PhD (Comp. Sc.), MSc, BMusic, BA (Queen's University, Canada)
Director, Document and Pattern Recognition Lab (dprl)
Department of Computer Science
Rochester Institute of Technology (NY, USA)
I am a Professor of Computer Science at RIT. My research interests include pattern recognition and machine learning, with applications in document recognition and information retrieval. I've worked on math-aware search engines and recognizing math notation (most recently with the MathSeer project), locating text in pictures, and audio-visual search in lecture videos. Please click on the links above for information about my teaching, research, publications (including .pdfs), software produced by or associated with the dprl, and resources for students.
I direct the Document and Pattern Recognition Lab (dprl) and am affiliated with the Artificial Intelligence Cluster in the Computer Science Department. I am on the Editorial Board of the International Journal on Document Analysis and Recognition (IJDAR), and am a member of the IEEE Computer Society, ACM, and International Association for Pattern Recognition (IAPR). I served as the Communications Officer for IAPR Technical Committee No. 11 ('Reading Systems') during 2017-2018, recently joined the IAPR Conferences and Meetings Committee and I Co-Chaired the International Conference on Frontiers in Handwriting Recognition (ICFHR 2018).
Some notes for students:
- I am currently looking for a new PhD student to begin in Fall 2020. If you would be interested in extracting and recognizing formulas for use in math-aware search engines, please send me an email along with your CV.
- Students at RIT interested in doing an Independent Study, Master's project or thesis with myself as advisor should consult the DPRL Project and Thesis Guidelines.
- I am not taking on any new MSc students at the moment, and don't have time to reply to inquries about this.
- I do not have time to respond to unsolicited emails asking for advice on how to prepare for/excel at work in Machine Learning or other areas of Computer Science.
News (dprl News)
ECIR 2020 The dprl had three papers on accelerated formula search, the new MathDeck search interface, and the ARQMath task accepted at ECIR! Congratulations to Wei Zhong, Behrooz Mansouri, Gavin Nishizawa, Abishai Dmello, Jennifer Liu, and Yancarlos Diaz on a job well done! [ publications page ]
ICDAR Papers. My PhD students Mahshad Mahdavi and Kwon-Young Choi have had their papers on formula recognition in typeset images and detection of accidentals in scans of printed musical scores accepted for publication at ICDAR. Both papers present novel recognition models employing CNNs. ICDAR 2019 will be held in Sydney, Australia this fall.
ECIR 2019 Best Applications Paper. My PhD student Wei Zhong received the Best Applications Paper award at ECIR 2019. His paper's title is Structural Similarity Search for Formulas using Leaf-Root Paths in Operator Subtrees.
MathSeer Pages. (March, 2019) Web pages for the MathSeer project are now online.
JCDL 2019 Paper. My PhD student Behrooz Mansouri had a full paper on log analysis for math searches in a general-purpose search engine paper accepted to JCDL 2019, which will was held at the University of Illinois Urbana-Champaign in June. Nominated for a Best Paper award.
ECIR 2019 Papers. My PhD student Wei Zhong and my former PhD student Kenny Davila have both had full papers on math formula search accepted for oral presentation at ECIR 2019 in Cologne, Germany. Wei's paper received the Best Applications Paper Award.
Best Paper Award at ICFHR 2018. My former PhD student Kenny Davila and I received the award for Best Paper at ICFHR (Aug 2018). The paper title is "Visual Search Engine for Handwritten and Typeset Math in Lecture Videos and LaTeX Notes."
Tangent-V ('visual' search). Kenny Davila's Tangent-V system for visual search in binary images in now available for download. An accompanying paper is being presented at ICFHR 2018 in Niagara Falls in early August. Kenny applied Tangent-V to searching for math in lecture notes, and in automatically generated keyframe summaries of whiteboard contents in math lecture videos.