Hi! I’m a Ph.D. candidate in Data Science at Worcester Polytechnic Institute and a member of the DAISY Lab advised by Elke Rundensteiner and Xiangnan Kong. I am developing deep learning and reinforcement learning methods for time series data mining/machine learning and am interested in conserving wildlife and increasing the accessibility of high-quality healthcare. I have published several papers at top data mining and machine learning conferences and have also been fortunate enough to collaborate with some wonderful people on a wide variety of topics.

My CV can be found here.

Please feel free to contact me with questions regarding my research or our program at WPI. I am always looking to chat about research!

News

Technical Interests

  • Time series modeling and classification
  • Recurrent Neural Networks
  • Early Classification
  • Healthcare/sustainability applications

Publications

  • Recurrent Halting Chain for Early Multi-label Classification

    T. Hartvigsen, C. Sen, X. Kong, E. Rundensteiner.
    KDD, 2020. Research track, acceptance rate: 16.9%.
  • Human Attention Maps for Text Classification: Do Humans and Neural Networks Focus on the Same Words?

    C. Sen, T. Hartvigsen, B. Yin, X. Kong, E. Rundensteiner.
    ACL, 2020. Long paper, acceptance rate: 17.6%.
  • Learning to Selectively Update State Neurons in Recurrent Networks

    T. Hartvigsen, C. Sen, X. Kong, E. Rundensteiner.
    CIKM, 2020. Research track, acceptance rate: 18%.
    [pdf] [link] [code]
  • Learning Similarity-Preserving Meta-Embedding for Text Mining

    J. Thadajarassiri, C. Sen, T. Hartvigsen, X. Kong, E. Rundensteiner.
    IEEE BigData, 2020. Long paper, acceptance rate: 15.5%.
    [pdf]
  • Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection

    E. Teeple, T. Hartvigsen, C. Sen, K. Claypool, E. Rundensteiner.
    HEALTHINF, 2020. Best Poster.
    [link]
  • Patient-Level Classification of Clinical Note Sequences Guided by Attributed Hierarchical Attention

    C. Sen, T. Hartvigsen, X. Kong, E. Rundensteiner.
    IEEE BigData, 2019.
    [link]
  • Learning Temporal Relevance in Longitudinal Medical Notes

    C. Sen, T. Hartvigsen, X. Kong, E. Rundensteiner.
    IEEE BigData, 2019.
    [link]
  • Adaptive-Halting Policy Network for Early Classification

    T. Hartvigsen, C. Sen, X. Kong, E. Rundensteiner.
    KDD, 2019. Research track, acceptance rate: 14.2%.
  • Early Diagnosis Prediction with Recurrent Neural Networks

    D. Johnston, L. Klindziuk, L. Nazarov, T. Hartvigsen, E. Rundensteiner.
    IEEE URTC, 2019.
  • Comparing General and Locally-Learned Word Embeddings for Clinical Text Mining

    J. Thadajarassiri, C. Sen, T. Hartvigsen, X. Kong, E. Rundensteiner.
    IEEE BHI, 2019.
    [pdf]
  • Handling Missing Values in Multivariate Time Series Classification

    J. Friend, A. Hauck, S. Kurada, C. Sen, T. Hartvigsen, E. Rundensteiner.
    IEEE URTC, 2018.
  • Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record Data

    T. Hartvigsen, C. Sen, E. Rundensteiner.
    Communications in Computer and Information Science, Volume 1024, 2018.
    [link]
  • Early Prediction of MRSA Infections using Electronic Health Records

    T. Hartvigsen, C. Sen, S. Brownell, E. Teeple, X. Kong, E. Rundensteiner.
    HEALTHINF, 2018. Best student paper short list.
    [pdf]
  • CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining

    C. Sen, T. Hartvigsen, K. Claypool, E. Rundensteiner.
    ECML, 2017.
    [link]

Personal

In my spare time, I enjoy rock climbing, cycling, reading (fantasy, science fiction, science fact), and playing guitar.