At Wildcat, I enhance battery research efficiency through Data, AI, Software, and IT Solutions.Core Battery Data System- Architected a scalable, cloud-native battery data pipeline handling > 1 M files monthly delivering processed cell results within an hour.- Developed a battery LIMS managing metadata for thousands of cells monthly.- Implemented a laboratory SCADA system for real-time data ingestion from laboratory and process equipment, enabling continuous process monitoring.- Integrated robotic automation tools (e.g., liquid handlers and powder dispensers) via custom control software and/or API integration to improve lab efficiency, reduce errors, and enhance control.- Building pipelines for automatic data ingestion from analytical tools, delivering results to scientists within minutes to an hour.- Architected a battery data warehouse combining pipeline results, metadata, and analytical outcomes from cell components.AI, ML & Advanced Analytics- Fostered collaboration between scientists and ML engineers, identifying impactful projects and key error metrics.- Enabled secure AI access to real-time laboratory data streams for model development and deployment.- Developed predictive models for battery performance, introducing advanced evaluation metrics and visualizations beyond simple MAE/MSE.- Created flexible analysis software and open data access to empower scientists and reduce silos.Continuous Improvement & Tech Stack Modernization- Led migration of corporate assets to the cloud (Azure, O365), eliminating on-prem hardware and enhancing scalability with PaaS solutions.- Seamlessly transitioned from a legacy on-prem battery data pipeline to a cloud-native system over a weekend without disruption.- Directed tech stack upgrades with modern tools (React.js, .Net Core), implementation of CI/CD practices and migrating R&D machine control from monolithic desktop apps to a distributed IoT approach.

Guide materials companies toward powerful and cost effective means of laboratory data collection, processing, consolidation and visualization.

Built and deployed predictive models in diverse industries including,- Predictive maintenance of semiconductor fab equipment by modeling time series data from hundreds of sensors per tool, each generating data at kHz frequencies- Risk assessment of key industry assets, including interpolation of missing attribute data for inaccessible components

Lead group responsible for Data Infrastructure at Kinestral, a data-driven company spanning 2 sites and multiple groups ranging from Chemistry R&D, High-Throughput/Combinatorial Research to Process R&D, Scale-Up.Specifically,- Defined Data Architecture- Architected, implemented and maintain production databases and Data Warehouse- Developed data harvesting code, Web-APIs, Webpages, data reduction and ETL code- Deployed Tableau Server and built data visualization objects - Support and encourage engineers interaction with data (data processing, manipulation and query)

- Designed, built and deployed a site-wide Informatics system to support chemistry, high-throughput, and process development R&D, including experiment design, tool integration, and data harvesting, processing and visualization.- Led development of high throughput workflows for electrochromic materials and devices.- Developed new materials research and testing strategies

- The initial Scientific hire. Recruited the initial scientific team (5 Ph.Ds, 6 R.A.s), managed our first high-throughput program, secured major company milestone and funding event. - Managed multiple research projects leading to discoveries such as a battery cathode material with 25 % increase in energy density and a hydrogen storage material with 10x improvement in absorption rate. - Developed high throughput (combinatorial) workflows in bulk materials synthesis and hydrogen storage.- Scaled up lithium ion cathode materials from g to multi-kg quantities.- Supervised >20 collaborative R&D projects with materials suppliers, device manufacturers and OEMs.

Initiated techniques to synthesize new non-oxide materials with potential for correlated electronic behavior. Techniques included lithium metal flux growth of new borides and high-pressure (GPa) synthesis of nitrides