Cindi Thompson

Cindi is the Head of Data Science at SVDS. She is a naturally collaborative problem-solver able to bridge technical and business concerns using strong communication and facilitation skills. With over fifeteen years experience of research and applications of machine learning and natural language processing across academia and industry, she brings a unique blend of academic and industry experience AI and R&D. She has also collaborated extensively to solve problems by bridging technical and business concerns using strong communication and facilitation skills.

Cindi holds a PhD and MA in Computer Sciences from UT-Austin, and a Bachelor of Science in Computer Science from NCSU. She has dozens of publications in both journals and refereed conferences and is the co-inventor on three patents.

Recent Posts

Analyzing Sentiment in Caltrain Tweets

Analyzing Sentiment in Caltrain Tweets

As a first step to using Twitter activity as one of the data sources for train prediction, we start with a simple question: How do Twitter users currently feel about Caltrain?

Chatbots in Banking

This post explains why chatbots are rising in popularity with banks, the opportunities and challenges presented, and how data science fits into the puzzle.

Open Source Toolkits for Speech Recognition

This article reviews the main options for free speech recognition toolkits that use traditional HMM and n-gram language models.

Past Events


  • Data Dialogues: Data in Practice


    The Data in Practice track focuses on modern techniques for efficient execution of your data strategy. Register now!


  • ODSC West 2016

    Santa Clara, CA

    Join us in Santa Clara as we talk about managing data science in the enterprise, data valuation, and best practices for data visualization.

  • TDWI San Diego 2016

    San Diego, CA

    CTO John Akred, VP of Data Science Jeffrey Yau, and Senior Data Scientist Cindi Thompson will teach a three-hour tutorial in which they will share our methods and observations from three years of effectively deploying data science in enterprise organizations. Attendees will learn how to be an effective member or manager of a data science team.