Sara Beckman, Senior Lecturer

Closed (1) Teaming with Diversity for Innovative Output

Applications for fall 2021 are now closed for this project.

Solutions to today’s increasingly “wicked problems” demand teaming across disciplinary boundaries. Teaming in organizations is the “engine of organizational learning…a way of working that brings people together to generate new ideas, find answers and solve problems. But people have to learn to team; it doesn’t come naturally.” (Amy Edmondson, Harvard Business School)
Good teaming skills allow teams to leverage diversity to better frame and solve problems, and ultimately to better innovate. Diverse teams have been shown to either significantly under- or out-perform more homogeneous teams. They underperform when they are either blind to the diversity present, or treat the diversity with stereotypes. They outperform when they have a learning perspective that allows them to leverage the multiple bodies of knowledge that become available when they take this perspective.
Through this research project, we aim to create a robust curriculum and toolkit for students to be able to better learn about teaming, a skill that is required in any future work they will do. This project entails analyzing the data produced by the teaming questionnaires to discern the core problems that students experience on projects and then creating a user-friendly toolkit for them to use to improve their teaming capabilities and experiences.
This work aims to address the following research questions:
• What are the key success factors for making a good team experience on student projects?
• How might teaming tools be best provided to students to improve their immediate teaming experiences?
• How might a teaming toolkit be best leveraged over time to develop teaming capabilities?


We seek URAP candidates who are comfortable with ambiguity and interested in exploration. We expect to engage in the following activity in the fall:

Qualitative Data Analysis: Analysis of open-ended responses to mid-semester and end-of-semester peer evaluation surveys. For this work we need people who are facile with Natural Language Processing and building Neural Nets and know how to use that knowledge to unpack qualitative data.



Qualifications: All URAPs will be expected to be highly motivated, organized, and self-directed. Experience with qualitative research analysis. Comfort with ambiguity, curiosity to explore, interest in learning about teaming and the effects of diversity.

Weekly Hours: to be negotiated

Off-Campus Research Site: remote

Related website: https://www.teamingxdesign.com/

Closed (2) Understanding Design Thinking and Teaming by Product Managers

Applications for fall 2021 are now closed for this project.

Product Management is a critical role in most companies today. Product Managers sit between customers/users and the technology teams and are responsible for bring new customer/user experiences alive. We run an executive program for product managers that draws over 350 of them per year. We have surveyed those product managers to learn about how much they engage in design thinking and how well their teams work.

We seek URAP candidates who are comfortable with ambiguity and interested in exploration. We are looking for someone with background in machine learning who can work with us to take our large database and develop machine learning algorithms that will allow us to tag the data and extract insights from it. The dataset contains over 10,000 entries and so offers an opportunity to do a variety of analyses.

Qualifications: All URAPs will be expected to be highly motivated, and self-directed. This position requires understanding of machine learning algorithms and their application to qualitative datasets. Comfort with ambiguity, curiosity to explore, interest in learning about product management.

Weekly Hours: to be negotiated

Off-Campus Research Site: remote

Closed (3) Systems Mapping: Modeling Execution Systems

Closed. This professor is continuing with Spring 2021 apprentices on this project; no new apprentices needed for Fall 2021.

We live in a messy world today that requires understanding of systems dynamics. Our recent research has leveraged systems modeling using software such as kumu.io to capture the complex dynamics of a variety of systems.

In particular, we've been examining the dynamics that surround excelling at execution...getting the everyday work done that needs to be done to, for example, run the Cirque du Soliel, serve chicken sandwiches at Chick-fil-a, or fill orders at Amazon.

We've completed a partial literature review and conducted some interviews with people in these companies and others. We would like to take this research further, creating a publishable paper by the end of the Fall semester.

Our objectives for this semester:
Build the systems model that captures excellence in execution
Complete a literature review
Conduct more interviews with people involved in execution
Draft a paper for publication


This is a great chance to learn about system modeling, including kumu.io or other systems modeling software. It is also a great chance to learn about the other side of design and innovation -- getting the job done!, Post-Doc

Qualifications: All URAPs will be expected to be highly motivated, and self-directed. This project will require a high degree of organization. Experience with qualitative research analysis and conducting literature reviews would be great. Comfort with ambiguity, curiosity to explore, interest in learning about teaming and the effects of diversity.

Weekly Hours: to be negotiated

Closed (4) Nobel Prize Winners and Creativity

Closed. This professor is continuing with Spring 2021 apprentices on this project; no new apprentices needed for Fall 2021.

This is a joint project with Autodesk (and its Innovation Genome project) and the Nobel Foundation. Autodesk has for the past few years been studying the 1000 greatest innovations in history to uncover common principles that lead to innovation. In this project, we will examine Nobel prize winners, particularly those from UC Berkeley, Stanford University and UCSF to understand how they came to the findings that led to winning the Nobel Prize. We seek to find commonalities and differences among them, and thus to inform future generations of innovation work.

Very little work has been done to date to understand how Nobel Prize winners think, and what creative processes they use to generate their ideas. We will start our work with background research on Nobel Prize winners, and then plan to conduct interviews with winners in the local area. We will take the data we collect from these primary and secondary sources, and will look for patterns of behaviors and thinking approaches that are shared across the prize winners.

If you would like an opportunity to get to know some Nobel prize winners well, this is the project for you!

This is early-stage research, and so we seek URAP candidates who are comfortable with ambiguity and interested in exploration. We expect to engage in the following activities in the fall:

1)Literature review: We have already begun to review the small amount of literature on Nobel prize winners and creativity. We will complete that review this fall.
2) Nobel Prize winner background research: Before we conduct interviews in person with the prize winners, we will do some background research to learn about them -- their backgrounds, what they did to earn the prize, who they worked with, etc. We expect that students will be able to work in teams to do this work.
3) Interview Nobel Prize winners: Finally, we will create interview guides and deploy teams of URAPs to conduct interviews with the Nobel Prize winners, allowing us to dig more deeply into how they think, what they think allowed them to win.

Qualifications: All URAPs will be expected to be highly motivated, organized, and self-directed. Experience conducting ethnographic interviews and doing observation work would be great. Comfort with ambiguity, curiosity to explore, interest in learning about the lives and work of others.

Weekly Hours: to be negotiated