In the service of creating a training dataset discribing the taxonomy of bio masses and their possible classification as tumors. Given the importance for the user to understand the 3D spatial relationships of the data, and the need to inspect the structures while preserving context of the overall structure and placement, we arrived to a Virtual Reality based solution.

We created OVS+Tumor: a seamless VR environment designed for intuitive interaction aiding in the complex task of parsing through 3D CT-scans and annotating candidate tumors. Through interactive subsetting and on-the-fly iso-cloud generation, a wider range of users beyond just domain experts (radiologists/surgeons) can generate a viable machine-learning training dataset.

Self Organizing Maps (SOM) is an unsupervised machine learning technique that has become quite valuable for understanding a wide spectrum of biological processes, as it produces a "low-dimensional representation of a higher dimensional data space, while preserving the topological structure of the data". Understanding how a researcher can make best sense of the SOM data can be as straight forward as finding clear visual mechanisms to convey the mapped data into an interactive system which responds to the natural queries about specific populations that researchers would have after gaining a clear understanding of the overall structure of the data. After understanding what the needs to the researchers were, and how the representation would guide them into their exploration and analysis, an interactive web based tool was easily deployed to help answer researcher questions.

PIXLIZE (formerly PIXELATE) is a microXRF visualization and analysis tool built for NASA JPL's Mars 2020 rover PIXL instrument team. The tool is built to enable the team's search for signs of past life on Mars, as well as to explore the red planet's geologic history. Thus, the users central need is to have a tool which could visualize the spatial relationships and concentrations of elements in Martian rock samples in order to enable the search for signs of past life on the red planet.

The DARPA Data Driven Discovery of Models (D3M) program automates methods in data science to enable domain experts to incorporate their knowledge into the modeling process and create meaningful and valid predictive models of real, complex processes without the need for expert data scientists. All data from the D3M AutoML explorations is available for exploration through MARVIN. MARVIN is a visual interface that helps you navigate the rich D3M AutoML ecosystem. Powered by the metalearning database, which captures all assets, pipelines, and experiments run, MARVIN acts as a frontend to help explore and analyze metalearning resources.

MARVIN allows one to explore the datasets, problems, primitives, pipelines, and ML pipeline runs in the D3M Ecosystem. One can easily compare how selecting various primitives for data preprocessing, encoding, embedding, featurization, and modeling improves the accuracy of particular auto-generated pipelines (problem solutions). A dynamic leaderboard can be generated for any problem by ranking captured pipeline runs on the scored metric. Recognizing how a user chooses to interact with such a rich database, has led to solutions from simple (simple barchart) to complex (comparative exploration of different pipelines) that can be tackled at different levels of granularity of the data, at different levels in the data exploration, and targeted to different user groups.