Stephanie Lunn

Assistant Professor at SUCCEED

Lecture Information:
  • January 9, 2023
  • 3:00 PM
  • CASE 241 & Zoom

Speaker Bio

Stephanie J. Lunn is an Assistant Professor in the School of Universal Computing, Construction, and Engineering Education (SUCCEED) and the STEM Transformation Institute at Florida International University (FIU). Her research interests span the fields of computing education, human-computer interaction, data science, and machine learning. Previously, Dr. Lunn she served as a postdoctoral fellow at the Georgia Institute of Technology. She earned her doctoral degree in computer science from the Knight Foundation School of Computing and Information Sciences at FIU. She also holds B.S. and M.S. degrees in computer science from FIU and B.S. and M.S. degrees in neuroscience from the University of Miami.


We frequently hear about the ongoing issues in computing, from its lack of diversity to concerns about students’ struggles with the hiring process. Yet there are many things that are going well, such as the success stories of those who attain a computing job, the initiatives that strengthen disciplinary ties and aspirations, and the programs that help to build a community. In this talk, Dr. Lunn shares her prior and ongoing research around students’ professional and technical development. She describes strategies to cultivate cohesion within academia and industry in addition to opportunities for coupling between them to empower and support diverse populations throughout their trajectories in the field. Specifically, she focuses on topics such as: 1) data-driven approaches to extract insights using large educational datasets; 2) the computing touchpoints and experiences that provide engagement with the discipline (e.g., participation in hackathons or bootcamps); 3) the creation and assessment of digital tools to cultivate competencies and enhance technical interview preparation; and 4) efforts to raise awareness and promote consideration of ethical issues related to data privacy and security, algorithmic accountability, and system design and deployment.