Home

I am a Professor of Computer Science at RIT, where I direct the Document and Pattern Recognition Lab. I hold a PhD and Master's in Computer Science, a BA with a minor in Computer Science, and a Bachelor of Music degree, all from Queen's University, Canada.

My research interests include information retrieval, document recognition, pattern recognition, and machine learning. Recently I co-authored a book manuscript on mathematical information retrieval for Foundations and Trends in Information Retrieval that will appear in January 2025.

I was a Program Co-Chair for ICDAR 2023, and I previously chaired the ICFHR 2018, DRR 2012, and DRR 2013 conferences. I also serve on program committees for information retrieval conferences (e.g., SIGIR, and the new SIGIR-AP conference).

At RIT I teach courses on Information Retrieval (grad/undergrad) and Machine Learning (undergrad). I am also the head of the AI Cluster within the Computer Science department.

Please click on the links above for more information regarding my research, teaching, software and data from the dprl, and resources for students. Some recent news is included below.

News

(Dec 2024) The final draft of the Math IR book is finished, and has been submitted to the publisher. A preprint of the final draft should be available from arXiv by Dec. 6th.
(May 2024) I was happy to collaborate on a new survey with Masaki Nakagawa's group and Harold Mouchère: A Survey of Handwritten Mathematical Expression Recognition: The Rise of Encoder-Decoder and GNN Models. The paper is available for free from the Pattern Recognition journal here until June 21st. A preprint of the final paper is also available here.
(Jan 2024) Three students from my information retrieval class, Ben Giacalone, Greg Paiement, and Quinn Tucker have published an interesting paper on the role of the [MASK] tokens in the ColBERT retrieval model, which will be presented at the European Conference on Information Retrieval (ECIR) this March in Glasgow, Scotland.
(Nov, 2023) I have posted a small python debugging library on GitLab that was created for my classes and the dprl lab. The library is organized around pretty-printed debug checks/tests with descriptive messages. I've called it the Message-Oriented Debugging Library for Python (msg_debug). It avoids the need to repeatedly add/remove print, input, and assert statements to check values and types, and provides functions to record and report execution times when our program requirements keep changing, and bugs abound.