About

I am a senior software developer with expertise spanning machine learning engineering, distributed services, and front-end development. While at Amazon I've had the opportunity to:

• Trailblaze design patterns for securely training and hosting machine learning models for PHI-handling products at Amazon Pharmacy
• Lead development for scalable and reliable C++ and Python software that powered the inner-most loop of model training and inference for Alexa's natural language understanding models
• Overhaul Audible's presence on the Alexa mobile app, and bar-raise CI/CD best-practices for front-end development

I am most at home in organizations that challenge me to learn new skills, and that also take meaningful action to improve diversity, equity, and inclusion.

Prior to software development, I completed a Computer Science PhD as a National Science Foundation Graduate Research Fellow at Brown University. My dissertation focused on visualization design, and I leveraged a wide array of research methods (e.g., systems work, algorithm design, and human subject evaluation).

Contact Information

I can be found on LinkedIn, and am in the process of migrating from Twitter to Mastadon.

Do not contact me if you are a recruiter in anything related to blockchain, "web3", or cryptocurrency. Web3 is going just great.

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 Semantic Scholar, 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:

• Colorgorical: an automated tool to make categorical color palettes, taking both legibility and aesthetic preference into account. (use) (paper) (source)
• Chromaticity: a manual color palette generation tool based on a color picker that spans a variety of perceptual color spaces, and provides warnings about ineffective color combinations. (use) (source)
• d3-cam02: a D3.js module that defines CIECAM02 and CIECAM02-UCS perceptual color spaces. (use) (source)
• d3-jnd: a D3.js module that allows designers to quickly check whether two colors can be easily differentiated, and considers how color discriminability can shift with changes in size. (use) (source)

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.