Contact Mathematics

Chair: Kris Green
Phone: (585) 385-7367

Department Page

Math Internships & Activities

Internship Opportunities

Qualified senior students can partake in internships at local-area companies such as Xerox, Kodak, and Paychex. There are also often opportunities for part-time jobs, of which we are notified from time to time. These opportunities provide significant work experience that can lead to permanent employment. Qualifications and guidelines for math internships are available from Professor Liz Leboffe at

Department Activities

Math Awareness Month

Each April, the College celebrates Math Awareness Month with talks by faculty and special guests.

Mathematics Club

The Math Club is a student group for math majors or anyone interested in the pursuit of mathematics. For more information, please contact Dr. Erica Johnson at or Dr. Mark McKinzie at

Math Tutors

There are many jobs as tutors available in the Math Center for those who are qualified. Qualified students have the opportunity to be lab assistants to the Computer Lab Coordinator and also to be instructional lab assistants.

Upcoming Math Events

Check out our News & Events page!

Recent Math Events

"An Intro to JPEGs and Computer Vision" with Candice Rockell Gerstner ’06

Fri., September 30, 2016 at 2:30 PM
Join alumna Dr. Candice Rockell Gersnter ’06 as she discusses "An Intro to JPEGs and Computer Vision." Room:  Wegmans School of Nursing, room 101 Reception immediately following with fellow alumni and math department faculty.  Gersnter, a Spencerport native, earned a degree in mathematics at Fisher, then received a full scholarship to Old Dominion University to pursue a Ph.D. in computational and applied mathematics. She has done post-doctoral work at NASA and the National Security Agency. Abstract:  Digital images are numerical representations of images.  The most common format for storing a digital image is a JPEG, which is a lossy compression method.  In this talk I will present an introduction to JPEGs, including the mathematics behind how they are stored.  I will also show that one can use this knowledge to start to answer the question of how to take the information stored in the image and use concepts from mathematics, statistics, neuroscience, and computer science to enable a computer to "see".  This concept is referred to as computer vision.  Several methods that are currently the state-of-the-art will be outlined.  No knowledge of images is required to understand this talk.

Event Details


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