Jeremy Brown Headshot

Jeremy Brown

Lecturer

Department of Computer Science
Golisano College of Computing and Information Sciences

585-475-4523
Office Location
Office Mailing Address
Computer Science Depr 102 Lomb Memorial Dr Rochester, NY 14623

Jeremy Brown

Lecturer

Department of Computer Science
Golisano College of Computing and Information Sciences

Education

BS in Computer Science, RIT; MS in Information Technology Management, Florida Institute of Technology

585-475-4523

Areas of Expertise

Currently Teaching

CSCI-251
3 Credits
This course is an introduction to the organization and programming of systems comprising multiple computers. Topics include the organization of multi-core computers, parallel computer clusters, computing grids, client-server systems, and peer-to-peer systems; computer networks and network protocols; network security; multi-threaded programming; and network programming. Programming projects will be required.
CSCI-320
3 Credits
This course provides a broad introduction to the principles and practice of modern data management, with an emphasis on the relational database model. Topics in relational database systems include data modeling; the relational model; relational algebra; Structured Query Language (SQL); and data quality, transactions, integrity and security. Students will also learn approaches to building relational database application programs. Additional topics include object-oriented and object-relational databases; semi-structured databases (such as XML); and information retrieval. A database project is required.
CSCI-351
3 Credits
This course is an in-depth study of data communications and networks. The course covers design of, and algorithms and protocols used in, the physical, data link, network, transport, and application layers in the Internet; methods for modeling and analyzing networks, including graphs, graph algorithms, and discrete event simulation; and an introduction to network science. Programming projects will be 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-472
3 Credits
Students who have a background in Computer Science theories, algorithms, and data structures will be provided a look at the history of Computer Science from historical and current perspectives. Topics include an early history of Computer Science, a study of the people who shaped Computer Science, and a discussion of major milestones in Computer Science. Additionally, students will study current issues in Computer Science, including legal, ethical, diversity, equity, inclusion, accessibility and privacy issues, and how past issues affect modern designs and decision making by computer scientists. Students will be required to work in teams on several assignments. Papers, presentations, and oral presentations are required.
CSCI-541
3 Credits
The goal of this course is to introduce the students to a programming paradigm and an appropriate programming language chosen from those that are currently important or that show high promise of becoming important. A significant portion of the learning curve occurs through programming assignments with exemplary solutions discussed later in class. The instructor will post specifics prior to registration. With the approval of the program coordinator, the course can be taken for credit more than once, provided each instance deals with a different paradigm and language.
CSCI-620
3 Credits
This course provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. First, practical techniques used in exploratory data analysis and mining are introduced; topics include data preparation, visualization, statistics for understanding data, and grouping and prediction techniques. Second, approaches used to store, retrieve, and manage data in the real world are presented; topics include traditional database systems, query languages, and data integrity and quality. Case studies will examine issues in data capture, organization, storage, retrieval, visualization, and analysis in diverse settings such as urban crime, drug research, census data, social networking, and space exploration. Big data exploration and management projects, a term paper and a presentation are required. Sufficient background in database systems and statistics is recommended.

In the News

  • October 11, 2022

    students sitting at a table and writing on a dry-erase board.

    RIT faculty prepare to teach large classes in SHED using scaled-up classroom

    A room in Slaughter Hall seats 150 students and is meant to simulate the learning spaces in the Student Hall for Exploration and Development (SHED) that will hold classes next fall. The Slaughter classroom, dubbed the “betaSHED,” combines three rooms to give professors and students a preview of the large-scale learning environment.