T. Ryan Hoens

About Me

After graduating from the Rochester Institute of Technology in 2008, I became a graduate student in the department of Computer Science and Engineering at the University of Notre Dame, advised by Dr. Nitesh V. Chawla. I am a member of the Data, Inference, Analysis, and Learning (DIAL) Laboratory, and also affiliated with the Interdisciplinary Center for Network Science and Applications (iCeNSA). My research is supported by a Notebaert Premier Graduate Fellowship.

Research Interests

My main research interests are in the fields of data mining and machine learning, specifically classification in (highly) imbalanced datasets. My published work ranges from traditional binary class imbalance, to multi-class imbalance, and finally to dealing with imbalance in nonstationary data streams. Moving forward I am interested in applying existing (and developing new) methods for large, real world datasets.

In addition to Machine Learning, I am also interested in privacy and security. My published work includes a novel recommendation system which preserves user privacy.

Publications

• T. Ryan Hoens, Nitesh V. Chawla, and Robi Polikar. Heuristic updatable weighted random subspaces for nonstationary environments. In 2011 IEEE International Conference on Data Mining. IEEE, 2011. PDF
• David Cieslak, T. Ryan Hoens, Nitesh V. Chawla, and W. Philip Kegelmeyer. Hellinger distance decision trees are robust and skew-insensitive. Data Mining and Knowledge Discovery, pages 123, 2011. PDF
• Troy Raeder, T. Ryan Hoens, and Nitesh V. Chawla. Consequences of variability in classier performance estimates. In 2010 IEEE International Conference on Data Mining, pages 421430. IEEE, 2010. PDF
• T. Ryan Hoens, Marina Blanton, and Nitesh V. Chawla. Reliable medical recommendation systems with patient privacy. In Proceedings of the 1st ACM International Health Informatics Symposium, pages 173182. ACM, 2010. PDF
• T. Ryan Hoens, Marina Blanton, and Nitesh V. Chawla. A private and reliable recommendation system for social networks. In IEEE International Conference on Social Computing/IEEE International Conference on Privacy, Security, Risk and Trust, pages 816825. IEEE, 2010. PDF
• T. Ryan Hoens and Nitesh V. Chawla. Generating diverse ensembles to counter the problem of class imbalance. Advances in Knowledge Discovery and Data Mining, pages 488499, 2010. PDF Updated PDF

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