publications

2026

1. On the Common Pitfalls of Designing and Communicating Within-Subjects Experiments in HCI

   London Bielicke, Emery D. Berger, Adam Chlipala, and 1 more author

   In Proceedings of the Extended Abstracts of the 2026 CHI Conference on Human Factors in Computing Systems, , 2026

   Abs DOI

   Well-designed experiments are essential for drawing valid statistical conclusions. In studies with limited resources (e.g., access to human participants), researchers often assign multiple conditions to the same participant (i.e., within-subjects experiments). Although such designs can increase statistical power, dependencies across trials within a participant may threaten the validity of the experiment. Unfortunately, domain-specific assumptions about these dependencies are often left implicit when conducting, analyzing, and communicating results from within-subjects experiments. We show that some common within-subjects experiments in the HCI community make assumptions that may not actually hold and provide a formal representation for precisely encoding these assumptions. We hope these results and examples will motivate changes to how we as a community reason about, design, and communicate experiments.

2. Causality and Semantic Separation

   Anna Zhang, Qinglan Luo, London Bielicke, and 2 more authors

   2026

2025

1. PLanet: Formalizing Assignment Procedures in the Design of Experiments

   London Bielicke, Anna Zhang, Shruti Tyagi, and 3 more authors

   2025

   arXiv preprint

   Abs

   Carefully constructed experimental designs are essential for drawing valid, generalizable conclusions from scientific experiments. Unfortunately, experimental designs can be difficult to specify, communicate clearly, and relate to alternatives. In response, we introduce a grammar of composable operators for constructing experimental assignment procedures (e.g., Latin square). The PLanet DSL implements this grammar. Researchers specify assignment requirements, which PLanet compiles into constraint-satisfaction problems over matrices to determine viable experimental plans. In an expressivity evaluation, we find that PLanet is the most expressive compared to two existing experimental-design libraries. Its composability enables expression of both canonical and customized designs in HCI experiments. Case studies with three researchers reveal how PLanet helps them make complex design choices explicit, explore alternatives, and develop a deeper understanding of experimental design.

2. "I spent 14 hours debugging just one assignment": Toward Computer-Mediated Personal Informatics for Computer Science Student Mental Health

   Aishwarya Chandrasekaran, London Bielicke, Diya Shah, and 2 more authors

   In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, May 2025

   Abs DOI

   Anxiety and depression rates in Computer Science (CS) students are double those of other undergraduates and 5-10 times higher than the general population. However, factors contributing to the elevated mental health issues in CS students remain unknown. To bridge this gap, we conducted need-finding interviews (N=20), which revealed that the complexity of debugging, along with imposter syndrome, are key contributors to stress and burnout. Participants expressed openness toward and feature preferences in a computer-based Personal Informatics (PI) tool to facilitate self-reflection. In response, we developed EmotionStream, an algorithm-assisted PI tool that provides both contextual and emotional insights based on individual behaviors. We found that participants rated their experience with the tool highly. Post-hoc analysis revealed that emotional states, augmented with contextual cues, show promise of predicting real-time stress. Based on our findings, we provide design implications for future PI tools to support CS student mental well-being.

3. Workplace Monitoring through the Lens of Future Information Workers

   Aishwarya Chandrasekaran, Diya Shah, London Bielicke, and 1 more author

   In CHI 2025 Workshop on Sociotechnical AI Governance, May 2025

   Abs

   Emerging technologies are transforming all areas of life, and the information workplace is at the forefront of innovation. Passive sensing technologies, with significant potential to monitor human behavior and offer insights, are particularly effective for information workers, as their computer-centric tasks enable seamless integration. These technologies can potentially provide valuable insights to support workers by tracking well-being and performance. Prior research has examined various stakeholders’ views, such as managers and employees, on workplace monitoring and its contesting notions of worker well-being and performance. However, with the fast-paced advancements in information technology and increasing anxiety among university students, attitudes regarding workplace culture and practices can impact the desirability and choice to pursue career options in Computer Science (CS). In this study, we recruited 20 university CS students to understand their perspectives on these technologies as future workers. Our findings surface how students perceived such technologies, whether and how their views differ from current workers, and how their views on organizational culture changed based on its adoption. From our findings, we propose initial implications for the design and adoption of future workplace monitoring technologies.

4. Charliecloud as a Kubernetes Container Runtime

   A. Loschak and L. Bielicke

   In SC 2025 Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC, Nov 2025