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Sarah Poon
Sarah Poon is a User Experience (UX) designer and researcher, who focuses on applying modern design principles to improve scientific computing. She has worked closely with many scientific projects and user facilities at LBNL, including the ALS, NERSC, KBase, and CCSI. View examples of her work.
Sarah is particularly interested in how UX can be used to design UIs for the "expert use" systems that are endemic to scientific computing, such as software to perform complex and technical workflows, to analyze and manage large volumes of data, and to support highly focused tasks requiring quick decision making in real-time. Correctness and accuracy are critical in these environments. At the same time, she aims to design software that is intuitive and even "delightful" to use.
Sarah is also researching how to create "design systems" that leverage commonalities across scientific projects and lower the barrier for scientists to create their own UIs.
Sarah received her Masters in Information Management and Systems from the School of Information at UC Berkeley.
Journal Articles
Cecilia Aragon, Stephen Bailey, Sarah Poon, Karl Runge, and Rollin Thomas, "Sunfall: A Collaborative Visual Analytics System for Astrophysics", J. Phys.: Conf. Ser. 125 012091 (Proceedings of SciDAC 2008), 2008, LBNL 657E,
- Download File: jpconf8125012091.pdf (pdf: 3.3 MB)
Conference Papers
Devarshi Ghoshal, Ludovico Bianchi, Abdelilah Essiari, Drew Paine, Sarah Poon, Michael Beach, Alpha N'Diaye, Patrick Huck, Lavanya Ramakrishnan, "Science Capsule: Towards Sharing and Reproducibility of Scientific Workflows", 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS), November 15, 2021, doi: 10.1109/WORKS54523.2021.00014
Workflows are increasingly processing large volumes of data from scientific instruments, experiments and sensors. These workflows often consist of complex data processing and analysis steps that might include a diverse ecosystem of tools and also often involve human-in-the-loop steps. Sharing and reproducing these workflows with collaborators and the larger community is critical but hard to do without the entire context of the workflow including user notes and execution environment. In this paper, we describe Science Capsule, which is a framework to capture, share, and reproduce scientific workflows. Science Capsule captures, manages and represents both computational and human elements of a workflow. It automatically captures and processes events associated with the execution and data life cycle of workflows, and lets users add other types and forms of scientific artifacts. Science Capsule also allows users to create `workflow snapshots' that keep track of the different versions of a workflow and their lineage, allowing scientists to incrementally share and extend workflows between users. Our results show that Science Capsule is capable of processing and organizing events in near real-time for high-throughput experimental and data analysis workflows without incurring any significant performance overheads.
NC Chen, SS Poon, L Ramakrishnan, CR Aragon, "Considering time in designing Large-Scale systems for scientific computing", Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW, 2016, 27:1535--1547, doi: 10.1145/2818048.2819988
Lavanya Ramakrishnan, Sarah S. Poon, Val C. Hendrix, Dan K. Gunter, Gilberto Z. Pastorello, Deb A. Agarwal, "Experiences with User-Centered Design for the Tigres Workflow API", Proceedings of the 10th IEEE International Conference on e-Science (e-Science 2014), Guaruja, Brazil, 2014, doi: 10.1109/eScience.2014.56
JR Balderrama, M Simonin, L Ramakrishnan, V Hendrix, C Morin, D Agarwal, C Tedeschi, "Combining workflow templates with a shared space-based execution model", Proceedings of WORKS 2014: The 9th Workshop on Workflows in Support of Large-Scale Science - held in conjunction with SC 2014: The International Conference for High Performance Computing, Networking, Storage and Analysis, 2014, 50--58, doi: 10.1109/WORKS.2014.14
Cecilia Aragon, Sarah Poon, Gregory Aldering, Rollin Thomas, and Robert Quimby, "Using Visual Analytics to Maintain Situational Awareness in Astrophysics", Proceedings of 2008 IEEE Symposium on Visual Analytics Science and Technology, Columbus, OH, USA, IEEE Computer Society Press, October 2008, LBNL 658E,
- Download File: aragonvast08.pdf (pdf: 448 KB)
Sarah Poon, Rollin Thomas, Cecilia Aragon, and Brian Lee, "Context-Linked Virtual Assistants for Distributed Teams: An Astrophysics Case Study", CSCW 2008: ACM Conference on Computer Supported Cooperative Work, 2008,
- Download File: cscw08contextlinked.pdf (pdf: 625 KB)
Reports
Drew Paine, Sarah Poon, Lavanya Ramakrishnan, "Investigating User Experiences with Data Abstractions on High Performance Computing Systems", June 29, 2021, LBNL LBNL-2001374,
Scientific exploration generates expanding volumes of data that commonly require High Performance Computing (HPC) systems to facilitate research. HPC systems are complex ecosystems of hardware and software that frequently are not user friendly. The Usable Data Abstractions (UDA) project set out to build usable software for scientific workflows in HPC environments by undertaking multiple rounds of qualitative user research. Qualitative research investigates how individuals accomplish their work and our interview-based study surfaced a variety of insights about the experiences of working in and with HPC ecosystems. This report examines multiple facets to the experiences of scientists and developers using and supporting HPC systems. We discuss how stakeholders grasp the design and configuration of these systems, the impacts of abstraction layers on their ability to successfully do work, and the varied perceptions of time that shape this work. Examining the adoption of the Cori HPC at NERSC we explore the anticipations and lived experiences of users interacting with this system's novel storage feature, the Burst Buffer. We present lessons learned from across these insights to illustrate just some of the challenges HPC facilities and their stakeholders need to account for when procuring and supporting these essential scientific resources to ensure their usability and utility to a variety of scientific practices.
