach or solve a problem. How might managers deal with this? Niranjan Janardhanan, Kyle Lewis, Rhonda R. Reger, and Cynthia K. Stevens write that, rather than focusing on common ground, team leaders should emphasise cross-understanding. Understanding the bases of someone’s views will help get to the real reasons behind differences in opinion, and therefore help to achieve mutually agreeable solutions.

  PublisherOA PDF

• Team Cognition at a Crossroad: Forging the Way Forward

  Academy of Management Proceedings · 2021-07-26

  articleSenior author

  Team cognition enables members' diverse expertise and knowledge to be recognized, shared, and harnessed in teams. Over the past 30 years, the team cognition field has been regarded as an interdisciplinary success story. The empirically established team cognition – team performance link has also been noted as one of the most exciting developments in team research. Although there is much to celebrate, a careful evaluation of this literature reveals a surprising lack of shared cognition about team cognition, including little cross-integration across the multiple forms of team cognition (e.g., team mental models, transactive memory systems, team learning, cross-understanding, representational gaps). Moreover, team cognition measurement continues to be a significant challenge. How do we make intersections across previously siloed literatures normative rather than rare? How do we move from an emphasis on team aggregated measurement to more advanced techniques to permit answers to more expansive and nuanced research questions? Responding to these key issues, the panel will discuss the state-of-the-art in team cognition research and help craft a theoretical, methodological, and empirical agenda for future studies with audience participation.

  PublisherDOI

• Intelligent Machines and Teamwork: Help or Hindrance?

  Academy of Management Proceedings · 2020 · 4 citations

  • Computer Science
  • Artificial Intelligence
  • Knowledge management

  Intelligent machines are being deployed as human assistants in a variety of corporate, military, and healthcare settings. Research has generally examined how intelligent machines affect individual human behavior, but very few studies explore how intelligent machines impact teams and teamwork. We examine the performance and processes of teams using an embodied intelligent personal assistant (EIA) to complete a collaborative task. We expected that EIA use would enhance team performance on an intellective task but interfere with the development of a transactive memory system (TMS). A TMS is a collective memory system that, once developed, has strong positive effects on sustained team performance. Our findings show that under some conditions EIA use may be helpful to team performance initially, but harmful to the eventual development of a TMS. We highlight the need for new theorizing about the generative and destructive impacts of intelligent machine use in teams.

  PublisherDOI

• Funding the future of global health: A medical student perspective

  Columbia Academic Commons (Columbia University) · 2020-02-29

  articleOpen access

  As students from the Penn State College of Medicine (PSCOM) who are engaged in medical research projects in Ecuador, Kenya, Ethiopia and Peru, we recognize we are at a defining crossroad in global health; yet, our voices are seldom invited into public debate. For decades, global health has been synonymous with prevention and treatment of infectious diseases such as malaria and tuberculosis; today, however, the disease burden is shifting toward non-communicable diseases (NCDs) and future physicians will face the likelihood of having to make difficult decisions about the distribution of the scarce resources devoted to health care.1 The 2011 United Nations General Assembly Summit on Non-Communicable Diseases sparked a debate when members proposed increased funding for NCDs without acknowledging the negative impact such allocations would have on funding for preventing and treating infectious diseases.1 With our future careers and past experiences in mind, students from PSCOM have explored this conflict in depth and concluded that it is critical that we fight for continued funding of neglected tropical diseases (NTDs).

  PublisherDOI

• Getting to Know You: Motivating Cross-Understanding for Improved Team and Individual Performance

  Organization Science · 2019-11-27 · 34 citations

  articleOpen access

  Many contemporary organizations depend on team-based organizing to achieve high performance, innovate services and products, and adapt to environmental turbulence. Significant research focuses on understanding how teams develop, assimilate, and apply diverse information; yet, organizational practices have evolved in new ways that are not fully explored in the teams literature. Individuals with diverse motivations, knowledge, and perspectives are often assigned to teams, creating burdens for members to develop effective ways to work together, learn from each other, and achieve goals amid the complexity of today’s organizational contexts. In this paper, we examine a multilevel model of how team goal orientation affects cross-understanding—the extent to which team members understand the other members’ mental models—which in turn, affects team and individual performance. We examine these effects using 160 teams of 859 participants who completed a semester-long business simulation. Findings show that the more team members are motivated by learning goals, the greater a team’s cross-understanding and subsequent team and individual performance. These effects are dampened when members are motivated by performance goals—to avoid mistakes or prove competence. This study expands the cross-understanding literature, revealing motivational antecedents that explain why some teams develop higher cross-understanding than others. We also contribute to the goal orientation literature by demonstrating that team goal orientation influences members’ learning about other members and in so doing, also affects team and individual performance. Because team motivation can be influenced by organizational practices, our findings also contribute practical insights for organizational leaders.

  PublisherOA PDFDOI

Frequent coauthors

• Cynthia Kay Stevens

  39 shared

• Niranjan Srinivasan Janardhanan

  London School of Economics and Political Science

  38 shared

• Rhonda K. Reger

  31 shared

• Pedro Marques-Quinteiro

  Universidade Lusófona

  15 shared

• Luí­s Curral

  Museum für Naturkunde

  12 shared

• Ana Passos

  Iscte – Instituto Universitário de Lisboa

  12 shared

• Catarina Gomes

  9 shared

• George W. Huber

  University of Wisconsin–Madison

  7 shared

Education

• BS Computer Science; BA Mathematics

  Duke University

• Ph.D., Management

  University of Maryland, College Park

  1999

• MS - Industrial Administration

  Carnegie-Mellon University

  1990

Awards & honors

• Jay Wright Forrester Award (2018)