Hi! I’m a Ph.D. candidate at Worcester Polytechnic Institute advised by Elke Rundensteiner and Xiangnan Kong in the Data Science program, a part of the Computer Science department. I am looking to combine reinforcement learning with sequence modeling to train computers to perceive the world like humans do and solve problems in healthcare and sustainability.

My CV can be found here.



  • Time series modelling
  • Recurrent Neural Networks
  • Early Classification
  • Healthcare applications

I am generally interested in sequence modeling, or building vector representations that capture relevant temporal dynamics in sequential data such as time series or text. So far, I have studied and published on early classification, clinical note classification, attention mechanisms for RNNs, meta word embeddings, interpretable machine learning, and irregularly-sampled time series. Much of my work involves Reinforcement Learning. On my Publications page I describe some of my representative research in more detail. I am also interested in applications to problems of human health and sustainability (particularly conservation biology).

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


  • Recurrent Halting Chain for Early Multi-label Classification

    T. Hartvigsen, C. Sen, X. Kong, E. Rundensteiner.
    KDD, 2020. Research track, acceptance rate: 16.9%.
    [pdf] [code]
  • 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.
    [pdf] [link] [data]
  • 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.
  • Patient-Level Classification of Clinical Note Sequences Guided by Attributed Hierarchical Attention

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

    C. Sen, T. Hartvigsen, X. Kong, E. Rundensteiner.
    IEEE BigData, 2019.
  • 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.
  • 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.
  • 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.
  • CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining

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


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