As the landscape of high-performance computing expands, support for machine learning workflows and interactivity becomes even more critical. JupyterLab, an evolution of the popular Jupyter Notebook, provides an interactive and extensible user interface, supporting many workflows in data science, scientific computing, and machine learning. With the integration of JupyterHub, users can access the power of high-performance systems like Polaris through JupyterLab. This not only enables interactive data analysis and visualization, but also streamlines tasks such as job submission and monitoring. 

Murat Keçeli is an assistant computational scientist at Argonne National Laboratory. With a background in quantum chemistry, he focuses on accelerating molecular simulations through high-performance computing and machine learning. He is also passionate about developing user-friendly codes for scientific computing and broadening access to high-performance computing. Murat received BS and MS degrees in physics from Bilkent University and a PhD degree in chemical physics from University of Illinois at Urbana-Champaign.

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