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.

My research lies at the intersection of deep reinforcement learning and data mining, with a focus on time series. I aim to advance time series data mining and machine learning to increase the quality and accessibility of healthcare to ultimately empower disadvantaged communities.

My work has appeared at several top data mining and machine learning conferences and I have been fortunate enough to collaborate with some wonderful folks to push the envelope on machine learning for time series and text. I also spent a year collaborating with the data science department in the UMass Medical School to assist doctors in expediting the clinical trial writing process using machine learning.

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!

Research Interests

  • Time series modeling and classification
  • Sequential Learning in Time-Sensitive Domains (see [KDD'19][KDD'20])
  • Recurrent Neural Networks (see [AAAI'21][CIKM'20])
  • Explainable Deep Learning (see [ACL'20])
  • Healthcare/sustainability applications

News

Publications

    2021

  • Semi-Supervised Knowledge Amalgamation for Sequence Classification

    J. Thadajarassiri, T. Hartvigsen, X. Kong, E. Rundensteiner.
    AAAI, 2021. Acceptance rate: 21%.
    [pdf] [code]
  • 2020

  • 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] [code]
  • 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]
  • 2019

  • Adaptive-Halting Policy Network for Early Classification

    T. Hartvigsen, C. Sen, X. Kong, E. Rundensteiner.
    KDD, 2019. Research track, acceptance rate: 14.2%.
  • 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]
  • 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]
  • -2018

  • 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.