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Danielle Dean    

Technical Director of Machine Learning, iRobot

Danielle Dean, PhD is the Technical Director of Machine Learning at iRobot where she is helping lead the intelligence revolution for robots. She leads a team that leverages machine learning, reinforcement learning, and software engineering to build algorithms that will result in massive improvements in our robots.

Before iRobot, Dean was a Principal Data Scientist Lead at Microsoft Corp. in AzureCAT Engineering within the Cloud AI Platform division. There, she led an international team of data scientists and engineers to build predictive analytics and machine learning solutions with external companies utilizing Microsoft's Cloud AI Platform. Before working at Microsoft, she was a data scientist at Nokia, where she produced business value and insights from big data, through data mining & statistical modeling on data-driven projects that impacted a range of businesses, products and initiatives.

Dean completed her Ph.D. in quantitative psychology with a concentration in biostatistics at the University of North Carolina at Chapel Hill, where she studied the application of multi-level event history models to understand the timing and processes leading to events between dyads within social networks. She obtained her Masters in quantitative psychology, and graduated from the University of Massachusetts Amherst with two Bachelor's Degrees, in Psychology and BDIC in Organizational Behavior & Statistical Analysis with a minor in Mathematics/Statistics.

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