Sharing
To enhance data privacy and collaboration within the MapLab platform, I designed a new resource sharing model that transitioned the system from being public by default to private by default.
This "Share" modal is a key component of the new framework, giving resource Owners explicit control over who can view or edit their work. The design provides granular, user-level permissions (Owner, Editor, Viewer), a simple toggle to share with an entire account, and intelligent "upstream auto-sharing" to ensure that dependent resources are automatically shared for a seamless user experience. This solution empowers users with control over their work, meets critical compliance requirements, and facilitates focused, effective collaboration.
To enhance data privacy and collaboration within the MapLab platform, I designed a new resource sharing model that transitioned the system from being public by default to private by default.
This "Share" modal is a key component of the new framework, giving resource Owners explicit control over who can view or edit their work. The design provides granular, user-level permissions (Owner, Editor, Viewer), a simple toggle to share with an entire account, and intelligent "upstream auto-sharing" to ensure that dependent resources are automatically shared for a seamless user experience. This solution empowers users with control over their work, meets critical compliance requirements, and facilitates focused, effective collaboration.
Export
To make MapLab a more integrated part of our users' broader data ecosystem, I designed the platform's Export functionality.
The design, showcased in this modal, provides a clear and powerful workflow for sending datasets to external destinations like Snowflake. Recognizing that users have different needs, I incorporated options for both immediate "One-Time" transfers and automated "Scheduled" exports for recurring data pipelines. This feature is governed by the platform's permissions model, ensuring only authorized users (Editors and Owners) can move data, thereby enhancing both security and interoperability.
To make MapLab a more integrated part of our users' broader data ecosystem, I designed the platform's Export functionality.
The design, showcased in this modal, provides a clear and powerful workflow for sending datasets to external destinations like Snowflake. Recognizing that users have different needs, I incorporated options for both immediate "One-Time" transfers and automated "Scheduled" exports for recurring data pipelines. This feature is governed by the platform's permissions model, ensuring only authorized users (Editors and Owners) can move data, thereby enhancing both security and interoperability.
Codeset Recommendation
To ensure users could confidently adopt generative AI for complex medical analysis, my core design principle for MapAI was transparency. For users to trust an AI-generated recommendation, they must understand its reasoning.
This interface is designed to provide a "glass box" experience. Instead of just delivering a final answer, MapAI explains its step-by-step process: it confirms the user's intent, details the exact search parameters used, clarifies adjustments made to the query, and justifies why a new codeset was drafted. This "show your work" methodology is vital for building user trust, as it empowers clinical and research professionals to quickly validate the AI's logic and feel secure integrating its outputs into their workflows.
To ensure users could confidently adopt generative AI for complex medical analysis, my core design principle for MapAI was transparency. For users to trust an AI-generated recommendation, they must understand its reasoning.
This interface is designed to provide a "glass box" experience. Instead of just delivering a final answer, MapAI explains its step-by-step process: it confirms the user's intent, details the exact search parameters used, clarifies adjustments made to the query, and justifies why a new codeset was drafted. This "show your work" methodology is vital for building user trust, as it empowers clinical and research professionals to quickly validate the AI's logic and feel secure integrating its outputs into their workflows.
Dataflow Editor
To make complex data preparation accessible to a wider range of users, I designed the Dataflow editor, a visual canvas for building and managing data pipelines within MapLab.
This node-based interface allows users to intuitively connect different processing steps—from reading a cohort definition to outputting a final dataset—abstracting away the need for complex backend code. The design not only simplifies the creation of sophisticated workflows but also provides a clear, visual data lineage. This enhances reproducibility and makes the entire process easier to debug, validate, and securely share with colleagues using the platform's granular access controls.
To make complex data preparation accessible to a wider range of users, I designed the Dataflow editor, a visual canvas for building and managing data pipelines within MapLab.
This node-based interface allows users to intuitively connect different processing steps—from reading a cohort definition to outputting a final dataset—abstracting away the need for complex backend code. The design not only simplifies the creation of sophisticated workflows but also provides a clear, visual data lineage. This enhances reproducibility and makes the entire process easier to debug, validate, and securely share with colleagues using the platform's granular access controls.