Aakash Varambhia - Delivering state of the art imaging data science to aid research and development

Johnson Matthey is focused on catalyst design and optimization, working at multiple scales from atomic to device level to analyze and improve catalytic materials

Main challenges include processing terabyte-scale imaging datasets, dealing with proprietary data formats, and making analysis tools accessible to non-coding scientists

Core tech stack uses Python with libraries like scikit-image, Dash for web interfaces, and Q-Pi for GPU acceleration. Focus on balancing commercial needs with open source tools

Data analysis pipeline includes acquisition, reconstruction, alignment, segmentation and reporting - automated through web interfaces that simplify complex workflows

Team uses multiple approaches for deployment including VMs, servers near instruments, and cloud platforms depending on specific needs

Development happens through a mix of in-house work by core data science team and academic partnerships/sponsorships, especially through Oxford

Key focus areas include improving pore network structures, nanoparticle analysis, and atomic-level imaging to understand catalyst performance

Web platform allows scientists to upload data, run analysis, and generate reports without coding while preserving complex analysis capabilities

Emphasis on making tools performant and user-friendly while handling large datasets through techniques like Dask for parallelization

Future goals include enabling real-time analysis and expanding capabilities for live microscopy data processing