Hi! I’m currently a Ph.D. candidate at Worcester Polytechnic Institute in our Data Science program, a part of the Computer Science department. Broadly, I am interested in sequence modeling and so my research often involves designing Recurrent Neural Networks to solve problems on a variety of sequential data. So far, I have enjoyed developing and applying my representation learning methods to clinical sequences (e.g., vital signs, lab results, and free-hand notes).

I am advised by Prof. Elke Rundensteiner and Prof. Xiangnan Kong.

Specific Research Interests

I am 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 early classification, clinical note classification, meta word embeddings, and representation learning for irregularly-sampled time series. Additionally, I have some conditional computing in RNNs work in submission with an extension on the way! I have also had the pleasure of advising many NSF-funded REU students over the summers on some research involving missing values in clinical time series and sequential diagnosis prediction using RNNs.

Please feel free to contact me with questions regarding my research or our program at WPI at twhartvigsen ‘at’ wpi ‘dot’ edu. I would be happy to go into much more detail on my research one-on-one.


  • [BigData] Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner. Patient-Level Classification of Clinical Note Sequences Guided by Attributed Hierarchical Attention. IEEE BigData, 2019.
  • [BigData] Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner. Learning Temporal Relevance in Longitudinal Medical Notes. IEEE BigData, 2019.
  • [KDD] Thomas Hartvigsen, Cansu Sen, Xiangnan Kong, Elke Rundensteiner. Adaptive-Halting Policy Network for Early Classification. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2019. [ACM Paper Link][pdf][code].
  • [BHI] Jidapa Thadajarassiri, Cansu Sen, Thomas Hartvigsen, Xiangnan Kong, Elke Rundensteiner. Comparing General and Locally-Learned Word Embeddings for Clinical Text Mining. IEEE International Conference on Biomedical and Health Informatics (BHI), 2019. [pdf].
  • [CCIS] Thomas Hartvigsen, Cansu Sen, Elke Rundensteiner. Detecting MRSA Infections by Fusing Structured and Unstructured Electronic Health Record Data. Communications in Computer and Information Science. [pdf]
  • [HEALTHINF] Thomas Hartvigsen, Cansu Sen, Sarah Brownell, Erin Teeple, Xiangnan Kong, Elke Rundensteiner. Early Prediction of MRSA Infections using Electronic Health Records. International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 156-167, ISBN: 978-989-758-281-3. Shortlisted for Best Student Paper. [pdf]
  • [ECML/PKDD] Cansu Sen, Thomas Hartvigsen, Kajal Claypool, Elke Rundensteiner. CREST - Risk Prediction for Clostridium Difficile Infection Using Multimodal Data Mining. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD) 2017. [pdf]

Useful links


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