Data Science Institute

Data Scientist Spotlight

Harleen Kaur

Data Engineer Lead

What is your role here at the Lab? I am currently a Data Engineer Lead on the Data Lifecycle Management team in the Computing Applications, Simulations, and Quality Division.

When did you first start at Livermore? I joined the lab on October 31st, 2022. This Halloween marked my third year at LLNL—and yes, I always try to dress up for the occasion.

What did you study in your path to this career? I studied at the University of California, Santa Barbara with a B.A. in economics and B.S. in statistics & data science. This combination helped me balance domain expertise with the technical skills needed to build and manage complex data solutions.

What project(s) are you currently working on? I lead a team of software and data engineers, developing data workflows, architecture, and analytic solutions for a wide range of stakeholders. My focus is on the full data lifecycle: designing robust workflows, ensuring experimental data is archived and accessible, and building dashboards that turn data into actionable insights. I am also working on data readiness to enable the use of AI in scientific applications.

ParaView-MCP levels the playing field for complex scientific visualization

Visualizing the structures, forces, and processes involved in scientific research projects is essential for clearly conveying complex aspects of experiments. However, outside of designing, running, and analyzing their experiments, few scientists have enough time to commit to learning the software tools used to create scientific visualizations.

LLNL researchers Shusen Liu, Haichao Miao, and Peer-Timo Bremer from LLNL’s Center for Applied Scientific Computing (CASC) set out to enable scientists to make use of a visualization tool without extensive training and allow for direct, expert input into the visualization design process. They focused on ParaView, a premier, open-source application for scientific visualization used across the National Laboratories. The outcome of their study, called ParaView-MCP, empowers users to interact with the application through natural-language and visual inputs instead of the typical graphic user interface (GUI), which can appear daunting for novice users. Their work helps lower the barrier of entry to using ParaView’s capabilities while empowering users by autonomously conducting analysis of complex datasets via AI-driven decision support.