Richard Zanibbi Headshot

Richard Zanibbi

Professor, Computer Science

Department of Computer Science
Golisano College of Computing and Information Sciences
rxzvcs@rit.edu
585-475-5023
Office Hours
Tues, Thurs 3:30-5pm
Office Location
GOL-3551
Office Mailing Address
Dept. Computer Science Rochester Institute of Technology 102 Lomb Memorial Drive, Rochester, NY 14623-5608

Richard Zanibbi

Professor, Computer Science

Department of Computer Science
Golisano College of Computing and Information Sciences

Education

BMusic; BA (minor) in Computer Science; MS in Computer Science; Ph.D. in Computer Science, Queen's University (Canada)

Bio

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. We expect the book to be published early in 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).

rxzvcs@rit.edu
585-475-5023
Personal Links
Personal Website
ORCID
Publication Database
Curriculum Vitae
Google Scholar
Areas of Expertise
Artificial Intelligence
Document Recognition
Human-Computer Interaction
Information Retrieval

Currently Teaching

CSCI-335
Machine Learning
3 Credits
An introduction to both foundational and modern machine learning theories and algorithms, and their application in classification and regression. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), Bayesian decision theory, parametric and non-parameteric classification models (e.g., SVMs and Nearest Neighbor models) and neural network models (e.g. Convolutional, Recurrent, and Deep Neural Networks). Programming assignments are required.