Daniel Rammer

Daniel Rammer is a software engineer at Union.ai. Daniel has also previously worked as a graduate research assistant at Colorado State University, where they focused on distributed systems, and as a solutions developer at VersiFit Technologies. Daniel began their career as a tutor at the University of Wisconsin-Oshkosh.

Rammer is known for their work on improving spatiotemporal bounded analytics performance by up to 4x and reducing disk and network I/O by 3 orders of magnitude. Daniel has also developed an HDFS-compliant distributed file system which leverages lossy compression to provide near in-memory analytics speeds (up to 500x faster than on-disk HDFS) over large, spatiotemporal datasets.

Daniel Rammer has a PhD in Computer Science from Colorado State University, with a focus on Distributed Systems. Daniel also has an MS in Computer Science from Colorado State University, with a focus on Network Security. Daniel's undergraduate degree is in Computer Science from the University of Wisconsin Oshkosh. Daniel is certified as an Apache Cassandra Developer by O'Reilly Media.

Daniel Rammer reports to Haytham Abuelfutuh, Software Engineer & CTO. Some of their coworkers include Yee Hing Tong - Software Engineer, Niels Bantilan - Machine Learning Engineer, and Carina Ursu - Software Engineer.