We are Tilburg University

We are Tilburg University

Bio

I’m a Lecturer at Tilburg University, as well as a joint PhD candidate doing research for both the Department of Cognitive Science and Artificial Intelligence at Tilburg and CLiPS at the University of Antwerp under the supervision of Eric Postma, Grzegorz Chrupała, and Walter Daelemans. I previously taught Text Mining and Data Mining in context of our Data Science master, and before that worked for the AMiCA project. My earlier research involved scientific development of tools for text forensics and online security such as detection of cyberbullying and child grooming, and author profiling. Currently I’m on two years research time to finish my dissertation, mainly focussing on applying machine learning to protect users from exposing latent information through their language use.

Expertise

I'm interested in the effect of intelligent systems on our lives. Systems that uncover our personal information, monitor and change our behavior, subtly restrict our exposure to information, and treat us unfairly. My current research focuses on the dual-use of computational stylometry; a field that aims to infer information from writing for good, proving harmfully invasive at the same time. My dissertation works toward developing open-source tools to better understand, and defend against such techniques invading one's privacy.

Courses

Top publications

  1. Towards Replication in Computational Cognitive Modeling - A Machine L…

    Emmery, C., Kádár, Á., Wiltshire, T. J., & Hendrickson, A. T. (2019). Towards Replication in Computational Cognitive Modeling: A Machine Learning Perspective. Computational Brain & Behavior. ,
  2. Style Obfuscation by Invariance

    Emmery, C., Manjavacas, E., & Chrupala, G. (2018). Style Obfuscation by Invariance. In COLING 2018 Association for Computational Linguistics. https://arxiv.org/abs/1805.07143
  3. Simple Queries as Distant Labels for Detecting Gender on Twitter

    Emmery, C., Chrupala, G., & Daelemans, W. (2017). Simple Queries as Distant Labels for Detecting Gender on Twitter. In Proceedings of the 3rd Workshop on Noisy User-generated Text (pp. 50-55). Association for Computational Linguistics. http://noisy-text.github.io/2017/pdf/WNUT07.pdf
  4. Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource

    Tulkens, S., Emmery, C., & Daelemans, W. (2016). Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource. In Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource Association for Computational Linguistics. https://arxiv.org/abs/1607.00225

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