I'm a 6th year PhD student, working under Dr. Eugene Charniak in the Brown Laboratory for Linguistic Information Processing (BLLIP). My interests are in statistical machine learning, particularly within natural language processing. 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). My goal is to graduate in May 2018.
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
- online algorithms for content characterization
- anomaly detection
- adaptive web personalization
- speech recognition
- error-correcting codes
- social network analysis
- 2d pattern recognition
- animats-based learning
You can download my resume here.
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
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 include:
- Special Topics in Computational Linguistics (Brown)
- Computational Molecular Biology (Brown)
- 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)