Describes Chris Tanner’s research interests in Machine Learning, Deep Learning, and Natural Language Processing (NLP). Brown University, Spotify, IBM Research, Johns Hopkins HLT COE, MIT Lincoln Laboratory, MITLL, Department of Defense, Google, Florida Tech, UCLA.


Brown University February 21

MIT Media Lab February 25

Harvard University March 5

Invitae March 11-12

University of Washington March 13-15

Bloomberg March 21-22

Carnegie Mellon March 31 - April 2

MIT April 3




I'm a 7th year PhD student, working under Dr. Eugene Charniak in the Brown Laboratory for Linguistic Information Processing (BLLIP). I’m graduating in May 2019 and am now on the job market.

My interests are in machine learning / deep learning, particularly natural language processing (NLP). My thesis focuses on cross-document co-reference resolution for both entities and events.  That is, given many text documents, how can we automatically determine which underlying things are referring to the same things (e.g., maybe a particular "He", "Obama", and "President" are referring to the same underlying entity, but other instances of "He" and "President" may be referring to a different person who is in charge of a company).

In general, I’m most passionate about discourse, semantics, and understanding, but I tend to get excited about a multitude of Machine Learning problems.


During my career within academia, industry, and the government, my work has concerned:

  • coreference resolution

  • link prediction

  • face recognition

  • named-entity disambiguation

  • topic modelling

  • machine translation

  • streaming algorithms for NLP

  • anomaly detection

  • adaptive web personalization

  • speech recognition via active learning

  • error-correcting codes

  • social network analysis

  • 2d pattern recognition

  • animats-based learning (swarm intelligence)


You can download my resume here.


Chris Tanner and Eugene Charniak. Symbiotic Coreference Resolution for Entities and Events. (In Submission).

Chris Tanner and Eugene Charniak. Toward Featureless Event Coreference Resolution via Conjoined Convolutional Neural Networks. (In Submission).

Chris Tanner and Eugene Charniak. A Hybrid Generative/Discriminative Approach to Citation Prediction. NAACL, 2015

Chris Tanner, Stephen Chen, Byron Wallace, and Eugene Charniak. Discriminative Approaches to Citation Evidence Linking and Discourse Prediction. TAC Workshop (NIST), 2014


Unpublished Papers

The following includes my Master's Thesis and write-ups for course projects long ago:

Chris Tanner. An Exploration of Animats-Based Evolution and Communication. May 2009. UCLA Master's Thesis.

Chris Tanner and Eric Wood. Meerkat Manor: An Approach to Simulated, Genetic Co-Evolution.December 2008.

Chris Tanner and Leslie Choong. Automated Story Conversion: Generating Children Stories from Adult Stories. June 2008. (3-week course project)

Chris Tanner and Leslie Choong. Utilizing Natural Language Processing Conceptual Dependencies to Infer Meaning of 'Dashed Hopes.' May 2008. (3-week course project)

Chris Tanner, Irina Litvin, Amruta Joshi. Social Networks: Finding Highly Similar Users and their Inherit Patterns. February 2008.

Chris Tanner, Chu-Cheng Hsieh, and Keenahn Jung. Understanding Pure Social Networks: Structure, Connectivity, and Patterns of Interests. November 2007.

Chris Tanner. Improving Web Personalization via User Interest Hierarchy and Scoring Techniques. December 2006.

Chris Tanner. Accelerating Artificial Neural Network Learning via Weight Predictions. April 2005.



Some interesting courses I've taken as a student include:

  • Special Topics in Computational Linguistics (Brown)

  • Computational Molecular Biology (Brown)

  • Human-Computer Interaction Seminar (Brown)

  • Computational Linguistics (Brown)

  • The Photo Book (Rhode Island School of Design)

  • Reasoning with Partial Beliefs (UCLA)

  • Approximation Algorithms (UCLA)

  • Statistical Learning (UCLA)

  • Language and Thought (UCLA)

  • Animats-Based Learning (UCLA)

  • Parallel Programming (UCLA)

  • Web Information Management/Data Mining (UCLA)

  • Machine Learning (Florida Tech)

  • Artificial Intelligence (Florida Tech)

  • Neural Networks (Florida Tech)

  • Chaos Math (Florida Tech)

  • Abstract Algebra (Florida Tech)

  • Introductory Analysis (Florida Tech)

  • Numerical Analysis (Florida Tech)