Thomas Kinsman Headshot

Thomas Kinsman

Lecturer
Department of Computer Science
Golisano College of Computing and Information Sciences

585-475-5188
Office Location

Thomas Kinsman

Lecturer
Department of Computer Science
Golisano College of Computing and Information Sciences

Education

BS in Electrical Engineering, University of Delaware; MS in Electrical and Computer Engineering, Carnegie Mellon; Ph.D. in Imaging Science, RIT

585-475-5188

Areas of Expertise
Data science
Machine learning
Computer vision
Computer graphics
Feature selection
Performance (speed) optimization
Rapid prototyping
Embedded processing

Currently Teaching

CSCI-431
3 Credits
An introduction to the underlying concepts of computer vision. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming homework assignments that implement the concepts discussed in class are an integral part of the course.
CSCI-720
3 Credits
This course provides a graduate-level introduction to the concepts and techniques used in data mining. Topics include the knowledge discovery process; prototype development and building data mining models; current issues and application domains for data mining; and legal and ethical issues involved in collecting and mining data. Both algorithmic and application issues are emphasized to permit students to gain the knowledge needed to conduct research in data mining and apply data mining techniques in practical applications. Data mining projects, a term paper, and presentations are required.
CSCI-420
3 Credits
This course provides an introduction to the major concepts and techniques used in data mining of large databases. Topics include the knowledge discovery process; data exploration and cleaning; data mining algorithms; and ethical issues underlying data preparation and mining. Data mining projects, presentations, and a term paper are required.
CSCI-731
3 Credits
This course examines advanced topics in computer vision including motion analysis, video processing and model based object recognition. The topics will be studied with reference to specific applications, for example video interpretation, robot control, road traffic monitoring, and industrial inspection. A research paper, an advanced programming project, and a presentation will be required.
CSCI-529
3 Credits
This course examines current topics in Data Management. 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 Data Management cluster, the Security cluster, or both clusters.
CSCI-631
3 Credits
An introduction to the underlying concepts of computer vision and image understanding. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming assignments are an integral part of the course. Note: students who complete CSCI-431 may not take CSCI-631 for credit.

Select Scholarship

Full Length Book
Kinsman, Thomas B. Semi-Supervised Pattern Recognition and Machine Learning for Eye-Tracking (Ph.D. Dissertation). Rochester, NY: Rochester Institute of Technology, 2015. Print.