Connor Gramazio

  • Senior Software Engineer, Amazon
  • Machine learning engineering focus
  • Computer Science PhD, data visualization


I am an SDE3 (i.e., Senior SDE) at Amazon, where I focus on machine learning and data-intensive software. I currently work within Amazon Pharmacy (PillPack). Previously I worked in Alexa AI, where I was the technical lead for developing scalable and reliable C++ and Python software for mission-critical natural language understanding models.

I am a strong advocate of inclusive and accessible human-entered computing. I view computer science as a field that extends beyond algorithms alone.

Before joining the software industry, I completed a Computer Science PhD as a National Science Foundation Graduate Research Fellow at Brown University. My dissertation focused on visualization design, and drew on a variety of research methods: my work involved not just software development, but also both quantitative and qualitative human subject evaluation. Although I am now an SDE, I still have a strong UXR perspective.

Contact Information

Please reach out over Twitter DM or LinkedIn.

Summary of experience

Industry experience

Amazon, Boston and Cambridge, MA

Google, Cambridge, MA. Software Engineer Intern. 2012.

Charles River Analytics, Cambridge, MA. Software Engineer Intern. 2011.

Education and Academic Experience

Brown University, Providence, RI. Graduate Researcher. 2012-2017.

Tufts University, Somerville, MA. Undergraduate Researcher & TA. 2008-2012.

Academic Research

While in academia, I focused on information visualization design techniques and theory. My research led to eleven publications, and also additional conference posters. For a publication list, please see my Google Scholar or dblp profiles.

Much of my dissertation focuses on color's role in effective visualization. My talk at OpenVisConf 2017, Empowering Effective Visualization (Color) Design, highlights:

Aside from research on color, I also spent time looking at how information retrieval techniques can help aid qualitative evaluation of visualization design, effective design for genome visualization, as well as other areas like software visualization and crowdsourcing.