Richard Zanibbi Headshot

Richard Zanibbi

Professor

Department of Computer Science
Golisano College of Computing and Information Sciences
rxzvcs@rit.edu
585-475-5023
Office Hours
By appointment
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

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.  I direct the Document and Pattern Recognition Lab (dprl) where we currently work on the MathSeer and MMLI (chemical informatics) projects. My research interests include pattern recognition and machine learning, with applications in document recognition and information retrieval.

I serve on Editorial Boards for the International Journal on Document Analysis and Recognition (IJDAR) and Pattern Recognition. I was also general chair for the International Conference on Frontiers in Handwriting Recognition (ICFHR 2018), a program co-chair for the IAPR Workshop on Graphics Recognition (GREC 2021), and will be a program chair for the upcoming International Conference on Document Analysis and Recognition being held in San Jose (ICDAR 2023).

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.
CSCI-536
Information Retrieval
3 Credits
An introduction to the theories and techniques used to construct search engines. Topics include search interfaces, traditional retrieval models (e.g., TF-IDF, BM25), modern retrieval techniques (e.g., neural reranking and retrieval), search engine evaluation, and search applications (e.g., conversational IR, enterprise search). Students will also review current IR research topics, and complete a group project in which they will design and execute experiments for search engine components.
CSCI-539
Seminar in Artificial Intelligence
3 Credits
This course examines current topics in Artificial Intelligence. This is intended to allow faculty to pilot potential new undergraduate offerings. Specific course details (such as prerequisites, course seminar, format, learning outcomes, assessment methods, and resource needs) will be determined by the faculty member(s) who propose a specific seminar course in this area. Specific course instances will be identified as belonging to the Artificial Intelligence cluster, the Computer Graphics and Visualization cluster, the Security cluster, or some combination of these three clusters.
CSCI-636
Information Retrieval
3 Credits
An introduction to the theories and techniques used to construct search engines. Topics include search interfaces, traditional retrieval models (e.g., TF-IDF, BM25), modern retrieval techniques (e.g., neural reranking and retrieval), search engine evaluation, and search applications (e.g., conversational IR, enterprise search). Students will also review current IR research, provide written summaries of current research papers, and complete a group project in which they will design and execute experiments for search engine components.
CSCI-737
Pattern Recognition
3 Credits
An introduction to pattern classification and structural pattern recognition. Topics include Bayesian decision theory, evaluation, clustering, feature selection, classification methods (including linear classifiers, nearest-neighbor rules, support vector machines, and neural networks), classifier combination, and recognizing structures (e.g. using HMMs and SCFGs). Students will present current research papers and complete programming projects such as optical character recognizers. Offered every other year.
IMGS-699
Imaging Science Graduate Co-op
0 Credits
This course is a cooperative education experience for graduate imaging science students.
IMGS-890
Research & Thesis
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

In the News

  • June 23, 2020

    screenshot of program that searches math formulas.

    RIT researchers create easy-to-use math-aware search interface

    Researchers at RIT have developed MathDeck, an online search interface that allows anyone to easily create, edit and lookup sophisticated math formulas on the computer. Created by an interdisciplinary team of more than a dozen faculty and students, MathDeck aims to make math notation interactive and easily shareable, and it's is free and open to the public.

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