A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.




CSI4107: Information Retrieval and the Internet

Basic principles of Information Retrieval. Indexing methods. Query processing. Linguistic aspects of Information Retrieval. Agents and artificial intelligence approaches to Information Retrieval. Relation of Information Retrieval to the World Wide Web. Search engines. Servers and clients. Browser and server side programming for Information Retrieval.

CSI5180: Topics in Artificial Intelligence

Semantic web technologies (RDF, RDFS, OWL). Ontology and knowledge base development. Data integration and normalization. Ontology matching. Semantic Web access through SPARQL queries. Semantic Web expansion from unstructured data (text), including Named Entity Recognition, Entity Linking and Relation Extraction from textual data. Question Answering over Linked Data. Data availability, redundancy, contextualization and trust.

COMP5900: Advanced Machine Learning

Machine learning (ML) is the scientific study of algorithms and statistical models that computers use in order to perform a specific task effectively without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. This course will cover advanced topics in machine learning such as deep learning, transfer learning, multiview learning, clustering and Interpretability of ML methods.


CS Study Group

A website for our COMP1406 W18 study group, using React, Node, and Firebase.

Deep Neural Networks for Baseball

Following along with Andrew Trask’s “Grokking Deep Learning” by modelling baseball statistics using various architectures of neural networks built from scratch.

Menu Bot

Google Actions / Dialogflow applet for fetching Carleton’s cafeteria menu.


Extension of search engine built within Information Retrieval course featuring full-text search, spell check, document clustering, topic classification. Live site


Towards a Computational Approach to Conceptual Metaphor

Exploring a computational model of Lakoff’s conceptual metaphor theory.

Recommended citation: Lynch, B., Danovitch, J., & Davies, J. (2018). Towards a Computational Approach to Conceptual Metaphor. Poster session at CogSci 2019, Montreal, CA.



Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.