Tom Hartvigsen


Data Science Ph.D. student at Worcester Polytechnic Institute.

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I’m a Ph.D student in Data Science at Worcester Polytechnic Institute focusing on Machine Learning, advised by Prof. Elke Rundensteiner and collaborating with Prof. Xiangnan Kong and Cansu Sen in the Data Science Research Group.


I investigate sequential learning algorithms, particularly recurrent neural networks, with a focus on handling a variety of sequential data sources. I believe that in the future, a major player in integrating Artificial Intelligence into daily life will be a variety of small sensors strapped to every-day items. I am working to develop algorithms that handle such a paradigm effectively, aiding potential for global impact of Artificial Intelligence. I am also deeply invested in environmental issues solvable through AI.

Please feel free to contact me with questions regarding my research or Data Science at WPI at


Current Research

I am currently interested in learning patterns in time series as early as possible. In many domains, the evaluation of a predictive model should not only be accuracy, but a trade-off between accuracy and earliness. In general I am interested in modeling very long sequences and sensor fusion through Recurrent Neural Networks. While so far my work has been evaluated in the clinical domain, I am far more interested in the theory behind these techniques, and these problems are present in many domains.