My name is Bill Gleim, and I’m the latest member to join the team at Sprint.ly. I’m excited to join Sprint.ly – let me tell you why. I used Sprint.ly professionally before ever thinking about joining the team. The team is led by a guy that friends have brought to my attention randomly in the past, Joe Stump.
After looking further into Sprint.ly, I developed an impression of Joe Stump watching videos of him evangelizing his approach to startups & project management. His philosophy of eliminating friction within a team by promoting transparency is infectious. His philosophy of eliminating friction by promoting transparency is embodied in the sprint.ly product.
I come to the Sprint.ly product development team from a background of human-computer interaction and machine learning in academia. This academic experience is balanced by a decade-to-score of hardcore software engineering across large & small organizations, including as founder of two companies. My focus at Sprint.ly will include large measures of algorithmic modeling and, perhaps, data modeling [the distinction between the two is discussed at length here ].
My engineering approach to creating algorithmic or data models is fundamentally based on my experience in modeling language development. I modeled language development under National Institute of Health grants in the following capacities: (1) brainwave analyses at the Salk Institute in San Diego, (2) behavioral analyses at the Language Research Center of Children’s Hospital San Diego, & (3)algorithmic modeling at the Center for Research in Language at University of California, San Diego. As a result, I do not look at data as a general statistician; rather, I look at the world in terms of the algorithmic modeling of time series data. In particular, I pay attention to structural patterns in time that emerge from complex systems. What can organizational data tell us about the limits and boundaries and norms of how organizations function? How can this information be re-purposed to better serve these (& other) organizations? How can this be executed not just from a data harvesting perspective, but also a human-computer interaction perspective? These are the long-term challenges that I am excited about tackling.
As a team, we will first be re-structuring our text-based search to produce more relevant results & to include natural language capabilities in our search model. Look for exciting changes to come. The better we make the Sprint.ly product, the more our customers can achieve.
