Select Work Experience


Software Developer, Alexa Machine Learning. Cambridge, MA. 2018 – Now.

  • Assorted work spanning engineering and science divisions of Alexa Machine Learning, primarily focused on natural langauge understanding

Audible, an Amazon company

Software Developer, Audible on Alexa. Cambridge, MA. 2017 – 2018.

  • Led development of time-sensitive and critical functionality across a diverse breadth of the team’s responsibilities (new device integration, front-end development, etc.)
  • Created front-end development best practices for Audible’s Alexa presence, which lowered dev cost of many tasks from days to hours while also improving preemptive bug identification
  • Wrote technical documentation to support engineering decisions and onboarding

Brown University

NSF Graduate Research Fellow. Providence, RI. 2012 – 2017.

  • Published three papers in top-tier data visualization venues (author on 9 papers total)
  • Created Colorgorical, a color palette design tool better than Microsoft and Tableau, featured on HackerNews and used by over 17,000 unique users
  • Created new machine learning approach to use mouse interactions as a proxy for classifying interactive visualization use by domain experts (cancer researchers)


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

  • Conceptualized and implemented a new visualization-based viral content exploration feature for Google+, which drew on UX (e.g., wireframing) and full-stack development skills (e.g., balancing functionality over the client vs. server)
  • Collaborated with developers and designers to overcome front-end optimization hurdles and data center load constraints
  • Programmed production-ready and test-driven C++ based BigTable jobs, Java-based asynchronous middleware, and Javascript-based front-end visualizations
  • Regularly performed code reviews and applied Google’s clean code policies

Select Projects

An automated color palette design tool that can create palettes that are more effective than the defaults provided by Microsoft and Tableau.
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Behavior classification
A new approach to task requirement analysis that uses cursor interaction summary statistics in accessible classifiers (e.g., random forests) to understand domain expert behavior usability needs.
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A new tool that facilitates accessible color palette design (e.g., accomodating color vision deficiencies) by applying bleeding edge color science research.
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A D3 implementation of CIECAM02 and CIECAM02-UCS, which are two of the most perceptually accurate color spaces, but are also typically not supported in most major visualization toolkits.
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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
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Teaching Experience


Languages: Javascript, Python, Java, R (proficient), C, C++ (historical proficiency)

Libraries/Tech: D3, NumPy, SciKit-Learn, node, Tornado (proficient), OpenGL (historical proficiency)

Design: Adobe Photoshop, Adobe Illustrator, paper prototyping, wireframing, responsive design, accessibility

Machine Learning and Statistics: linear regression, classification, clustering, experimental stats (e.g., Χ2, ANOVA)

Select Awards and Honors

OpenVisConf Speaker, 2017; NSF Graduate Research Fellowship, 2012–17; Andries van Dam Graduate Fellowship, 2012–13; Brown University Dissertation Fellowship 2016–17; Runner Up Computing Research Association Outstanding Undergrad Research Award, 2011