Hi! I’m Ava Robinson.

I'm a software engineer with experience developing, designing, and researching novel products including augmented reality and mobile experiences. I'm passionate about creating products to improve people’s lives.

Most recently, I was a Research Engineer at Snap Inc. on the Human-Computer Interaction Research Team. My work focused on building prototypes and launching experimental technologies that enable people to connect and collaborate in new ways.

In 2021, I graduated Magna Cum Laude from Northwestern University with a B.S. in Computer Science as well as a Design Certificate and Integrated Marketing Communications Certificate.

Offline, I’m a yogi, dancer, and bubblegum enthusiast.

Currently looking for full-time opportunities and would love to connect!


Beyond Screens: IoT + AR

Integrating IoT devices with mobile AR for immersive experiences.

Project IRL: AR for Co-location

Developing and launching a suite of AR experiences for co-located interactions.

Knowledge Maps

An interface to help users build a knowledge map of grid CSS techniques.

Delta Lab

Detecting student perceptions of programming from interaction log data.

Human-Centered ML

Predicting the risk for depression via Tweets via an interactive system.

Exploring Veganism

Visualizing and explaining the benefits of a vegan diet.

Internal Tracking GUI

Supporting the analysis of CureMetrix's mammogram analysis algorithm.

Google Scholar Profile

Academic Publications

Project IRL: Playful Co-Located Interactions with Mobile Augmented Reality (CSCW '22)

Dagan, E., Cárdenas Gasca, A. M., Robinson, A., Noriega, A., Tham, Y. J., Vaish, R., & Monroy-Hernández, A. (2022). Project IRL: Playful Co-Located Interactions with Mobile Augmented Reality. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW1), 1-27.

We present Project IRL (In Real Life), a suite of five mobile apps we created to explore novel ways of supporting in-person social interactions with augmented reality. In recent years, the tone of public discourse surrounding digital technology has become increasingly critical, and technology's influence on the way people relate to each other has been blamed for making people feel "alone together," diverting their attention from truly engaging with one another when they interact in person. Motivated by this challenge, we focus on an under-explored design space: playful co-located interactions. We evaluated the apps through a deployment study that involved interviews and participant observations with 101 people. We synthesized the results into a series of design guidelines that focus on four themes: (1) device arrangement (e.g., are people sharing one phone, or does each person have their own?), (2) enablers (e.g., should the activity focus on an object, body part, or pet?), (3) affordances of modifying reality (i.e., features of the technology that enhance its potential to encourage various aspects of social interaction), and (4) co-located play (i.e., using technology to make in-person play engaging and inviting). We conclude by presenting our design guidelines for future work on embodied social AR.
Read more
An approach for detecting student perceptions of the programming experience from interaction log data. (AIED‘21)

Gorson, J., LaGrassa, N., Hu, C. H., Lee, E., Robinson, A. M., & O’Rourke, E. (2021, June). An approach for detecting student perceptions of the programming experience from interaction log data. International Conference on Artificial Intelligence in Education (pp. 150-164). Springer, Cham.

Student perceptions of programming can impact their experiences in introductory computer science (CS) courses. For example, some students negatively assess their own ability in response to moments that are natural parts of expert practice , such as using online resources or getting syntax errors. Systems that automatically detect these moments from interaction log data could help us study these moments and intervene when the occur. However, while researchers have analyzed programming log data, few systems detect pre-defined moments, particularly those based on student perceptions. We contribute a new approach and system for detecting programming moments that students perceive as important from interaction log data. We conducted retrospective interviews with 41 CS students in which they identified moments that can prompt negative self-assessments. Then we created a qualitative codebook of the behavioral patterns indicative of each moment, and used this knowledge to build an expert system. We evaluated our system with log data collected from an additional 33 CS students. Our results are promising, with F1 scores ranging from 66% to 98%. We believe that this approach can be applied in many domains to understand and detect student perceptions of learning experiences.
Read more

Patents Granted

Multi-user AR experience with offline synchronization.
Monroy-Hernández, A., Robinson, A., Tham, Y. J., & Vaish, R. (2022). U.S. Patent No. 11,383,156. Washington, DC: U.S. Patent and Trademark Office.
Colocated shared augmented reality without shared backend. June 14, 2022. Patent Number: 11360733.
Gasca, A. M. C., Peled, E. D., Monroy-Hernández, A., Robinson, A., Tham, Y. J., & Vaish, R. (2022). U.S. Patent No. 11,360,733. Washington, DC: U.S. Patent and Trademark Office.
View my resume here.