Nathan Moeller

• Currently working on the Ingest Processing team, which develops and maintains services that process all datapoints within Splunk Observability. These services are responsible for parsing raw metric data, fetching associated MTS data (Metric Time Series), matching metric rules and routing to the correct storage layer. These services operate at a massive scale, processing billions of datapoints per minute.
• Leading development of a project to automatically route metrics to a cheaper storage layer, saving customers 10x on storage costs for their unused metrics.

• Developed Metrics Pipeline Management feature within Splunk Observability. This application on allows customers to create custom metric pipelines to filter, aggregate, and route their data. This work consisted of creating and implementing a database model for storing metric rules, gRPC API for internal services to create and update metric rules, a REST API for external users to interact with metric rules, and finally a UI for a way to interact with metric rules.

• Worked on IBM Cloud Pak for Data, an AI platform for businesses to collect, organize, and analyze data.
• Developed core platorm features including service provisioning, service life cycle management, and service extensions. This included an extensible UI framework for consuming services to create a functional service provisioning workflow by only providing a configuration schema. This framework is used by over 50 internal and external services provided by Cloud Pak for Data. Stack included React, GoLang, CockroachDB, and Kubernetes.

• Worked on a desktop application, SPSS Statistics. Re-wrote the entire front end experience using React and Electron. Decoupled backend statistics engine from frontend by writing a java REST API middlelayer.

• While receiving my graduate degree at the University of Minnesota, I worked as a research assistent to Professor Genevieve Melton-Meaux. I worked on a team of graduate students and surgeons to develop machine learning methods to detect post-surgical complications. My main responsibilities were to work with surgeons to understand what data we could use to predict a post-surgical complication (Ex: UTI), and then use this information to extract, clean, and format the EHR data to use for machine learning purposes. I also worked on developing the penalized regression algorithm we used to predict the surgical complication.
• Publication: Accelerating Chart Review Using Automated Methods on Electronic Health Record Data for Postoperative Complications

While in graudate school I took a variety of classes including Artificial Intelligence, machine learning, robotics, and experimental games

While in undergraduate school I took many classes to grow my knowledge and interest in computer science. I really enjoyed taking classes like algorithms, graphics, and database systems.